INVESTIGATIONHIGHLIGHTED ARTICLE

Sequencing and Assembly of the 22-Gb LoblollyPine Genome

Aleksey Zimin1 Kristian A Stevensdagger12 Marc W Crepeaudagger Ann Holtz-MorrisDagger Maxim KoriabineDagger

Guillaume Marccedilais Daniela Puiusect Michael Roberts Jill L Wegrzyn Pieter J de JongDagger

David B Neale Steven L Salzbergsect James A Yorkedaggerdagger and Charles H Langleydagger

Institute for Physical Sciences and Technology and daggerdaggerDepartments of Mathematics and Physics University of Maryland CollegePark Maryland 20742 daggerDepartment of Evolution and Ecology and Department of Plant Sciences University of California DavisCalifornia 95616 DaggerChildrenrsquos Hospital Oakland Research Institute Oakland California 94609 sectCenter for Computational Biology

McKusick-Nathans Institute of Genetic Medicine The Johns Hopkins University Baltimore Maryland 21205

ABSTRACT Conifers are the predominant gymnosperm The size and complexity of their genomes has presented formidable technicalchallenges for whole-genome shotgun sequencing and assembly We employed novel strategies that allowed us to determine theloblolly pine (Pinus taeda) reference genome sequence the largest genome assembled to date Most of the sequence data werederived from whole-genome shotgun sequencing of a single megagametophyte the haploid tissue of a single pine seed Although thatconstrained the quantity of available DNA the resulting haploid sequence data were well-suited for assembly The haploid sequence wasaugmented with multiple linking long-fragment mate pair libraries from the parental diploid DNA For the longest fragments we used novelfosmid DiTag libraries Sequences from the linking libraries that did not match the megagametophyte were identified and removedAssembly of the sequence data were aided by condensing the enormous number of paired-end reads into a much smaller set of longerldquosuper-readsrdquo rendering subsequent assembly with an overlap-based assembly algorithm computationally feasible To further improvethe contiguity and biological utility of the genome sequence additional scaffolding methods utilizing independent genome andtranscriptome assemblies were implemented The combination of these strategies resulted in a draft genome sequence of 2015billion bases with an N50 scaffold size of 669 kbp

GYMNOSPERMS diverged from the lineage leading toangiosperms 300 million years ago (Bierhorst 1971

Magalloacuten and Sanderson 2005) In comparison to their morerecently diversified sister taxa the flowering plants gymno-sperms possess dramatically larger genomes (Bowe et al 2000Peterson et al 2002 Morse et al 2009 Zonneveld 2012) Whilerapid progress has been made in characterizing the genomes ofangiosperms the same is not true for gymnosperms in part dueto their size and complexity Comparative studies indicate thatrepetitive sequences transposable elements and gene duplica-tion have proliferated in gymnosperms (Ahuja and Neale 2005Morse et al 2009 Kovach et al 2010 Mackay et al 2012)

The gymnosperms diversified early after the divergencefrom angiosperms and are represented today by strikinglydiverse ancient lineages (Magalloacuten and Sanderson 2005)Less than 100 MYA in the Cretaceous period the Pinaceaelineage diversified to become the dominant terrestrial plantin temperate and subarctic climatic zones Conifers are byfar the most abundant gymnosperm representing 80 ofthe Earthrsquos biomass (Neale and Kremer 2011)

The first target for genome sequencing from the genusPinus was chosen for its economic (agricultural) as well asbiological (phylogenetic and ecological) importance Pinustaeda (loblolly pine) is a classic representative of the largestgenus in the order Conifereae The genus Pinus consists of100 species (Mirov 1967) Loblolly pine is a native foresttree species in the southeastern United States that is har-vested for commercial lumber pulp and paper markets(Frederick et al 2008) Because of its economic value lob-lolly pine has been at the center of a number of long-termbreeding programs (Schutz 1997 McKeand et al 2003) aswell as more fundamental genetics investigations and resource

Copyright copy 2014 by the Genetics Society of Americadoi 101534genetics113159715Manuscript received November 13 2013 accepted for publication December 10 2013Available freely online through the author-supported open access optionData can be accessed at httpwwwpinegenomeorgpinerefseq and at NationalCenter for Biotechnology Information BioProject 1744501Joint first authors2Corresponding author Department of Evolution and Ecology University of CaliforniaDavis CA 95616 E-mail kastevensucdavisedu

Genetics Vol 196 875ndash890 March 2014 875

development (eg genetic maps) The genotype selected forsequencing designated ldquo20-1010rdquo is the property of theVirginia State Department of Forestry which released thisgermplasm into the public domain for use without restric-tion Standard flow cytometry methods applied to P taedaplaced the size of the genome at 216 Gb (OrsquoBrien et al1996) more than seven times larger than the human ge-nome It is the largest genome sequenced and assembled todate using any technology Achieving a successful outcomerequired leveraging unique aspects of conifer biology andemploying novel computational methodologies to reducethe assembly problem to a tractable scale

Among the many distinguishing characteristics of gym-nosperms the most important for our project is the haploidfemale gametophyte (or megagametophyte) in each seedThe genome of the megagametophytic cells is derived bymitosis from the same meiotic product (megaspore) as theegg nucleus in the archagonium The haploid megagameto-phyte serves as a nutrient source for the developing embryo

A single haploid megagametophyte offers a major reductionin sequence complexity although at the expense of constrain-ing the amount of DNA available Haploid sequence is mucheasier to assemble than outbred diploid sequence As describedbelow the majority of our raw sequence data came froma single pine seed but the source was insufficient to provideDNA for our long-range ldquolinkingrdquo libraries Paired reads fromlonger DNA fragments require considerably more DNA and forthese libraries we used diploid DNA obtained from needles ofthe maternal tree (see Materials and Methods) The large sizeof the P taeda genome necessitated many individual long-insert libraries to achieve sufficient physical coverage To linkthe assembly together over large distances we created twotypes of linking libraries (1) mate pair ldquojumpingrdquo libraries inwhich paired reads were 1ndash6 kbp apart and (2) fosmid endsor DiTags in which paired reads were from the ends of a 35- to40-kbp fosmid clone Fosmid clones are less expensive to gen-erate than BACs and are well known to have less cloning biasand a more narrowly controlled insert size (Kim et al 1992)

Our assembly strategy was built around the MaSuRCAgenome assembler (Zimin et al 2013) Several of its inno-vations were developed specifically to handle the demandsof this very large project The key idea in MaSuRCA is toreduce high-coverage paired-end reads to a much smallerand more concise set of ldquosuper-readsrdquo

Applied to our data the MaSuRCA assembler can beconceptually divided into four major phases The first phasecorrects errors in the Illumina reads using the QuORUMerror corrector (Marcais et al 2013) The second phasereduces the short and highly redundant Illumina paired-endreads to a concise set of super-reads To do this MaSuRCAutilizes an exhaustive list of distinct sequences of length kor k-mers derived from the paired-end reads as a summaryof the haploid genome A ldquosuper-readrdquo is constructed from apaired-end read when the ends can be extended uniquely (asjudged from the list of k-mers) to form a single uninterruptedsequence containing both reads For a deep-coverage data

set many read pairs will extend into the same super-readwhich results in significant compression of the data The as-sembly uses only maximal super-reads ie those super-readsthat are not properly contained in other super-reads Thethird phase of assembly uses an adapted overlap-basedassembler CABOG (Miller et al 2008) to assemble the super-reads together with filtered read pairs from the longer diploidlibraries The final phase further increases contiguity by usinga local assembly algorithm to fill gaps in the scaffolds

Following assembly by MaSuRCA we implemented addi-tional custom scaffolding methods described below usingboth an independent SOAPdenovo2 (Luo et al 2012) assem-bly and high-quality transcriptome data to further improvethe assemblyrsquos contiguity

Two other conifer genome sequences of the Norway spruceand white spruce have recently been reported (Birol et al2013 Nystedt et al 2013) Our initial draft assembly ofloblolly pine release 06 was released on June 11 2012(httploblollyucdavisedubipodftpGenome_DatagenomepinerefseqPitav06) It contained 185 Gbp of sequence withan N50 contig size of 800 bp

The much-improved genome sequence of Loblolly pine(Neale et al 2014) whose sequencing and assembly are de-scribed here contains 201 Gbp with an N50 contig size of 82kbp and an N50 scaffold size of 669 kbp By many measuresit represents the most contiguous and complete conifer (gym-nosperm) genome sequence available (Appendix C)

Materials and Methods

Plant material

Wind-pollinated seeds (the source of a megagametophyteDNA) were collected from a grafted ramet of P taeda genotype20-1010 in a Virginia Department of Forestry seed orchardnear Providence Forge Virginia Needles were collected fromgrafted ramets of the P taeda genotype 20-1010 growing atthe Erambert Genetic Resource Management Area near BrooklynMississippi and the Harrison Experimental Forest near SaucierMississippi

DNA isolation

Megagametophytes were individually dissected from theseeds Each megagametophyte was frozen in liquid nitrogenand ground to a fine powder with a mortar and pestle andDNA was extracted using the NucleoSpin Plant II kit withbuffer PL2 DNA yield was quantified using a PicoGreenfluorometric assay Nuclei were isolated from the needlesusing the method in Peterson et al (2000) with the excep-tion that two layers of MiraCloth were substituted for cheese-cloth in the second filtration step and the 375 Percollgradient was replaced by a layered gradient of 30 60 and90 Percoll atop a final layer of 85 sucrose (intact nucleiaccumulated just above the sucrose layer) A total of 500 mlof extraction buffer nuclear isolation buffer (MEB) was usedfor every 50 g of needles (Note that during preparation ofMEB the pH must first be stabilized between pH 3 and 4

876 A Zimin et al

with concentrated HCl before being brought back to a finalpH of 60 with NaOH) In some instances the above methodwas modified by doubling the concentrations of anti-oxidantsand nuclease inhibitors in the extraction buffer and extractingthe crude homogenate with chloroform just prior to the firstcentrifugation step

Isolated nuclei for fosmid cloning were embedded in agaroseplugs and nuclear DNA was further purified by pulsed-fieldgel electrophoresis (Appendix A) Nuclei for jumping libraryconstruction were resuspended in 4 ml of MPDB (2-methyl-24-pentanediol buffer) and then aqueous SDS was added toa final concentration of 2 (wv) Contents were mixed gentlyto lyse the nuclei and placed on ice for 20 min A 1100 volumeof 10 mgml Proteinase K was added and the mixture wasincubated for 30 min at 37 A second 1100 volume aliquot ofProteinase K was added followed by another 30-min incuba-tion at 37 and then 7565 g of finely ground CsCl2 was addedand the mixture was incubated at room temperature for30 min with gentle rocking Volume was brought to 85 mlwith MPDB and the mixture was centrifuged at 90000 3 gfor72 hr Fractions were collected from the resulting gradientand the fractions containing DNAwere dialyzed for several daysagainst multiple 4-liter volumes of 13 TE (10mMTris-HCl 1 mMEDTA pH 80) DNA yield was quantified using a PicoGreen fluo-rometric assay To reduce the presence of single-strand nicks(which increase the fraction of nonjunction mate pairs) DNAfor jumping library construction was in some instances treatedwith 10 units of S1 nuclease per microgram of DNA thusconverting single-strand nicks into double-strand breaks

Megagametophyte selection

Care was taken to select a single haploid megagametophytefor sequencing from recently collected 20-1010 seeds Illuminalibrary bias and complexity is well known to be affected by thequality and quantity of the input DNA (Ross et al 2013) Theamount of DNA recovered in a preliminary experiment fromthe haploid tissue of a loblolly pine megagametophyte variedconsiderably (mean 135 mg standard deviation 050 mg) andwas always below the ideal for the Illumina paired-end libraryprotocol From a set of 17 megagametophyte DNA samplesselected for preliminary library construction and sequencingone with a high DNA yield (21 mg in the upper 10) wasselected for construction of a series of short-insert libraries tobe deeply sequenced Coincidentally these selected librariesranked among the lowest in the amount of contaminatingplastid DNA as well as in a measure of bias using homologyto repeats in existing BAC sequences (Kovach et al 2010)

Sequence data

Second-generation genome sequencing projects can benefitgreatly by sequencing a diversity of libraries which can reducebias (Ross et al 2013) and provide linking information atdifferent scales (Gnerre et al 2011 Birol et al 2013 Wanget al 2013) We sequenced 11 haploid paired-end Illuminalibraries on three Illumina platforms The HiSeq 2000 wasthe least expensive per base pair but read length (100 bp) was

the shortest We obtained reads up to 160 bp from theGAIIx but with lower throughput and increased cost and250-bp paired-end reads from a MiSeq instrument whichhas the lowest throughput and the highest cost per base Intotal 14 trillion base pairs (Tbp) of short-insert paired-endhigh-quality sequence was obtained (Table 1) which corre-sponds to 643 coverage of a 22-Gbp genome

A total of 48 Illumina long-insert mate pair libraries weresequenced on the Illumina GAIIx (Appendix B Table B2) Asexpected from the limited efficiency of the library construc-tion protocol the complexity of each library (defined as thenumbers of unique molecules sequenced) was much lowerthan for the short-insert libraries The total raw sequencecoverage obtained from long-insert libraries was 133

Construction of paired-end librariesfrom megagametophytes

The entire DNA amount extracted 21 mg for the targetmegagametophyte was fragmented by sonication using aBioruptor UCD-200 (Diagenode) at high power for 15 cyclesof 30 sec on30 sec off Universal Illumina paired-endadapter was ligated to the end-repaired A-tailed fragmentsTo create highly size-specific libraries but preserve the max-imum amount of DNA for library construction the adapter-ligation product was size selected on a 2 agarose gel bycollecting a series of 1-mm gel slices along the length ofthe gel lane with mean insert sizes ranging from 209 to 638bp (see Figure 1) DNA was extracted from the gel slicesusing a QIAGEN MinElute kit and each DNA aliquot wasused in its entirety as template for a 10-cycle enrichmentPCR with Phusion HF DNA polymerase and barcoded pri-mers Libraries were quantified on an Agilent Bioanalyzer2100 and sequenced on the GAIIx and HiSequation 2000platforms

Subsequent alignment using Illuminarsquos CASAVA pipeline(version 182) to the P taeda chloroplast genome and avail-able genomic sequence was used for determining insert-sizestatistics To estimate the empirical distributions shown inFigure 1 100000 correctly oriented paired-end read align-ments were sampled from each library to construct an insert-size histogram

Long-insert mate pair (jumping) libraries

Jumping libraries were constructed using standard Illuminamethods with either Illumina Mate Pair Library v2 kits or

Table 1 Overview of WGS sequence obtained for this project

Library type InstrumentFragmentsize (bp)

Readlength (bp) Coverage

Illumina Paired-end GAIIx 200ndash657 156ndash160 223Illumina Paired-end HiSeq 200ndash657 100ndash128 423Illumina Paired-end MiSeq 350ndash657 255 13Illumina Mate Pair GAIIx 1300ndash5500 156ndash160 133Fosmid DiTag GAIIx 35000ndash40000 156ndash160 13

The final column reports the nonredundant depth of coverage that was obtainedfor each library and instrument type based on a genome size of 22 Gbp

Sequencing and Assembling P taeda 877

comparable reagents from other vendors Briefly at least10 mg of DNA was fragmented to the desired size rangeusing a HydroShear Plus (Digilabs) DNA was end-repairedfor 15 min with unmodified dNTPs and then for an addi-tional 15 min with biotinylated dNTPs Where the Illuminakit was not used we found that a mixture of 075 mM bi-otin-16-AA-29-dUTP (TriLink Biotechnologies) and 075 mMbiotin-16-AA-29-dCTP (TriLink Biotechnologies) could besubstituted for the biotin-dNTP supplied in the kits Biotiny-lated DNA was size-selected on a 06 MegaBaseagarose(BioRad) gel run overnight at 1 Vcm The gel was cutto collect DNA fractions with defined size ranges andDNA was extracted from each gel slice using the QIAEX IIkit (Qiagen) Circularization reactions containing up to 600ng (2- to 5-kb libraries) or 1200 ng (5-kb libraries) of DNAwere run overnight at 30 Where non-kit reagents wereused we found that T4 DNA ligase (New England Biolabs)at a final concentration of 90 unitsml achieved efficientcircularization Noncircularized fragments were removed bydigestion with exonuclease and the remaining circularizedmolecules were fragmented by sonication using a BioruptorUCD-200 (Diagenode) Biotinylated (junction) fragmentswere pulled down using Dynabeads M-280 streptavidinmagnetic beads (Invitrogen) and end-repair A-tailing andadapter-ligation reactions were performed on the biotiny-lated fragments bound to the beads The adapter-ligatedfragments were then used as template for 18 cycles of enrich-ment PCR All jumping libraries were made using multiplexpaired-end adapters and oligos as described for fragmentlibraries above We ultimately preferred to use KAPAHiFiHotStartReadyMix (KAPA Biosystems) over Phusion poly-merase for the enrichment PCR taking care to follow the man-ufacturerrsquos recommendations regarding denaturation times andtemperatures

Finally the libraries were quantified on an Agilent Bioana-lyzer and sequenced on a GAIIx with a paired-end read lengthof 160 3 156 bp a run regime chosen for compatibility withshort-insert libraries sequenced simultaneously on adjacentlanes of the flow cell The sequenced reads were aligned toavailable high-quality P taeda reference sequence to confirm

that the insert sizes and numbers of nonjunction fragmentswere within acceptable ranges

Fosmid DiTag libraries

We constructed fosmid DiTag libraries as follows DNAfrom diploid needle tissue was cloned using the same method asdescribed below for fosmid pool construction to create a particlelibrary of 20 million clones in vector pFosDT54 The vectorpFosDT54 incorporates features allowing two methods for thecreation of fosmid DiTags (Figure 2) The first a nick-translationmethod (Williams et al 2012) utilizes two NbBbvCI recognitionsites located on opposite vector strands flanking the insert (Fig-ure 3A) Digestion with enzyme NbBbvCI introduced a nick ineach strand This was followed by nick translation with Escher-ichia coli DNA polymerase I that moved nicks toward each otherand inside the insert The distance between insert ends and nicksis controlled by the polymerase concentration Nicks were thenconverted to double-strand breaks with S1 nuclease repaired toblunt ends and the resulting internally deleted fosmid vectorswith truncated insert sequences attached were recirculated atlow DNA concentration to prevent formation of chimeras by in-termolecular ligation

Alternatively the nick translation approach was replacedwith an endonuclease digestion method (Figure 3B) Thismethod was facilitated by engineering the pFosDT54 vectorto contain no FspBl or Csp6l recognition sites Fosmid mol-ecules were digested with each enzyme either singly or incombination (the two enzymes generate compatible ends)to release a large internal fragment of the genomic insertwhile leaving behind ends of indeterminate length This wasfollowed by recircularization of the internally deleted fosmids

Following recircularization the remaining insert endswere amplified by PCR using the Illumina primers built intothe vector and the resulting library was size-selected on anagarose gel to collect library molecules with insert sizes rangingfrom 150 to 450 bp As an internal control we supplementedthe libraries at a 1 ratio with library molecules made fromDrosophila melanogaster genomic DNA using the same vectorThe resulting DiTag libraries were sequenced on an IlluminaGAIIx (Appendix B Table B3)

Figure 1 Partition library construction(A) Scheme to partition the single mega-gametophyte DNA sample into multiplelibraries Megagametophyte DNA wassonicated and then run out on an aga-rose gel the target size range was ex-cised and partitioned into equal spacedslices The goal was to set the numberand sizes of slices such that the coeffi-cient of variation on the insert-size dis-tribution within each fraction was smallenough to support high quality de novoassembly (B) The empirical fragmentsize distributions of partitioned librariesmade from our target megagametophyte

878 A Zimin et al

Estimating the haploid genome size from haploidk-mer data

Recall that a k-mer is a contiguous sequence of length kthus a read of length L contains L ndash k +1 k-mers We com-puted k-mer histograms of haploid sequence data using theprogram jellyfish (Marcais and Kingsford 2011) To reducethe effect of low-quality data we used the QuORUM error-corrected reads (below) To avoid quantization error thetotal count for every distinct k-mer was computed indepen-dently of the histogram

The genome size was estimated as the total number ofk-mers divided by the expected depth or multiplicity ofdistinct k-mers in the data The approach used to estimatethe latter quantity is to focus on the range of the distributionin which most k-mers are unique and substitute an estimateof the expected depth of a unique k-mer instead Uponinspecting our empirical data we determined that k-mersoccurring 15 or fewer times were mostly errors We alsoconsidered k-mers observed 120 times (approximatelytwice the most common multiplicities of unique k-mers) tobe repeats We then determined the expected value of thek-mer depth distribution from a cubic interpolating spline fitto the data using MATLAB fit after outliers had been re-moved This gave a slightly smaller estimate of the expectedk-mer depth than the more typical approach of using themaximum (mode)

Whole-genome shotgun assembly with MaSuRCA

Sequence preprocessing and error correction TheMaSuRCA assembly pipeline first corrected errors in theIllumina reads using QuORUM (Marcais et al 2013)a method that uses k-mer frequencies and quality scoresto correct errors A notable aspect of this was that the list ofldquogoodrdquo 24-mers used for error correction was compiled onlyfrom the haploid paired-end data During the process oferror correction SNPs between the haplotypes are indistin-guishable from error A fraction of SNPs in the reads from thediploid mate pair and DiTag libraries was changed to matchour target megagametophyte haplotype When larger differ-ences were present such as insertions or deletions the errorcorrector deleted these reads In addition the set of 24-mersbelonging to Illumina adapters chloroplast and mitochon-drial DNA were identified and added to a special QuORUMldquocontaminantrdquo list QuORUM truncated the read if it encoun-tered a contaminant 24-mer The trimming of mate pair andDiTag reads to the junction site was also accomplished duringthis phase

Optimized super-read construction For the super-readreduction the MaSuRCA assembler utilized k-mers fromthe error corrected paired-end reads as a summary of theunderlying haploid genome The k-mer length was opti-mized to maximize the number of distinct k-mers utilized bythe super-read reduction (see Zimin et al 2013 for details)If k is too small the number of distinct k-mers will decreasedue to short repeats Conversely if k is too large distinct

regions of the genome may be missing due to low coverageA grid search to find the optimal value was performed be-tween k = 70 and k = 85 From this we inferred an optimalvalue for the number of utilized k-mers at k = 79 Thisbecame the chosen k-mer length

We then applied the super-reads reduction using a data-base of 79-mers to the deep-coverage paired-end data fromthe megagametophyte This produced a set of super-readsbased strictly on the haploid DNA We discuss in detail theoutcome of this phase in the Results

Assembly with diploid reads Because the long-insert librarieswere constructed from diploid pine needles half of thesefragments on average derived from a different parental genomethan the haploid megagametophyte Many of these pairscontained differences that distinguished them from the haploidDNA Although most of the reads with haplotype differenceswere corrected or trimmed some remained after error correc-tion To reduce the number of scaffolding conflicts caused byheterozygosity we required that all 57-mers in both reads ina linking pair were present in the haploid paired-end data

Figure 2 An overview of the pFosDT54 fosmid cloning vector used forDiTag creation The cloning site (where the genomic DNA is inserted) isflanked by forward and reverse Illumina primers to enable end sequenc-ing Two NbBbvCl nicking endonuclease sites are located 59 of the Illu-mina primers on both strands to allow for creation of DiTags by a nicktranslation method The vector backbone has had all FspBl and Csp6l sitesremoved to allow for creation of DiTags by endonuclease digestion

Sequencing and Assembling P taeda 879

otherwise the read pair was rejected This step should haveretained all pairs from the same haplotype

We also implemented a preprocessing step to removeapparently duplicate molecules from the long-insert libraries Thisstep is critical for long-insert libraries because of their reducedlibrary complexity By filtering these out we ensure that eachlink inferred from long-insert reads is independently ascertained

We discuss details of coverage and filtering results forboth types of long-insert linking libraries in the Results Thefiltered linking reads were combined with super-reads andassembled using an adapted version of CABOG

The MaSuRCA assembler was run on a 64-core IA64computer with 1 terabyte of RAM Running the assemblypipeline took 3 months The maximum memory usage duringthe assembly reached 800 GB during the super-reads step Theoutput of MaSuRCA was the 10 version of the assembly (seeTable 3)

Results and Discussion

Haploid megagametophyte library complexity

The complexity of a library is typically measured as thenumber of distinct molecules in the library (Daley and Smith2013) For the library construction protocols described herethis is less than the total number of molecules in the librarydue to a PCR amplification step as well as in vivo amplifi-cation for fosmid clone-based libraries

Library complexity curves which plot the number ofdistinct molecules against the number of molecules se-quenced were used to characterize the rate of diminishingreturns because a library of finite complexity is sequenced

more deeply We constructed these curves on QuORUMerror-corrected data as follows We represented each mol-ecule by a concatenation of a short prefix and suffix oflength k giving a sequence or tag of length 2k For strandconsistency the prefix was obtained from the first read andthe suffix was obtained from the reverse complemented sec-ond read We chose the parameter k to be large enough(here k = 24) so that the tags were typically unique Twotags were identified as the same molecule if their sequencesmatched exactly in the same or opposite orientations Fromthe tags we constructed a histogram of molecular frequencyin the sequenced sample which we sampled without re-placement to create the complexity curve

Figure 4 plots the number of distinct read pairs againstthe total number of sequenced pairs The shape of the curveindicates how fast the libraryrsquos complexity is being depletedie how the likelihood of a duplicate increases as sequenc-ing continues For our most deeply sequenced library Figure4 shows the limits of paired-end library complexity whenconstructed from a megagametophyte For our deeply se-quenced library the curve shows that additional sequencingwould sample new molecules at a rate of 40 It alsoindicates that for most of our libraries deeper sequencingwould yield additional useful data When considered withthe partition scheme described previously we see that ourmegagametophyte WGS protocol is a valuable method forobtaining highly broad and deep paired-end sequence datatypes for de novo assembly

Haploid k-mers and an estimate of the 1N genome size

Using short k-mers from all the reads we estimated the genomesize to provide an assessment of the overall paired-end library

Figure 3 Schematic for our two methods for converting a fosmid library into an Illumina compatible DiTag library using the fosmid vector created forthis project (A) A nick translation approach similar to the approach used in Williams et al (2012) was implemented for approximately one-half of thelibraries (B) An endonuclease digestion protocol was also used for approximately one-half of the libraries Although the location of the junction sites ismore constrained in practice we obtained higher yields from this method

880 A Zimin et al

bias and complexity and to appraise previous estimates basedon flow cytometry

Figure 5 shows a histogram of k-mer counts in our hap-loid error-corrected reads computed using the program jel-lyfish (Marccedilais and Kingsford 2011) for k = 24 and k = 31Small values were chosen for tractability as well as goodseparation between the unique haploid k-mers and uncor-rected errors Each point in the plot corresponds to the num-ber of k-mers with a particular count for example thenumber of 31-mers that occur 50 times is plotted in red atx = 50 Note that we expect a k-mer that occurs just once inthe genome (ie is nonrepetitive) to occur C times on aver-age where C is depth of coverage For haploid data this plotwould ideally take the form of a single-mode Poisson distri-bution with mean equal to C (Lander and Waterman 1988)Deviations are attributable to other properties of the ge-nome and to systematic errors in library construction andsequencing Each sequencing error may create up to k sin-gle-copy k-mers producing a peak around x = 1 The num-ber of these k-mers have been greatly reduced in Figure 5 bythe QuORUM error-correction algorithm The long tail in theplot contains conserved high-copy-number repeats

We estimated the genome size as the ratio of the totalnumber of k-mers divided by the mean number of occur-rences of the unique k-mer counts (an estimate of theexpected depth of distinct sequences of length k) as shownin Figure 5 Table 2 gives these quantities as well as thegenome size estimates for k= 24 and k= 31 Both estimatesare slightly smaller than the value determined by flowcytometry (216 Gbp) (OrsquoBrien et al 1996) and are consis-tent with our final total contig length

Haploid sequence data reduction from readsto super- reads

The goal of the super-read algorithm is to reduce thequantity of sequence data presented to the overlap-basedassembler Each maximal super-read included in the down-stream assembly passed two critical tests successful fillingof the gap between a pair of reads and determination thatthe extended super-read was not properly contained withinanother super-read (maximal)

Paired-end gap fill The extension of a read by the super-read algorithm is done conservatively with the philosophy ofrelegating the important assembly decisions to the overlap-based assembly Hence read extension will halt when itcannot be done unambiguously For a pair of reads to resultin a super-read the algorithm needs to successfully fill in the

sequence of the gap (see Zimin et al 2013) Two propertiesof the sequenced genome play a large role in the success rateof gap filling The first is the number and size of repetitivesequences in the genome An unresolved repetitive sequencewill prevent filling the gap More repeats can be resolved byusing a longer k-mer size as explained above we chose k =79 for P taeda We avoided the second property of concerndivergence between the two parental chromosomes in a dip-loid genome by constructing all short-insert libraries fromDNA of a single haploid megagametophyte

As the gap size increases so does the likelihood ofencountering ambiguity making it more difficult to fill gapsfor longer fragments For our haploid read pairs the rate ofsuccessful gap filling was as high as 96 for overlappingGAIIx reads from our shortest library Over the range of gapsizes in our data we observed an approximately linear 6reduction in the rate of successful gap filling for every 100-bp increase in the expected gap size (Appendix B Table B1)This is consistent with a relatively low per-base probability(6 3 1024) that a problem is encountered during gapfilling over the range of gap sizes observed

Maximality Only the subset of ldquomaximalrdquo super-reads ispassed to the overlap-based assembler because fully containedreads would not provide additional contiguity informationThere is a trade-off in producing maximal super-reads Assum-ing that reads have the same length it is easier to fill the gapbetween pairs that come from shorter fragments But for li-braries of longer fragments although successful gap filling isless frequent more of the gap-filled pairs generate maximalsuper-reads We observed that the percentage of maximalsuper-reads uniformly increased with library fragment lengthdespite reduced gap-filling effectiveness (Appendix B TableB1) Thus the lower gap-filling rate for longer fragments didnot detract from their utility

We can use the rate of maximal super-read creation toestimate the marginal impact of additional paired-endsequencing To improve the assembly additional paired-end reads must be able to generate new maximal super-reads In our analysis we found that 24 of the read pairsin longer (500 bp) libraries produced additional maximalsuper-reads vs only 8ndash10 of the pairs in libraries shorterthan 300 bp (Appendix B Table B1) This suggests that themost effective way to improve the assembly further wouldbe to prioritize additional paired-end data from 500-bp orlonger fragment libraries

Overall paired-end data reductionMaSuRCArsquos error-correctionand super-reads processes reduced the 14 Tbp of raw paired-end read data (in 15 billion reads) to 52 Gbp in super-readsa 27-fold reduction The reduced data contained 150 millionmaximal super-reads with an average length of 362 bp equiv-alent to 243 coverage of the genome This 100-fold reductionin the number of paired-end reads is critical for the next step ofassembly which computes pairwise overlaps between thesereads along with the linking libraries

Table 2 Haploid (1N) genome size estimates

k-mer length

31 24Total k-mers 108 3 1012 116 3 1012

E(distinct k-mer depth) 5376 occurrences 5699 occurrencesGenome size (Gbp) 2012 2042

Sequencing and Assembling P taeda 881

Our sequencing strategy used the lowest-cost and mostaccurate datamdashthe reads from the HiSeq platformmdashto pro-vide the bulk of the k-mers required for error correction Wethen generated super-reads from all error-corrected readsincluding the longer reads from the GAIIx and MiSeq instru-ments As expected the GAIIx and MiSeq reads contributeda greater proportion of maximal super-reads The optimalmix of read lengths is difficult to determine and likely variesfor every genome

Incorporating diploid mate pair sequences

We obtained 1726 million paired reads from 48 mate pairlibraries with insert sizes of 1300ndash5500 bp (Table 2 andAppendix A Table A1) After filtering to remove reads thatfailed to match the haploid DNA 1156 million reads (69)remained Another 70 million paired reads that failed tocontain a biotin junction and 171 million paired readsidentified as duplicates were removed Finally we elimi-nated pairs in which either read was shorter than 64 bpthe minimum read length for the CABOG assembler Thefinal set of jumping reads contained 540 million reads(270 million pairs) which is 37-fold physical coverageof the genome (Appendix B Table B2) Notably there waslittle variation in the rate at which reads were filtered dueto error correction and haplotype differences as the li-brary length increased

Fosmid DiTags

Particularly useful for establishing long-range links in anassembly are paired reads from the ends of fosmid (Kimet al 1992) or BAC clones (Shizuya et al 1992) which spantens of kilobases Long-range paired-end data are even morecritical for second-generation sequencing projects (Schatz

et al 2010) where read lengths are significantly shorterthan they are for first-generation sequencing projects Recentreports demonstrate that mammalian genome assemblies con-taining high-quality fosmid DiTags can attain contiguity nearlyas well as traditional Sanger-based assemblies (Gnerre et al2011)

We generated 46 million pairs of DiTag reads (AppendixB Table B3) Read pairs from DiTag libraries are subject tothe same artifacts as those from mate pair libraries namelyduplicate molecules and nonjumping pairs Application ofthe aforementioned MaSuRCA filtering procedures for jump-ing libraries was applied and yielded 45 million distinctmapped read pairs for downstream overlap layout consensusassembly The largest reduction (47 of reads removed)was due to the removal of duplicate read pairs a result ofthe limited library complexity (Appendix B Table B3) Thefiltering statistics also indicated that the nick translationprotocol was the most efficient at generating distinctDiTag read pairs To obtain an unbiased estimate of thelength distribution of these libraries we incorporated a setof D melanogaster fosmids into select pools at 1 concen-tration prior to DiTag construction The median insert sizemeasured after removal of nonjumping and chimeric out-liers was 357 kbp Using a genome-size estimate of 22 Gbpfor loblolly pine this represents 733 physical coverage of thehaploid genome

While the inclusion of fosmid DiTag libraries was helpfulonly approximately one-third of the V10 MaSuRCA assem-bled genome scaffolds (by length) contained the two ormore DiTags required to make a long-range link This leadsus to conclude that deeper fosmid DiTag coverage couldsignificantly boost scaffold contiguity in future assemblies

Figure 4 Library complexity curves quan-tify library complexity and the diminishingreturns as sequencing progresses Librarycomplexity is an estimate of the numberof distinct molecules in the library (Daleyand Smith 2013) The convexly shapedcomplexity curve plots the number of dis-tinct molecules observed against thenumber of sequenced reads Only ourdeeply sequenced library exhibited dimin-ishing returns Libraries sequenced tolower depth are shown in the inset

882 A Zimin et al

Additional scaffolding procedures for final assembly

To produce the final assembly (version 101) from the outputof MaSuRCA (version 10) we employed two additionalstrategies that improved the length and accuracy of some ofthe scaffolds First we ran an independent WGS assemblyusing SOAPdenovo2 including its gap-closing module (Luoet al 2012) We used all haploid paired-end data for the contigassembly and added the mate pair and DiTag libraries for thescaffolding step only Following sequencing and quality con-trol the adapter sequence was removed from reads using cuta-dapt (Martin 2011) and the input data were filtered toremove sequences similar to the chloroplast reference se-quence (Parks et al 2009) After error correction 1144 billionreads were used for contig assembly and 1198 billion reads forscaffolding We used a k-mer length of 79 for contig assembly

Although the SOAPdenovo2 contigs are much smallerthan in the 10 assembly with an N50 size of just 687 bp thescaffolds are larger with an N50 size of 547 kbp Thereforewe ran the SOAPdenovo2 scaffolder again using the MaSuRCAscaffolds as input ldquocontigsrdquo and using a conservatively filteredset of mate pairs as linking information This produced a newset of 834 million scaffolds with an N50 size of 868 kbp sub-stantially larger than the original value

As discussed above the DiTag sequences prior to MaSURCAprocessing contained a moderate number of nonjunction readpairs (Appendix B Table B3) To improve the assembly fidelityduring this step we broke all newly created intrascaffold links

for which all supporting mates originated from a DiTag libraryThe resulting modified assembly had 144 million scaffoldswith an N50 size of 646 kbp still over twice as large as theoriginal scaffold N50 size

We used transcripts assembled de novo from RNA-seqreads and predicted to encode full-length proteins (see NationalCenter for Biotechnology Information BioProject 174450) toidentify scaffolds that could be joined or rearranged to becongruent with transcripts Because transcripts can be assem-bled erroneously we followed a conservative strategy that pri-marily linked together scaffolds that contained a single gene

We aligned the 87241 assembled transcripts to all scaffoldsin the modified assembly described above using the nucmerprogram in the MUMmer package (Kurtz et al 2004) andcollecting only those transcripts with at least two exactmatches of 40 bp or longer This yielded 68497 transcriptsmapping to 47492 different scaffolds

Alignments between transcripts and the WGS assemblywere checked for consistency looking for instances wherethe scaffold would require an inversion or a translocation tomake it align We found 1348 inconsistencies 2 of thetotal These could represent errors in either the de novotranscriptome assembly or the WGS assembly and they willbe investigated and corrected if necessary in future assemblyreleases

We scanned the alignments looking for transcripts thatspanned two distinct scaffolds For each transcript we sorted

Figure 5 Plot of k-mer counts in the haploid paired-end WGS sequence After counting all 24- and 31-mers in the QuORUM error-corrected reads weplotted the number of distinct k-mers (y-axis) that occur exactly X times (x-axis) For our haploid data each plot has a single primary peak at the X-valuecorresponding to the depth of coverage of the 1N genome

Sequencing and Assembling P taeda 883

the scaffolds aligned to it and created a directed link betweeneach pair of scaffolds The links were weighted using the sumof the alignment lengths If multiple transcripts aligned to thesame pair of scaffolds the link between them was given thesum of all the weights (This can happen when for exampletwo transcripts represent alternative splice variants of the samegene) We rebuilt the scaffolds using these new links in orderof priority from strongest (greatest weight) to weakest checkingfor circular links at each step and discarding them

This transcript-based scaffolding step linked together31231 scaffolds into 9170 larger scaffolds slightly increas-ing the N50 size and substantially improving the number oftranscripts that align to a single scaffold The final assemblyversion 101 has 144 million scaffolds spanning 232 GbpExcluding gaps the total size of all contigs is 2015 Gbp Thelargest scaffold is 8891046 bp The scaffold N50 size is66920 bp based on a 22 Gbp total genome size (Table 3)

Assessing assembly contiguity and completeness

We used the CEGMA pipeline (Parra et al 2007) to assessthe contiguity of both the V10 and V101 assemblies withrespect to a set of highly conserved eukaryotic genes CEGMAuses a set of conserved protein families from the Clusters ofOrthologous Groups database (Tatusov et al 2003) to an-notate a genome by constructing DNA-protein alignmentsbetween the proteins and the assembled scaffolds Of 248highly conserved protein families CEGMA found full-lengthalignments to the V10 assembly for 113 (45) Includingpartial alignments brings this total to 197 (79) In theV101 assembly CEGMA found 185 (75) full-length align-ments and 203 (82) full-length and partial alignmentsThere was little change in the total number of alignmentsbetween V10 and V101 However the fraction of all align-ments to the assembly that were full length increased from57 in the V10 assembly to 91 in the V101 assemblyfurther quantifying the biological utility of the additionalscaffolding phase There are a number of possible explanationsfor the discrepancy between the coverage and the observedrates of conserved gene annotations principally assemblyfragmentation and potential ascertainment bias in the de-termination of the gene set

Comparison of fosmid and whole-genome assemblies

For validation purposes we constructed sequenced andassembled a large pool of 4600 fosmid clones (Appendix A)The assembly contained 3798 contigs longer than 20000 bp

(50 of the fosmid insert in each case) which we used tocheck the completeness and quality of the WGS assembly Be-cause the fosmids were selected at random the amount ofsequence covered by the WGS assembly should provide anestimate of how much of the entire genome has been capturedin that assembly The 3798 contigs contain 109 Mbp or05of the entire genome

We aligned contigs to the WGS assembly (V10V101contigs) using MUMmer (Kurtz et al 2004) and found thatall but 4 of the 3798 contigs aligned across nearly theirentire length at an average identity of 99 Nearly all(9863) of the combined length of the fosmid pool contigsis covered by alignments to the WGS assembly The un-aligned fraction consists of the 4 contigs that failed to align(117681 bp) plus the unaligned portions of the remainingcontigs Assuming that the fosmids represent a random sam-ple this suggests that the WGS assembly covers 98 ofthe whole genome (with the reservation that hard-to-sequence regions might be missed by both the WGS andfosmid sequencing approaches and thus not counted in thisevaluation)

Fosmid haplotype comparisons

We expect that half of the fosmid contigs would correspondto the same haplotype as the aligned WGS contig Thuswhile many contigs should be close to 100 identical contigsfrom the alternative haplotype (untransmitted) would showa divergence rate corresponding to the differences betweenhaplotypes estimated to be 1ndash2 We considered the pos-sibility that the fosmid contigs would thus fall into two dis-tinct bins near-identical and 1ndash2 divergent Howeverrelatively few contigs showed this degree of divergence2120 contigs (56) were 995 or more identical and theothers matched the WGS contigs at a range of identities withthe vast majority 98 identical (Appendix A Figure A1)Only 189 contigs (5) were 95 identical with the lowestidentity at 91 This suggests that divergence between thetwo parental haplotypes is 1 on average with a smallnumber of highly divergent regions

Conclusions

The sequencing and assembly strategy described here ofthe largest genome to date resulted in a haploid assemblycomposed of 2015 billion base pairs By many measures itis the most contiguous and complete draft assembly ofa conifer genome (Appendix C) This project required a close

Table 3 Comparison of P taeda whole-genome assemblies before and after additional scaffolding

P taeda 10 (before) P taeda 101 (after)

Total sequence in contigs (bp) 20148103497 20148103497Total span of scaffolds (bp) 22564679219 23180477227N50 contig size (bp) 8206 8206N50 scaffold size (bp) 30681 66920No of contigs 500 bp 4047642 4047642No of scaffolds 500 bp 2319749 2158326

The contigs are the same for both assemblies N50 statistics use a genome size of 22 3 109

884 A Zimin et al

coupling between sequencing and assembly strategy and thedevelopment of new computational methods to address thechallenges inherent to the assembly of mega-genomes

Conifer genomes are filled with massive amounts ofrepetitive DNA mostly transposable elements (Nystedt et al2013 Wegrzyn et al 2013 2014) which might have pre-sented a major barrier to successful genome assembly Fortu-nately most of these repeats are relatively ancient so theiraccumulated sequence differences allowed the assembly algo-rithms to distinguish individual copies

A major concern in assembly of diploid genomes is thedegree of divergence between the two parental copies ofeach chromosome Even small differences between the chro-mosomes can cause an assembler to construct two distinctcontigs which can be indistinguishable from two-copy repeatsequences Conifers offer an elegant biological solution to thisproblem by producing megagametophytes with a single copyof each chromosome from which sufficient DNA can beextracted to cover the entire genome

While the current assembly represents an important land-mark and will serve as a powerful resource for biologicalanalyses it remains incomplete To further increase sequencecontiguity we plan to generate additional long-range pairedreads particularly DiTag pairs that should substantiallyincrease scaffold lengths Noting the success of the smallerfosmid pool assemblies presented here inexpensive sequenc-ing allows for the possibility of sequencing many more of thesepools and merging the long contigs from these assemblies intothe WGS assembly (Zhang et al 2012 Nystedt et al 2013) Inparallel with efforts to improve the next P taeda referencesequence we are gathering a large set of genetically mappedSNPs that will allow placement of scaffolds onto the 12 chro-mosomes providing additional concrete physical anchors tothe assembly Finally we note that ongoing efforts to expandthe diversity of conifer genome reference sequences will pro-vide a solid foundation for comparative genomics thus improv-ing the assemblies and their annotations

Acknowledgments

This work was supported in part by US Department ofAgricultureNational Institute of Food and Agriculture(2011-67009-30030) We wish to thank C Dana Nelson atthe USDA Forest Service Southern Research Station forproviding and verifying the genotype of target tree material

Note added in proof See Wegrzyn et al 2014 (pp891ndash909) in this issue for a related work

Literature Cited

Ahuja M R and D B Neale 2005 Evolution of genome size inconifers Silvae Genet 54 126ndash137

Bai C W S Alverson A Follansbee and D M Waller 2012 Newreports of nuclear DNA content for 407 vascular plant taxa fromthe United States Ann Bot (Lond) 110 1623ndash1629

Bierhorst D W 1971 Morphology of Vascular Plants MacmillanNew York

Birol I A Raymond S D Jackman S Pleasance R Coope et al2013 Assembling the 20 Gb white spruce (Picea glauca) ge-nome from whole-genome shotgun sequencing data Bioinfor-matics 29 1492ndash1497

Bowe L M G Coat and C W Depamphilis 2000 Phylogeny ofseed plants based on all three genomic compartments extantgymnosperms are monophyletic and Gnetalesrsquo closest relativesare conifers Proc Natl Acad Sci USA 97 4092ndash4097

Daley T and A D Smith 2013 Predicting the molecular com-plexity of sequencing libraries Nat Methods 10 325ndash327

Frederick W J S J Lien C E Courchene N A DeMartini A JRagauskas et al 2008 Production of ethanol from carbohy-drates from loblolly pine a technical and economic assessmentBioresour Technol 99 5051ndash5057

Fuchs J G Jovtchev and I Schubert 2008 The chromosomaldistribution of histone methylation marks in gymnosperms dif-fers from that of angiosperms Chromosome Res 16 891ndash898

Gnerre S I MacCallum D Przybylski F J Ribeiro J N Burtonet al 2011 High-quality draft assemblies of mammalian ge-nomes from massively parallel sequence data Proc Natl AcadSci USA 108(4) 1513ndash1518

Kim U-J H Shizuya P J de Jong B Barren and M I Simon1992 Stable propagation of cosmid-sized human DNA insertsin an F factor based vector Nucleic Acids Res 20 1083ndash1085

Kovach A J L Wegrzyn G Parra C Holt G E Bruening et al2010 The Pinus taeda genome is characterized by diverse andhighly diverged repetitive sequences BMC Genomics 11(1) 420

Kurtz S A Phillippy A L Delcher M Smoot M Shumway et al2004 Versatile and open software for comparing large ge-nomes Genome Biol 5(2) R12

Lander E S and M S Waterman 1988 Genomic mapping byfingerprinting random clones a mathematical analysis Ge-nomics 2(3) 231ndash239

Li R H Zhu J Ruan W Qian X Fang et al 2010 De novoassembly of human genomes with massively parallel short readsequencing Genome Res 20(2) 265ndash272

Luo R B Liu Y Xie Z Li W Huang et al 2012 SOAPdenovo2an empirically improved memory-efficient short-read de novoassembler GigaScience DOI1011862047-217X-1-18

Mackay J J F Dean C Plomion D G Peterson F M Caacutenovaset al 2012 Towards decoding the conifer giga-genome PlantMol Biol 80 555ndash569

Magallon S and M J Sanderson 2005 Angiosperm divergencetimes the effects of genes codon positions and time con-straints Evolution 59 1653ndash1670

Marccedilais G and C Kingsford 2011 A fast lock-free approach forefficient parallel counting of occurrences of k-mers Bioinfor-matics 27(6) 764ndash770

Marcais G J Yorke and A Zimin 2013 QuorUM an error cor-rector for Illumina reads arXiv13073515 [q-bioGN]

Martin M 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet J 17(1) 10

McKeand S T Mullin T Byram and T White 2003 Deploymentof genetically improved loblolly and slash pines in the south J For101(3) 32ndash37

Miller J R A L Delcher S Koren E Venter B P Walenz et al2008 Aggressive assembly of pyrosequencing reads withmates Bioinformatics 24(24) 2818ndash2824

Mirov N T 1967 The Genus Pinus Ronald Press New YorkMorse A M D G Peterson M N Islam-Faridi K E Smith Z

Magbanua et al 2009 Evolution of genome size and complex-ity in Pinus PLoS ONE 4(2) e4332

Neale D B and A Kremer 2011 Forest tree genomics growingresources and applications Nat Rev Genet 12(2) 111ndash122

Sequencing and Assembling P taeda 885

comparable reagents from other vendors Briefly at least10 mg of DNA was fragmented to the desired size rangeusing a HydroShear Plus (Digilabs) DNA was end-repairedfor 15 min with unmodified dNTPs and then for an addi-tional 15 min with biotinylated dNTPs Where the Illuminakit was not used we found that a mixture of 075 mM bi-otin-16-AA-29-dUTP (TriLink Biotechnologies) and 075 mMbiotin-16-AA-29-dCTP (TriLink Biotechnologies) could besubstituted for the biotin-dNTP supplied in the kits Biotiny-lated DNA was size-selected on a 06 MegaBaseagarose(BioRad) gel run overnight at 1 Vcm The gel was cutto collect DNA fractions with defined size ranges andDNA was extracted from each gel slice using the QIAEX IIkit (Qiagen) Circularization reactions containing up to 600ng (2- to 5-kb libraries) or 1200 ng (5-kb libraries) of DNAwere run overnight at 30 Where non-kit reagents wereused we found that T4 DNA ligase (New England Biolabs)at a final concentration of 90 unitsml achieved efficientcircularization Noncircularized fragments were removed bydigestion with exonuclease and the remaining circularizedmolecules were fragmented by sonication using a BioruptorUCD-200 (Diagenode) Biotinylated (junction) fragmentswere pulled down using Dynabeads M-280 streptavidinmagnetic beads (Invitrogen) and end-repair A-tailing andadapter-ligation reactions were performed on the biotiny-lated fragments bound to the beads The adapter-ligatedfragments were then used as template for 18 cycles of enrich-ment PCR All jumping libraries were made using multiplexpaired-end adapters and oligos as described for fragmentlibraries above We ultimately preferred to use KAPAHiFiHotStartReadyMix (KAPA Biosystems) over Phusion poly-merase for the enrichment PCR taking care to follow the man-ufacturerrsquos recommendations regarding denaturation times andtemperatures

Finally the libraries were quantified on an Agilent Bioana-lyzer and sequenced on a GAIIx with a paired-end read lengthof 160 3 156 bp a run regime chosen for compatibility withshort-insert libraries sequenced simultaneously on adjacentlanes of the flow cell The sequenced reads were aligned toavailable high-quality P taeda reference sequence to confirm

that the insert sizes and numbers of nonjunction fragmentswere within acceptable ranges

Fosmid DiTag libraries

We constructed fosmid DiTag libraries as follows DNAfrom diploid needle tissue was cloned using the same method asdescribed below for fosmid pool construction to create a particlelibrary of 20 million clones in vector pFosDT54 The vectorpFosDT54 incorporates features allowing two methods for thecreation of fosmid DiTags (Figure 2) The first a nick-translationmethod (Williams et al 2012) utilizes two NbBbvCI recognitionsites located on opposite vector strands flanking the insert (Fig-ure 3A) Digestion with enzyme NbBbvCI introduced a nick ineach strand This was followed by nick translation with Escher-ichia coli DNA polymerase I that moved nicks toward each otherand inside the insert The distance between insert ends and nicksis controlled by the polymerase concentration Nicks were thenconverted to double-strand breaks with S1 nuclease repaired toblunt ends and the resulting internally deleted fosmid vectorswith truncated insert sequences attached were recirculated atlow DNA concentration to prevent formation of chimeras by in-termolecular ligation

Alternatively the nick translation approach was replacedwith an endonuclease digestion method (Figure 3B) Thismethod was facilitated by engineering the pFosDT54 vectorto contain no FspBl or Csp6l recognition sites Fosmid mol-ecules were digested with each enzyme either singly or incombination (the two enzymes generate compatible ends)to release a large internal fragment of the genomic insertwhile leaving behind ends of indeterminate length This wasfollowed by recircularization of the internally deleted fosmids

Following recircularization the remaining insert endswere amplified by PCR using the Illumina primers built intothe vector and the resulting library was size-selected on anagarose gel to collect library molecules with insert sizes rangingfrom 150 to 450 bp As an internal control we supplementedthe libraries at a 1 ratio with library molecules made fromDrosophila melanogaster genomic DNA using the same vectorThe resulting DiTag libraries were sequenced on an IlluminaGAIIx (Appendix B Table B3)

Figure 1 Partition library construction(A) Scheme to partition the single mega-gametophyte DNA sample into multiplelibraries Megagametophyte DNA wassonicated and then run out on an aga-rose gel the target size range was ex-cised and partitioned into equal spacedslices The goal was to set the numberand sizes of slices such that the coeffi-cient of variation on the insert-size dis-tribution within each fraction was smallenough to support high quality de novoassembly (B) The empirical fragmentsize distributions of partitioned librariesmade from our target megagametophyte

878 A Zimin et al

Estimating the haploid genome size from haploidk-mer data

Recall that a k-mer is a contiguous sequence of length kthus a read of length L contains L ndash k +1 k-mers We com-puted k-mer histograms of haploid sequence data using theprogram jellyfish (Marcais and Kingsford 2011) To reducethe effect of low-quality data we used the QuORUM error-corrected reads (below) To avoid quantization error thetotal count for every distinct k-mer was computed indepen-dently of the histogram

The genome size was estimated as the total number ofk-mers divided by the expected depth or multiplicity ofdistinct k-mers in the data The approach used to estimatethe latter quantity is to focus on the range of the distributionin which most k-mers are unique and substitute an estimateof the expected depth of a unique k-mer instead Uponinspecting our empirical data we determined that k-mersoccurring 15 or fewer times were mostly errors We alsoconsidered k-mers observed 120 times (approximatelytwice the most common multiplicities of unique k-mers) tobe repeats We then determined the expected value of thek-mer depth distribution from a cubic interpolating spline fitto the data using MATLAB fit after outliers had been re-moved This gave a slightly smaller estimate of the expectedk-mer depth than the more typical approach of using themaximum (mode)

Whole-genome shotgun assembly with MaSuRCA

Sequence preprocessing and error correction TheMaSuRCA assembly pipeline first corrected errors in theIllumina reads using QuORUM (Marcais et al 2013)a method that uses k-mer frequencies and quality scoresto correct errors A notable aspect of this was that the list ofldquogoodrdquo 24-mers used for error correction was compiled onlyfrom the haploid paired-end data During the process oferror correction SNPs between the haplotypes are indistin-guishable from error A fraction of SNPs in the reads from thediploid mate pair and DiTag libraries was changed to matchour target megagametophyte haplotype When larger differ-ences were present such as insertions or deletions the errorcorrector deleted these reads In addition the set of 24-mersbelonging to Illumina adapters chloroplast and mitochon-drial DNA were identified and added to a special QuORUMldquocontaminantrdquo list QuORUM truncated the read if it encoun-tered a contaminant 24-mer The trimming of mate pair andDiTag reads to the junction site was also accomplished duringthis phase

Optimized super-read construction For the super-readreduction the MaSuRCA assembler utilized k-mers fromthe error corrected paired-end reads as a summary of theunderlying haploid genome The k-mer length was opti-mized to maximize the number of distinct k-mers utilized bythe super-read reduction (see Zimin et al 2013 for details)If k is too small the number of distinct k-mers will decreasedue to short repeats Conversely if k is too large distinct

regions of the genome may be missing due to low coverageA grid search to find the optimal value was performed be-tween k = 70 and k = 85 From this we inferred an optimalvalue for the number of utilized k-mers at k = 79 Thisbecame the chosen k-mer length

We then applied the super-reads reduction using a data-base of 79-mers to the deep-coverage paired-end data fromthe megagametophyte This produced a set of super-readsbased strictly on the haploid DNA We discuss in detail theoutcome of this phase in the Results

Assembly with diploid reads Because the long-insert librarieswere constructed from diploid pine needles half of thesefragments on average derived from a different parental genomethan the haploid megagametophyte Many of these pairscontained differences that distinguished them from the haploidDNA Although most of the reads with haplotype differenceswere corrected or trimmed some remained after error correc-tion To reduce the number of scaffolding conflicts caused byheterozygosity we required that all 57-mers in both reads ina linking pair were present in the haploid paired-end data

Figure 2 An overview of the pFosDT54 fosmid cloning vector used forDiTag creation The cloning site (where the genomic DNA is inserted) isflanked by forward and reverse Illumina primers to enable end sequenc-ing Two NbBbvCl nicking endonuclease sites are located 59 of the Illu-mina primers on both strands to allow for creation of DiTags by a nicktranslation method The vector backbone has had all FspBl and Csp6l sitesremoved to allow for creation of DiTags by endonuclease digestion

Sequencing and Assembling P taeda 879

otherwise the read pair was rejected This step should haveretained all pairs from the same haplotype

We also implemented a preprocessing step to removeapparently duplicate molecules from the long-insert libraries Thisstep is critical for long-insert libraries because of their reducedlibrary complexity By filtering these out we ensure that eachlink inferred from long-insert reads is independently ascertained

We discuss details of coverage and filtering results forboth types of long-insert linking libraries in the Results Thefiltered linking reads were combined with super-reads andassembled using an adapted version of CABOG

The MaSuRCA assembler was run on a 64-core IA64computer with 1 terabyte of RAM Running the assemblypipeline took 3 months The maximum memory usage duringthe assembly reached 800 GB during the super-reads step Theoutput of MaSuRCA was the 10 version of the assembly (seeTable 3)

Results and Discussion

Haploid megagametophyte library complexity

The complexity of a library is typically measured as thenumber of distinct molecules in the library (Daley and Smith2013) For the library construction protocols described herethis is less than the total number of molecules in the librarydue to a PCR amplification step as well as in vivo amplifi-cation for fosmid clone-based libraries

Library complexity curves which plot the number ofdistinct molecules against the number of molecules se-quenced were used to characterize the rate of diminishingreturns because a library of finite complexity is sequenced

more deeply We constructed these curves on QuORUMerror-corrected data as follows We represented each mol-ecule by a concatenation of a short prefix and suffix oflength k giving a sequence or tag of length 2k For strandconsistency the prefix was obtained from the first read andthe suffix was obtained from the reverse complemented sec-ond read We chose the parameter k to be large enough(here k = 24) so that the tags were typically unique Twotags were identified as the same molecule if their sequencesmatched exactly in the same or opposite orientations Fromthe tags we constructed a histogram of molecular frequencyin the sequenced sample which we sampled without re-placement to create the complexity curve

Figure 4 plots the number of distinct read pairs againstthe total number of sequenced pairs The shape of the curveindicates how fast the libraryrsquos complexity is being depletedie how the likelihood of a duplicate increases as sequenc-ing continues For our most deeply sequenced library Figure4 shows the limits of paired-end library complexity whenconstructed from a megagametophyte For our deeply se-quenced library the curve shows that additional sequencingwould sample new molecules at a rate of 40 It alsoindicates that for most of our libraries deeper sequencingwould yield additional useful data When considered withthe partition scheme described previously we see that ourmegagametophyte WGS protocol is a valuable method forobtaining highly broad and deep paired-end sequence datatypes for de novo assembly

Haploid k-mers and an estimate of the 1N genome size

Using short k-mers from all the reads we estimated the genomesize to provide an assessment of the overall paired-end library

Figure 3 Schematic for our two methods for converting a fosmid library into an Illumina compatible DiTag library using the fosmid vector created forthis project (A) A nick translation approach similar to the approach used in Williams et al (2012) was implemented for approximately one-half of thelibraries (B) An endonuclease digestion protocol was also used for approximately one-half of the libraries Although the location of the junction sites ismore constrained in practice we obtained higher yields from this method

880 A Zimin et al

bias and complexity and to appraise previous estimates basedon flow cytometry

Figure 5 shows a histogram of k-mer counts in our hap-loid error-corrected reads computed using the program jel-lyfish (Marccedilais and Kingsford 2011) for k = 24 and k = 31Small values were chosen for tractability as well as goodseparation between the unique haploid k-mers and uncor-rected errors Each point in the plot corresponds to the num-ber of k-mers with a particular count for example thenumber of 31-mers that occur 50 times is plotted in red atx = 50 Note that we expect a k-mer that occurs just once inthe genome (ie is nonrepetitive) to occur C times on aver-age where C is depth of coverage For haploid data this plotwould ideally take the form of a single-mode Poisson distri-bution with mean equal to C (Lander and Waterman 1988)Deviations are attributable to other properties of the ge-nome and to systematic errors in library construction andsequencing Each sequencing error may create up to k sin-gle-copy k-mers producing a peak around x = 1 The num-ber of these k-mers have been greatly reduced in Figure 5 bythe QuORUM error-correction algorithm The long tail in theplot contains conserved high-copy-number repeats

We estimated the genome size as the ratio of the totalnumber of k-mers divided by the mean number of occur-rences of the unique k-mer counts (an estimate of theexpected depth of distinct sequences of length k) as shownin Figure 5 Table 2 gives these quantities as well as thegenome size estimates for k= 24 and k= 31 Both estimatesare slightly smaller than the value determined by flowcytometry (216 Gbp) (OrsquoBrien et al 1996) and are consis-tent with our final total contig length

Haploid sequence data reduction from readsto super- reads

The goal of the super-read algorithm is to reduce thequantity of sequence data presented to the overlap-basedassembler Each maximal super-read included in the down-stream assembly passed two critical tests successful fillingof the gap between a pair of reads and determination thatthe extended super-read was not properly contained withinanother super-read (maximal)

Paired-end gap fill The extension of a read by the super-read algorithm is done conservatively with the philosophy ofrelegating the important assembly decisions to the overlap-based assembly Hence read extension will halt when itcannot be done unambiguously For a pair of reads to resultin a super-read the algorithm needs to successfully fill in the

sequence of the gap (see Zimin et al 2013) Two propertiesof the sequenced genome play a large role in the success rateof gap filling The first is the number and size of repetitivesequences in the genome An unresolved repetitive sequencewill prevent filling the gap More repeats can be resolved byusing a longer k-mer size as explained above we chose k =79 for P taeda We avoided the second property of concerndivergence between the two parental chromosomes in a dip-loid genome by constructing all short-insert libraries fromDNA of a single haploid megagametophyte

As the gap size increases so does the likelihood ofencountering ambiguity making it more difficult to fill gapsfor longer fragments For our haploid read pairs the rate ofsuccessful gap filling was as high as 96 for overlappingGAIIx reads from our shortest library Over the range of gapsizes in our data we observed an approximately linear 6reduction in the rate of successful gap filling for every 100-bp increase in the expected gap size (Appendix B Table B1)This is consistent with a relatively low per-base probability(6 3 1024) that a problem is encountered during gapfilling over the range of gap sizes observed

Maximality Only the subset of ldquomaximalrdquo super-reads ispassed to the overlap-based assembler because fully containedreads would not provide additional contiguity informationThere is a trade-off in producing maximal super-reads Assum-ing that reads have the same length it is easier to fill the gapbetween pairs that come from shorter fragments But for li-braries of longer fragments although successful gap filling isless frequent more of the gap-filled pairs generate maximalsuper-reads We observed that the percentage of maximalsuper-reads uniformly increased with library fragment lengthdespite reduced gap-filling effectiveness (Appendix B TableB1) Thus the lower gap-filling rate for longer fragments didnot detract from their utility

We can use the rate of maximal super-read creation toestimate the marginal impact of additional paired-endsequencing To improve the assembly additional paired-end reads must be able to generate new maximal super-reads In our analysis we found that 24 of the read pairsin longer (500 bp) libraries produced additional maximalsuper-reads vs only 8ndash10 of the pairs in libraries shorterthan 300 bp (Appendix B Table B1) This suggests that themost effective way to improve the assembly further wouldbe to prioritize additional paired-end data from 500-bp orlonger fragment libraries

Overall paired-end data reductionMaSuRCArsquos error-correctionand super-reads processes reduced the 14 Tbp of raw paired-end read data (in 15 billion reads) to 52 Gbp in super-readsa 27-fold reduction The reduced data contained 150 millionmaximal super-reads with an average length of 362 bp equiv-alent to 243 coverage of the genome This 100-fold reductionin the number of paired-end reads is critical for the next step ofassembly which computes pairwise overlaps between thesereads along with the linking libraries

Table 2 Haploid (1N) genome size estimates

k-mer length

31 24Total k-mers 108 3 1012 116 3 1012

E(distinct k-mer depth) 5376 occurrences 5699 occurrencesGenome size (Gbp) 2012 2042

Sequencing and Assembling P taeda 881

Our sequencing strategy used the lowest-cost and mostaccurate datamdashthe reads from the HiSeq platformmdashto pro-vide the bulk of the k-mers required for error correction Wethen generated super-reads from all error-corrected readsincluding the longer reads from the GAIIx and MiSeq instru-ments As expected the GAIIx and MiSeq reads contributeda greater proportion of maximal super-reads The optimalmix of read lengths is difficult to determine and likely variesfor every genome

Incorporating diploid mate pair sequences

We obtained 1726 million paired reads from 48 mate pairlibraries with insert sizes of 1300ndash5500 bp (Table 2 andAppendix A Table A1) After filtering to remove reads thatfailed to match the haploid DNA 1156 million reads (69)remained Another 70 million paired reads that failed tocontain a biotin junction and 171 million paired readsidentified as duplicates were removed Finally we elimi-nated pairs in which either read was shorter than 64 bpthe minimum read length for the CABOG assembler Thefinal set of jumping reads contained 540 million reads(270 million pairs) which is 37-fold physical coverageof the genome (Appendix B Table B2) Notably there waslittle variation in the rate at which reads were filtered dueto error correction and haplotype differences as the li-brary length increased

Fosmid DiTags

Particularly useful for establishing long-range links in anassembly are paired reads from the ends of fosmid (Kimet al 1992) or BAC clones (Shizuya et al 1992) which spantens of kilobases Long-range paired-end data are even morecritical for second-generation sequencing projects (Schatz

et al 2010) where read lengths are significantly shorterthan they are for first-generation sequencing projects Recentreports demonstrate that mammalian genome assemblies con-taining high-quality fosmid DiTags can attain contiguity nearlyas well as traditional Sanger-based assemblies (Gnerre et al2011)

We generated 46 million pairs of DiTag reads (AppendixB Table B3) Read pairs from DiTag libraries are subject tothe same artifacts as those from mate pair libraries namelyduplicate molecules and nonjumping pairs Application ofthe aforementioned MaSuRCA filtering procedures for jump-ing libraries was applied and yielded 45 million distinctmapped read pairs for downstream overlap layout consensusassembly The largest reduction (47 of reads removed)was due to the removal of duplicate read pairs a result ofthe limited library complexity (Appendix B Table B3) Thefiltering statistics also indicated that the nick translationprotocol was the most efficient at generating distinctDiTag read pairs To obtain an unbiased estimate of thelength distribution of these libraries we incorporated a setof D melanogaster fosmids into select pools at 1 concen-tration prior to DiTag construction The median insert sizemeasured after removal of nonjumping and chimeric out-liers was 357 kbp Using a genome-size estimate of 22 Gbpfor loblolly pine this represents 733 physical coverage of thehaploid genome

While the inclusion of fosmid DiTag libraries was helpfulonly approximately one-third of the V10 MaSuRCA assem-bled genome scaffolds (by length) contained the two ormore DiTags required to make a long-range link This leadsus to conclude that deeper fosmid DiTag coverage couldsignificantly boost scaffold contiguity in future assemblies

Figure 4 Library complexity curves quan-tify library complexity and the diminishingreturns as sequencing progresses Librarycomplexity is an estimate of the numberof distinct molecules in the library (Daleyand Smith 2013) The convexly shapedcomplexity curve plots the number of dis-tinct molecules observed against thenumber of sequenced reads Only ourdeeply sequenced library exhibited dimin-ishing returns Libraries sequenced tolower depth are shown in the inset

882 A Zimin et al

Additional scaffolding procedures for final assembly

To produce the final assembly (version 101) from the outputof MaSuRCA (version 10) we employed two additionalstrategies that improved the length and accuracy of some ofthe scaffolds First we ran an independent WGS assemblyusing SOAPdenovo2 including its gap-closing module (Luoet al 2012) We used all haploid paired-end data for the contigassembly and added the mate pair and DiTag libraries for thescaffolding step only Following sequencing and quality con-trol the adapter sequence was removed from reads using cuta-dapt (Martin 2011) and the input data were filtered toremove sequences similar to the chloroplast reference se-quence (Parks et al 2009) After error correction 1144 billionreads were used for contig assembly and 1198 billion reads forscaffolding We used a k-mer length of 79 for contig assembly

Although the SOAPdenovo2 contigs are much smallerthan in the 10 assembly with an N50 size of just 687 bp thescaffolds are larger with an N50 size of 547 kbp Thereforewe ran the SOAPdenovo2 scaffolder again using the MaSuRCAscaffolds as input ldquocontigsrdquo and using a conservatively filteredset of mate pairs as linking information This produced a newset of 834 million scaffolds with an N50 size of 868 kbp sub-stantially larger than the original value

As discussed above the DiTag sequences prior to MaSURCAprocessing contained a moderate number of nonjunction readpairs (Appendix B Table B3) To improve the assembly fidelityduring this step we broke all newly created intrascaffold links

for which all supporting mates originated from a DiTag libraryThe resulting modified assembly had 144 million scaffoldswith an N50 size of 646 kbp still over twice as large as theoriginal scaffold N50 size

We used transcripts assembled de novo from RNA-seqreads and predicted to encode full-length proteins (see NationalCenter for Biotechnology Information BioProject 174450) toidentify scaffolds that could be joined or rearranged to becongruent with transcripts Because transcripts can be assem-bled erroneously we followed a conservative strategy that pri-marily linked together scaffolds that contained a single gene

We aligned the 87241 assembled transcripts to all scaffoldsin the modified assembly described above using the nucmerprogram in the MUMmer package (Kurtz et al 2004) andcollecting only those transcripts with at least two exactmatches of 40 bp or longer This yielded 68497 transcriptsmapping to 47492 different scaffolds

Alignments between transcripts and the WGS assemblywere checked for consistency looking for instances wherethe scaffold would require an inversion or a translocation tomake it align We found 1348 inconsistencies 2 of thetotal These could represent errors in either the de novotranscriptome assembly or the WGS assembly and they willbe investigated and corrected if necessary in future assemblyreleases

We scanned the alignments looking for transcripts thatspanned two distinct scaffolds For each transcript we sorted

Figure 5 Plot of k-mer counts in the haploid paired-end WGS sequence After counting all 24- and 31-mers in the QuORUM error-corrected reads weplotted the number of distinct k-mers (y-axis) that occur exactly X times (x-axis) For our haploid data each plot has a single primary peak at the X-valuecorresponding to the depth of coverage of the 1N genome

Sequencing and Assembling P taeda 883

the scaffolds aligned to it and created a directed link betweeneach pair of scaffolds The links were weighted using the sumof the alignment lengths If multiple transcripts aligned to thesame pair of scaffolds the link between them was given thesum of all the weights (This can happen when for exampletwo transcripts represent alternative splice variants of the samegene) We rebuilt the scaffolds using these new links in orderof priority from strongest (greatest weight) to weakest checkingfor circular links at each step and discarding them

This transcript-based scaffolding step linked together31231 scaffolds into 9170 larger scaffolds slightly increas-ing the N50 size and substantially improving the number oftranscripts that align to a single scaffold The final assemblyversion 101 has 144 million scaffolds spanning 232 GbpExcluding gaps the total size of all contigs is 2015 Gbp Thelargest scaffold is 8891046 bp The scaffold N50 size is66920 bp based on a 22 Gbp total genome size (Table 3)

Assessing assembly contiguity and completeness

We used the CEGMA pipeline (Parra et al 2007) to assessthe contiguity of both the V10 and V101 assemblies withrespect to a set of highly conserved eukaryotic genes CEGMAuses a set of conserved protein families from the Clusters ofOrthologous Groups database (Tatusov et al 2003) to an-notate a genome by constructing DNA-protein alignmentsbetween the proteins and the assembled scaffolds Of 248highly conserved protein families CEGMA found full-lengthalignments to the V10 assembly for 113 (45) Includingpartial alignments brings this total to 197 (79) In theV101 assembly CEGMA found 185 (75) full-length align-ments and 203 (82) full-length and partial alignmentsThere was little change in the total number of alignmentsbetween V10 and V101 However the fraction of all align-ments to the assembly that were full length increased from57 in the V10 assembly to 91 in the V101 assemblyfurther quantifying the biological utility of the additionalscaffolding phase There are a number of possible explanationsfor the discrepancy between the coverage and the observedrates of conserved gene annotations principally assemblyfragmentation and potential ascertainment bias in the de-termination of the gene set

Comparison of fosmid and whole-genome assemblies

For validation purposes we constructed sequenced andassembled a large pool of 4600 fosmid clones (Appendix A)The assembly contained 3798 contigs longer than 20000 bp

(50 of the fosmid insert in each case) which we used tocheck the completeness and quality of the WGS assembly Be-cause the fosmids were selected at random the amount ofsequence covered by the WGS assembly should provide anestimate of how much of the entire genome has been capturedin that assembly The 3798 contigs contain 109 Mbp or05of the entire genome

We aligned contigs to the WGS assembly (V10V101contigs) using MUMmer (Kurtz et al 2004) and found thatall but 4 of the 3798 contigs aligned across nearly theirentire length at an average identity of 99 Nearly all(9863) of the combined length of the fosmid pool contigsis covered by alignments to the WGS assembly The un-aligned fraction consists of the 4 contigs that failed to align(117681 bp) plus the unaligned portions of the remainingcontigs Assuming that the fosmids represent a random sam-ple this suggests that the WGS assembly covers 98 ofthe whole genome (with the reservation that hard-to-sequence regions might be missed by both the WGS andfosmid sequencing approaches and thus not counted in thisevaluation)

Fosmid haplotype comparisons

We expect that half of the fosmid contigs would correspondto the same haplotype as the aligned WGS contig Thuswhile many contigs should be close to 100 identical contigsfrom the alternative haplotype (untransmitted) would showa divergence rate corresponding to the differences betweenhaplotypes estimated to be 1ndash2 We considered the pos-sibility that the fosmid contigs would thus fall into two dis-tinct bins near-identical and 1ndash2 divergent Howeverrelatively few contigs showed this degree of divergence2120 contigs (56) were 995 or more identical and theothers matched the WGS contigs at a range of identities withthe vast majority 98 identical (Appendix A Figure A1)Only 189 contigs (5) were 95 identical with the lowestidentity at 91 This suggests that divergence between thetwo parental haplotypes is 1 on average with a smallnumber of highly divergent regions

Conclusions

The sequencing and assembly strategy described here ofthe largest genome to date resulted in a haploid assemblycomposed of 2015 billion base pairs By many measures itis the most contiguous and complete draft assembly ofa conifer genome (Appendix C) This project required a close

Table 3 Comparison of P taeda whole-genome assemblies before and after additional scaffolding

P taeda 10 (before) P taeda 101 (after)

Total sequence in contigs (bp) 20148103497 20148103497Total span of scaffolds (bp) 22564679219 23180477227N50 contig size (bp) 8206 8206N50 scaffold size (bp) 30681 66920No of contigs 500 bp 4047642 4047642No of scaffolds 500 bp 2319749 2158326

The contigs are the same for both assemblies N50 statistics use a genome size of 22 3 109

884 A Zimin et al

coupling between sequencing and assembly strategy and thedevelopment of new computational methods to address thechallenges inherent to the assembly of mega-genomes

Conifer genomes are filled with massive amounts ofrepetitive DNA mostly transposable elements (Nystedt et al2013 Wegrzyn et al 2013 2014) which might have pre-sented a major barrier to successful genome assembly Fortu-nately most of these repeats are relatively ancient so theiraccumulated sequence differences allowed the assembly algo-rithms to distinguish individual copies

A major concern in assembly of diploid genomes is thedegree of divergence between the two parental copies ofeach chromosome Even small differences between the chro-mosomes can cause an assembler to construct two distinctcontigs which can be indistinguishable from two-copy repeatsequences Conifers offer an elegant biological solution to thisproblem by producing megagametophytes with a single copyof each chromosome from which sufficient DNA can beextracted to cover the entire genome

While the current assembly represents an important land-mark and will serve as a powerful resource for biologicalanalyses it remains incomplete To further increase sequencecontiguity we plan to generate additional long-range pairedreads particularly DiTag pairs that should substantiallyincrease scaffold lengths Noting the success of the smallerfosmid pool assemblies presented here inexpensive sequenc-ing allows for the possibility of sequencing many more of thesepools and merging the long contigs from these assemblies intothe WGS assembly (Zhang et al 2012 Nystedt et al 2013) Inparallel with efforts to improve the next P taeda referencesequence we are gathering a large set of genetically mappedSNPs that will allow placement of scaffolds onto the 12 chro-mosomes providing additional concrete physical anchors tothe assembly Finally we note that ongoing efforts to expandthe diversity of conifer genome reference sequences will pro-vide a solid foundation for comparative genomics thus improv-ing the assemblies and their annotations

Acknowledgments

This work was supported in part by US Department ofAgricultureNational Institute of Food and Agriculture(2011-67009-30030) We wish to thank C Dana Nelson atthe USDA Forest Service Southern Research Station forproviding and verifying the genotype of target tree material

Note added in proof See Wegrzyn et al 2014 (pp891ndash909) in this issue for a related work

Literature Cited

Ahuja M R and D B Neale 2005 Evolution of genome size inconifers Silvae Genet 54 126ndash137

Bai C W S Alverson A Follansbee and D M Waller 2012 Newreports of nuclear DNA content for 407 vascular plant taxa fromthe United States Ann Bot (Lond) 110 1623ndash1629

Bierhorst D W 1971 Morphology of Vascular Plants MacmillanNew York

Birol I A Raymond S D Jackman S Pleasance R Coope et al2013 Assembling the 20 Gb white spruce (Picea glauca) ge-nome from whole-genome shotgun sequencing data Bioinfor-matics 29 1492ndash1497

Bowe L M G Coat and C W Depamphilis 2000 Phylogeny ofseed plants based on all three genomic compartments extantgymnosperms are monophyletic and Gnetalesrsquo closest relativesare conifers Proc Natl Acad Sci USA 97 4092ndash4097

Daley T and A D Smith 2013 Predicting the molecular com-plexity of sequencing libraries Nat Methods 10 325ndash327

Frederick W J S J Lien C E Courchene N A DeMartini A JRagauskas et al 2008 Production of ethanol from carbohy-drates from loblolly pine a technical and economic assessmentBioresour Technol 99 5051ndash5057

Fuchs J G Jovtchev and I Schubert 2008 The chromosomaldistribution of histone methylation marks in gymnosperms dif-fers from that of angiosperms Chromosome Res 16 891ndash898

Gnerre S I MacCallum D Przybylski F J Ribeiro J N Burtonet al 2011 High-quality draft assemblies of mammalian ge-nomes from massively parallel sequence data Proc Natl AcadSci USA 108(4) 1513ndash1518

Kim U-J H Shizuya P J de Jong B Barren and M I Simon1992 Stable propagation of cosmid-sized human DNA insertsin an F factor based vector Nucleic Acids Res 20 1083ndash1085

Kovach A J L Wegrzyn G Parra C Holt G E Bruening et al2010 The Pinus taeda genome is characterized by diverse andhighly diverged repetitive sequences BMC Genomics 11(1) 420

Kurtz S A Phillippy A L Delcher M Smoot M Shumway et al2004 Versatile and open software for comparing large ge-nomes Genome Biol 5(2) R12

Lander E S and M S Waterman 1988 Genomic mapping byfingerprinting random clones a mathematical analysis Ge-nomics 2(3) 231ndash239

Li R H Zhu J Ruan W Qian X Fang et al 2010 De novoassembly of human genomes with massively parallel short readsequencing Genome Res 20(2) 265ndash272

Luo R B Liu Y Xie Z Li W Huang et al 2012 SOAPdenovo2an empirically improved memory-efficient short-read de novoassembler GigaScience DOI1011862047-217X-1-18

Mackay J J F Dean C Plomion D G Peterson F M Caacutenovaset al 2012 Towards decoding the conifer giga-genome PlantMol Biol 80 555ndash569

Magallon S and M J Sanderson 2005 Angiosperm divergencetimes the effects of genes codon positions and time con-straints Evolution 59 1653ndash1670

Marccedilais G and C Kingsford 2011 A fast lock-free approach forefficient parallel counting of occurrences of k-mers Bioinfor-matics 27(6) 764ndash770

Marcais G J Yorke and A Zimin 2013 QuorUM an error cor-rector for Illumina reads arXiv13073515 [q-bioGN]

Martin M 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet J 17(1) 10

McKeand S T Mullin T Byram and T White 2003 Deploymentof genetically improved loblolly and slash pines in the south J For101(3) 32ndash37

Miller J R A L Delcher S Koren E Venter B P Walenz et al2008 Aggressive assembly of pyrosequencing reads withmates Bioinformatics 24(24) 2818ndash2824

Mirov N T 1967 The Genus Pinus Ronald Press New YorkMorse A M D G Peterson M N Islam-Faridi K E Smith Z

Magbanua et al 2009 Evolution of genome size and complex-ity in Pinus PLoS ONE 4(2) e4332

Neale D B and A Kremer 2011 Forest tree genomics growingresources and applications Nat Rev Genet 12(2) 111ndash122

Sequencing and Assembling P taeda 885

Estimating the haploid genome size from haploidk-mer data

Recall that a k-mer is a contiguous sequence of length kthus a read of length L contains L ndash k +1 k-mers We com-puted k-mer histograms of haploid sequence data using theprogram jellyfish (Marcais and Kingsford 2011) To reducethe effect of low-quality data we used the QuORUM error-corrected reads (below) To avoid quantization error thetotal count for every distinct k-mer was computed indepen-dently of the histogram

The genome size was estimated as the total number ofk-mers divided by the expected depth or multiplicity ofdistinct k-mers in the data The approach used to estimatethe latter quantity is to focus on the range of the distributionin which most k-mers are unique and substitute an estimateof the expected depth of a unique k-mer instead Uponinspecting our empirical data we determined that k-mersoccurring 15 or fewer times were mostly errors We alsoconsidered k-mers observed 120 times (approximatelytwice the most common multiplicities of unique k-mers) tobe repeats We then determined the expected value of thek-mer depth distribution from a cubic interpolating spline fitto the data using MATLAB fit after outliers had been re-moved This gave a slightly smaller estimate of the expectedk-mer depth than the more typical approach of using themaximum (mode)

Whole-genome shotgun assembly with MaSuRCA

Sequence preprocessing and error correction TheMaSuRCA assembly pipeline first corrected errors in theIllumina reads using QuORUM (Marcais et al 2013)a method that uses k-mer frequencies and quality scoresto correct errors A notable aspect of this was that the list ofldquogoodrdquo 24-mers used for error correction was compiled onlyfrom the haploid paired-end data During the process oferror correction SNPs between the haplotypes are indistin-guishable from error A fraction of SNPs in the reads from thediploid mate pair and DiTag libraries was changed to matchour target megagametophyte haplotype When larger differ-ences were present such as insertions or deletions the errorcorrector deleted these reads In addition the set of 24-mersbelonging to Illumina adapters chloroplast and mitochon-drial DNA were identified and added to a special QuORUMldquocontaminantrdquo list QuORUM truncated the read if it encoun-tered a contaminant 24-mer The trimming of mate pair andDiTag reads to the junction site was also accomplished duringthis phase

Optimized super-read construction For the super-readreduction the MaSuRCA assembler utilized k-mers fromthe error corrected paired-end reads as a summary of theunderlying haploid genome The k-mer length was opti-mized to maximize the number of distinct k-mers utilized bythe super-read reduction (see Zimin et al 2013 for details)If k is too small the number of distinct k-mers will decreasedue to short repeats Conversely if k is too large distinct

regions of the genome may be missing due to low coverageA grid search to find the optimal value was performed be-tween k = 70 and k = 85 From this we inferred an optimalvalue for the number of utilized k-mers at k = 79 Thisbecame the chosen k-mer length

We then applied the super-reads reduction using a data-base of 79-mers to the deep-coverage paired-end data fromthe megagametophyte This produced a set of super-readsbased strictly on the haploid DNA We discuss in detail theoutcome of this phase in the Results

Assembly with diploid reads Because the long-insert librarieswere constructed from diploid pine needles half of thesefragments on average derived from a different parental genomethan the haploid megagametophyte Many of these pairscontained differences that distinguished them from the haploidDNA Although most of the reads with haplotype differenceswere corrected or trimmed some remained after error correc-tion To reduce the number of scaffolding conflicts caused byheterozygosity we required that all 57-mers in both reads ina linking pair were present in the haploid paired-end data

Figure 2 An overview of the pFosDT54 fosmid cloning vector used forDiTag creation The cloning site (where the genomic DNA is inserted) isflanked by forward and reverse Illumina primers to enable end sequenc-ing Two NbBbvCl nicking endonuclease sites are located 59 of the Illu-mina primers on both strands to allow for creation of DiTags by a nicktranslation method The vector backbone has had all FspBl and Csp6l sitesremoved to allow for creation of DiTags by endonuclease digestion

Sequencing and Assembling P taeda 879

otherwise the read pair was rejected This step should haveretained all pairs from the same haplotype

We also implemented a preprocessing step to removeapparently duplicate molecules from the long-insert libraries Thisstep is critical for long-insert libraries because of their reducedlibrary complexity By filtering these out we ensure that eachlink inferred from long-insert reads is independently ascertained

We discuss details of coverage and filtering results forboth types of long-insert linking libraries in the Results Thefiltered linking reads were combined with super-reads andassembled using an adapted version of CABOG

The MaSuRCA assembler was run on a 64-core IA64computer with 1 terabyte of RAM Running the assemblypipeline took 3 months The maximum memory usage duringthe assembly reached 800 GB during the super-reads step Theoutput of MaSuRCA was the 10 version of the assembly (seeTable 3)

Results and Discussion

Haploid megagametophyte library complexity

The complexity of a library is typically measured as thenumber of distinct molecules in the library (Daley and Smith2013) For the library construction protocols described herethis is less than the total number of molecules in the librarydue to a PCR amplification step as well as in vivo amplifi-cation for fosmid clone-based libraries

Library complexity curves which plot the number ofdistinct molecules against the number of molecules se-quenced were used to characterize the rate of diminishingreturns because a library of finite complexity is sequenced

more deeply We constructed these curves on QuORUMerror-corrected data as follows We represented each mol-ecule by a concatenation of a short prefix and suffix oflength k giving a sequence or tag of length 2k For strandconsistency the prefix was obtained from the first read andthe suffix was obtained from the reverse complemented sec-ond read We chose the parameter k to be large enough(here k = 24) so that the tags were typically unique Twotags were identified as the same molecule if their sequencesmatched exactly in the same or opposite orientations Fromthe tags we constructed a histogram of molecular frequencyin the sequenced sample which we sampled without re-placement to create the complexity curve

Figure 4 plots the number of distinct read pairs againstthe total number of sequenced pairs The shape of the curveindicates how fast the libraryrsquos complexity is being depletedie how the likelihood of a duplicate increases as sequenc-ing continues For our most deeply sequenced library Figure4 shows the limits of paired-end library complexity whenconstructed from a megagametophyte For our deeply se-quenced library the curve shows that additional sequencingwould sample new molecules at a rate of 40 It alsoindicates that for most of our libraries deeper sequencingwould yield additional useful data When considered withthe partition scheme described previously we see that ourmegagametophyte WGS protocol is a valuable method forobtaining highly broad and deep paired-end sequence datatypes for de novo assembly

Haploid k-mers and an estimate of the 1N genome size

Using short k-mers from all the reads we estimated the genomesize to provide an assessment of the overall paired-end library

Figure 3 Schematic for our two methods for converting a fosmid library into an Illumina compatible DiTag library using the fosmid vector created forthis project (A) A nick translation approach similar to the approach used in Williams et al (2012) was implemented for approximately one-half of thelibraries (B) An endonuclease digestion protocol was also used for approximately one-half of the libraries Although the location of the junction sites ismore constrained in practice we obtained higher yields from this method

880 A Zimin et al

bias and complexity and to appraise previous estimates basedon flow cytometry

Figure 5 shows a histogram of k-mer counts in our hap-loid error-corrected reads computed using the program jel-lyfish (Marccedilais and Kingsford 2011) for k = 24 and k = 31Small values were chosen for tractability as well as goodseparation between the unique haploid k-mers and uncor-rected errors Each point in the plot corresponds to the num-ber of k-mers with a particular count for example thenumber of 31-mers that occur 50 times is plotted in red atx = 50 Note that we expect a k-mer that occurs just once inthe genome (ie is nonrepetitive) to occur C times on aver-age where C is depth of coverage For haploid data this plotwould ideally take the form of a single-mode Poisson distri-bution with mean equal to C (Lander and Waterman 1988)Deviations are attributable to other properties of the ge-nome and to systematic errors in library construction andsequencing Each sequencing error may create up to k sin-gle-copy k-mers producing a peak around x = 1 The num-ber of these k-mers have been greatly reduced in Figure 5 bythe QuORUM error-correction algorithm The long tail in theplot contains conserved high-copy-number repeats

We estimated the genome size as the ratio of the totalnumber of k-mers divided by the mean number of occur-rences of the unique k-mer counts (an estimate of theexpected depth of distinct sequences of length k) as shownin Figure 5 Table 2 gives these quantities as well as thegenome size estimates for k= 24 and k= 31 Both estimatesare slightly smaller than the value determined by flowcytometry (216 Gbp) (OrsquoBrien et al 1996) and are consis-tent with our final total contig length

Haploid sequence data reduction from readsto super- reads

The goal of the super-read algorithm is to reduce thequantity of sequence data presented to the overlap-basedassembler Each maximal super-read included in the down-stream assembly passed two critical tests successful fillingof the gap between a pair of reads and determination thatthe extended super-read was not properly contained withinanother super-read (maximal)

Paired-end gap fill The extension of a read by the super-read algorithm is done conservatively with the philosophy ofrelegating the important assembly decisions to the overlap-based assembly Hence read extension will halt when itcannot be done unambiguously For a pair of reads to resultin a super-read the algorithm needs to successfully fill in the

sequence of the gap (see Zimin et al 2013) Two propertiesof the sequenced genome play a large role in the success rateof gap filling The first is the number and size of repetitivesequences in the genome An unresolved repetitive sequencewill prevent filling the gap More repeats can be resolved byusing a longer k-mer size as explained above we chose k =79 for P taeda We avoided the second property of concerndivergence between the two parental chromosomes in a dip-loid genome by constructing all short-insert libraries fromDNA of a single haploid megagametophyte

As the gap size increases so does the likelihood ofencountering ambiguity making it more difficult to fill gapsfor longer fragments For our haploid read pairs the rate ofsuccessful gap filling was as high as 96 for overlappingGAIIx reads from our shortest library Over the range of gapsizes in our data we observed an approximately linear 6reduction in the rate of successful gap filling for every 100-bp increase in the expected gap size (Appendix B Table B1)This is consistent with a relatively low per-base probability(6 3 1024) that a problem is encountered during gapfilling over the range of gap sizes observed

Maximality Only the subset of ldquomaximalrdquo super-reads ispassed to the overlap-based assembler because fully containedreads would not provide additional contiguity informationThere is a trade-off in producing maximal super-reads Assum-ing that reads have the same length it is easier to fill the gapbetween pairs that come from shorter fragments But for li-braries of longer fragments although successful gap filling isless frequent more of the gap-filled pairs generate maximalsuper-reads We observed that the percentage of maximalsuper-reads uniformly increased with library fragment lengthdespite reduced gap-filling effectiveness (Appendix B TableB1) Thus the lower gap-filling rate for longer fragments didnot detract from their utility

We can use the rate of maximal super-read creation toestimate the marginal impact of additional paired-endsequencing To improve the assembly additional paired-end reads must be able to generate new maximal super-reads In our analysis we found that 24 of the read pairsin longer (500 bp) libraries produced additional maximalsuper-reads vs only 8ndash10 of the pairs in libraries shorterthan 300 bp (Appendix B Table B1) This suggests that themost effective way to improve the assembly further wouldbe to prioritize additional paired-end data from 500-bp orlonger fragment libraries

Overall paired-end data reductionMaSuRCArsquos error-correctionand super-reads processes reduced the 14 Tbp of raw paired-end read data (in 15 billion reads) to 52 Gbp in super-readsa 27-fold reduction The reduced data contained 150 millionmaximal super-reads with an average length of 362 bp equiv-alent to 243 coverage of the genome This 100-fold reductionin the number of paired-end reads is critical for the next step ofassembly which computes pairwise overlaps between thesereads along with the linking libraries

Table 2 Haploid (1N) genome size estimates

k-mer length

31 24Total k-mers 108 3 1012 116 3 1012

E(distinct k-mer depth) 5376 occurrences 5699 occurrencesGenome size (Gbp) 2012 2042

Sequencing and Assembling P taeda 881

Our sequencing strategy used the lowest-cost and mostaccurate datamdashthe reads from the HiSeq platformmdashto pro-vide the bulk of the k-mers required for error correction Wethen generated super-reads from all error-corrected readsincluding the longer reads from the GAIIx and MiSeq instru-ments As expected the GAIIx and MiSeq reads contributeda greater proportion of maximal super-reads The optimalmix of read lengths is difficult to determine and likely variesfor every genome

Incorporating diploid mate pair sequences

We obtained 1726 million paired reads from 48 mate pairlibraries with insert sizes of 1300ndash5500 bp (Table 2 andAppendix A Table A1) After filtering to remove reads thatfailed to match the haploid DNA 1156 million reads (69)remained Another 70 million paired reads that failed tocontain a biotin junction and 171 million paired readsidentified as duplicates were removed Finally we elimi-nated pairs in which either read was shorter than 64 bpthe minimum read length for the CABOG assembler Thefinal set of jumping reads contained 540 million reads(270 million pairs) which is 37-fold physical coverageof the genome (Appendix B Table B2) Notably there waslittle variation in the rate at which reads were filtered dueto error correction and haplotype differences as the li-brary length increased

Fosmid DiTags

Particularly useful for establishing long-range links in anassembly are paired reads from the ends of fosmid (Kimet al 1992) or BAC clones (Shizuya et al 1992) which spantens of kilobases Long-range paired-end data are even morecritical for second-generation sequencing projects (Schatz

et al 2010) where read lengths are significantly shorterthan they are for first-generation sequencing projects Recentreports demonstrate that mammalian genome assemblies con-taining high-quality fosmid DiTags can attain contiguity nearlyas well as traditional Sanger-based assemblies (Gnerre et al2011)

We generated 46 million pairs of DiTag reads (AppendixB Table B3) Read pairs from DiTag libraries are subject tothe same artifacts as those from mate pair libraries namelyduplicate molecules and nonjumping pairs Application ofthe aforementioned MaSuRCA filtering procedures for jump-ing libraries was applied and yielded 45 million distinctmapped read pairs for downstream overlap layout consensusassembly The largest reduction (47 of reads removed)was due to the removal of duplicate read pairs a result ofthe limited library complexity (Appendix B Table B3) Thefiltering statistics also indicated that the nick translationprotocol was the most efficient at generating distinctDiTag read pairs To obtain an unbiased estimate of thelength distribution of these libraries we incorporated a setof D melanogaster fosmids into select pools at 1 concen-tration prior to DiTag construction The median insert sizemeasured after removal of nonjumping and chimeric out-liers was 357 kbp Using a genome-size estimate of 22 Gbpfor loblolly pine this represents 733 physical coverage of thehaploid genome

While the inclusion of fosmid DiTag libraries was helpfulonly approximately one-third of the V10 MaSuRCA assem-bled genome scaffolds (by length) contained the two ormore DiTags required to make a long-range link This leadsus to conclude that deeper fosmid DiTag coverage couldsignificantly boost scaffold contiguity in future assemblies

Figure 4 Library complexity curves quan-tify library complexity and the diminishingreturns as sequencing progresses Librarycomplexity is an estimate of the numberof distinct molecules in the library (Daleyand Smith 2013) The convexly shapedcomplexity curve plots the number of dis-tinct molecules observed against thenumber of sequenced reads Only ourdeeply sequenced library exhibited dimin-ishing returns Libraries sequenced tolower depth are shown in the inset

882 A Zimin et al

Additional scaffolding procedures for final assembly

To produce the final assembly (version 101) from the outputof MaSuRCA (version 10) we employed two additionalstrategies that improved the length and accuracy of some ofthe scaffolds First we ran an independent WGS assemblyusing SOAPdenovo2 including its gap-closing module (Luoet al 2012) We used all haploid paired-end data for the contigassembly and added the mate pair and DiTag libraries for thescaffolding step only Following sequencing and quality con-trol the adapter sequence was removed from reads using cuta-dapt (Martin 2011) and the input data were filtered toremove sequences similar to the chloroplast reference se-quence (Parks et al 2009) After error correction 1144 billionreads were used for contig assembly and 1198 billion reads forscaffolding We used a k-mer length of 79 for contig assembly

Although the SOAPdenovo2 contigs are much smallerthan in the 10 assembly with an N50 size of just 687 bp thescaffolds are larger with an N50 size of 547 kbp Thereforewe ran the SOAPdenovo2 scaffolder again using the MaSuRCAscaffolds as input ldquocontigsrdquo and using a conservatively filteredset of mate pairs as linking information This produced a newset of 834 million scaffolds with an N50 size of 868 kbp sub-stantially larger than the original value

As discussed above the DiTag sequences prior to MaSURCAprocessing contained a moderate number of nonjunction readpairs (Appendix B Table B3) To improve the assembly fidelityduring this step we broke all newly created intrascaffold links

for which all supporting mates originated from a DiTag libraryThe resulting modified assembly had 144 million scaffoldswith an N50 size of 646 kbp still over twice as large as theoriginal scaffold N50 size

We used transcripts assembled de novo from RNA-seqreads and predicted to encode full-length proteins (see NationalCenter for Biotechnology Information BioProject 174450) toidentify scaffolds that could be joined or rearranged to becongruent with transcripts Because transcripts can be assem-bled erroneously we followed a conservative strategy that pri-marily linked together scaffolds that contained a single gene

We aligned the 87241 assembled transcripts to all scaffoldsin the modified assembly described above using the nucmerprogram in the MUMmer package (Kurtz et al 2004) andcollecting only those transcripts with at least two exactmatches of 40 bp or longer This yielded 68497 transcriptsmapping to 47492 different scaffolds

Alignments between transcripts and the WGS assemblywere checked for consistency looking for instances wherethe scaffold would require an inversion or a translocation tomake it align We found 1348 inconsistencies 2 of thetotal These could represent errors in either the de novotranscriptome assembly or the WGS assembly and they willbe investigated and corrected if necessary in future assemblyreleases

We scanned the alignments looking for transcripts thatspanned two distinct scaffolds For each transcript we sorted

Figure 5 Plot of k-mer counts in the haploid paired-end WGS sequence After counting all 24- and 31-mers in the QuORUM error-corrected reads weplotted the number of distinct k-mers (y-axis) that occur exactly X times (x-axis) For our haploid data each plot has a single primary peak at the X-valuecorresponding to the depth of coverage of the 1N genome

Sequencing and Assembling P taeda 883

the scaffolds aligned to it and created a directed link betweeneach pair of scaffolds The links were weighted using the sumof the alignment lengths If multiple transcripts aligned to thesame pair of scaffolds the link between them was given thesum of all the weights (This can happen when for exampletwo transcripts represent alternative splice variants of the samegene) We rebuilt the scaffolds using these new links in orderof priority from strongest (greatest weight) to weakest checkingfor circular links at each step and discarding them

This transcript-based scaffolding step linked together31231 scaffolds into 9170 larger scaffolds slightly increas-ing the N50 size and substantially improving the number oftranscripts that align to a single scaffold The final assemblyversion 101 has 144 million scaffolds spanning 232 GbpExcluding gaps the total size of all contigs is 2015 Gbp Thelargest scaffold is 8891046 bp The scaffold N50 size is66920 bp based on a 22 Gbp total genome size (Table 3)

Assessing assembly contiguity and completeness

We used the CEGMA pipeline (Parra et al 2007) to assessthe contiguity of both the V10 and V101 assemblies withrespect to a set of highly conserved eukaryotic genes CEGMAuses a set of conserved protein families from the Clusters ofOrthologous Groups database (Tatusov et al 2003) to an-notate a genome by constructing DNA-protein alignmentsbetween the proteins and the assembled scaffolds Of 248highly conserved protein families CEGMA found full-lengthalignments to the V10 assembly for 113 (45) Includingpartial alignments brings this total to 197 (79) In theV101 assembly CEGMA found 185 (75) full-length align-ments and 203 (82) full-length and partial alignmentsThere was little change in the total number of alignmentsbetween V10 and V101 However the fraction of all align-ments to the assembly that were full length increased from57 in the V10 assembly to 91 in the V101 assemblyfurther quantifying the biological utility of the additionalscaffolding phase There are a number of possible explanationsfor the discrepancy between the coverage and the observedrates of conserved gene annotations principally assemblyfragmentation and potential ascertainment bias in the de-termination of the gene set

Comparison of fosmid and whole-genome assemblies

For validation purposes we constructed sequenced andassembled a large pool of 4600 fosmid clones (Appendix A)The assembly contained 3798 contigs longer than 20000 bp

(50 of the fosmid insert in each case) which we used tocheck the completeness and quality of the WGS assembly Be-cause the fosmids were selected at random the amount ofsequence covered by the WGS assembly should provide anestimate of how much of the entire genome has been capturedin that assembly The 3798 contigs contain 109 Mbp or05of the entire genome

We aligned contigs to the WGS assembly (V10V101contigs) using MUMmer (Kurtz et al 2004) and found thatall but 4 of the 3798 contigs aligned across nearly theirentire length at an average identity of 99 Nearly all(9863) of the combined length of the fosmid pool contigsis covered by alignments to the WGS assembly The un-aligned fraction consists of the 4 contigs that failed to align(117681 bp) plus the unaligned portions of the remainingcontigs Assuming that the fosmids represent a random sam-ple this suggests that the WGS assembly covers 98 ofthe whole genome (with the reservation that hard-to-sequence regions might be missed by both the WGS andfosmid sequencing approaches and thus not counted in thisevaluation)

Fosmid haplotype comparisons

We expect that half of the fosmid contigs would correspondto the same haplotype as the aligned WGS contig Thuswhile many contigs should be close to 100 identical contigsfrom the alternative haplotype (untransmitted) would showa divergence rate corresponding to the differences betweenhaplotypes estimated to be 1ndash2 We considered the pos-sibility that the fosmid contigs would thus fall into two dis-tinct bins near-identical and 1ndash2 divergent Howeverrelatively few contigs showed this degree of divergence2120 contigs (56) were 995 or more identical and theothers matched the WGS contigs at a range of identities withthe vast majority 98 identical (Appendix A Figure A1)Only 189 contigs (5) were 95 identical with the lowestidentity at 91 This suggests that divergence between thetwo parental haplotypes is 1 on average with a smallnumber of highly divergent regions

Conclusions

The sequencing and assembly strategy described here ofthe largest genome to date resulted in a haploid assemblycomposed of 2015 billion base pairs By many measures itis the most contiguous and complete draft assembly ofa conifer genome (Appendix C) This project required a close

Table 3 Comparison of P taeda whole-genome assemblies before and after additional scaffolding

P taeda 10 (before) P taeda 101 (after)

Total sequence in contigs (bp) 20148103497 20148103497Total span of scaffolds (bp) 22564679219 23180477227N50 contig size (bp) 8206 8206N50 scaffold size (bp) 30681 66920No of contigs 500 bp 4047642 4047642No of scaffolds 500 bp 2319749 2158326

The contigs are the same for both assemblies N50 statistics use a genome size of 22 3 109

884 A Zimin et al

coupling between sequencing and assembly strategy and thedevelopment of new computational methods to address thechallenges inherent to the assembly of mega-genomes

Conifer genomes are filled with massive amounts ofrepetitive DNA mostly transposable elements (Nystedt et al2013 Wegrzyn et al 2013 2014) which might have pre-sented a major barrier to successful genome assembly Fortu-nately most of these repeats are relatively ancient so theiraccumulated sequence differences allowed the assembly algo-rithms to distinguish individual copies

A major concern in assembly of diploid genomes is thedegree of divergence between the two parental copies ofeach chromosome Even small differences between the chro-mosomes can cause an assembler to construct two distinctcontigs which can be indistinguishable from two-copy repeatsequences Conifers offer an elegant biological solution to thisproblem by producing megagametophytes with a single copyof each chromosome from which sufficient DNA can beextracted to cover the entire genome

While the current assembly represents an important land-mark and will serve as a powerful resource for biologicalanalyses it remains incomplete To further increase sequencecontiguity we plan to generate additional long-range pairedreads particularly DiTag pairs that should substantiallyincrease scaffold lengths Noting the success of the smallerfosmid pool assemblies presented here inexpensive sequenc-ing allows for the possibility of sequencing many more of thesepools and merging the long contigs from these assemblies intothe WGS assembly (Zhang et al 2012 Nystedt et al 2013) Inparallel with efforts to improve the next P taeda referencesequence we are gathering a large set of genetically mappedSNPs that will allow placement of scaffolds onto the 12 chro-mosomes providing additional concrete physical anchors tothe assembly Finally we note that ongoing efforts to expandthe diversity of conifer genome reference sequences will pro-vide a solid foundation for comparative genomics thus improv-ing the assemblies and their annotations

Acknowledgments

This work was supported in part by US Department ofAgricultureNational Institute of Food and Agriculture(2011-67009-30030) We wish to thank C Dana Nelson atthe USDA Forest Service Southern Research Station forproviding and verifying the genotype of target tree material

Note added in proof See Wegrzyn et al 2014 (pp891ndash909) in this issue for a related work

Literature Cited

Ahuja M R and D B Neale 2005 Evolution of genome size inconifers Silvae Genet 54 126ndash137

Bai C W S Alverson A Follansbee and D M Waller 2012 Newreports of nuclear DNA content for 407 vascular plant taxa fromthe United States Ann Bot (Lond) 110 1623ndash1629

Bierhorst D W 1971 Morphology of Vascular Plants MacmillanNew York

Birol I A Raymond S D Jackman S Pleasance R Coope et al2013 Assembling the 20 Gb white spruce (Picea glauca) ge-nome from whole-genome shotgun sequencing data Bioinfor-matics 29 1492ndash1497

Bowe L M G Coat and C W Depamphilis 2000 Phylogeny ofseed plants based on all three genomic compartments extantgymnosperms are monophyletic and Gnetalesrsquo closest relativesare conifers Proc Natl Acad Sci USA 97 4092ndash4097

Daley T and A D Smith 2013 Predicting the molecular com-plexity of sequencing libraries Nat Methods 10 325ndash327

Frederick W J S J Lien C E Courchene N A DeMartini A JRagauskas et al 2008 Production of ethanol from carbohy-drates from loblolly pine a technical and economic assessmentBioresour Technol 99 5051ndash5057

Fuchs J G Jovtchev and I Schubert 2008 The chromosomaldistribution of histone methylation marks in gymnosperms dif-fers from that of angiosperms Chromosome Res 16 891ndash898

Gnerre S I MacCallum D Przybylski F J Ribeiro J N Burtonet al 2011 High-quality draft assemblies of mammalian ge-nomes from massively parallel sequence data Proc Natl AcadSci USA 108(4) 1513ndash1518

Kim U-J H Shizuya P J de Jong B Barren and M I Simon1992 Stable propagation of cosmid-sized human DNA insertsin an F factor based vector Nucleic Acids Res 20 1083ndash1085

Kovach A J L Wegrzyn G Parra C Holt G E Bruening et al2010 The Pinus taeda genome is characterized by diverse andhighly diverged repetitive sequences BMC Genomics 11(1) 420

Kurtz S A Phillippy A L Delcher M Smoot M Shumway et al2004 Versatile and open software for comparing large ge-nomes Genome Biol 5(2) R12

Lander E S and M S Waterman 1988 Genomic mapping byfingerprinting random clones a mathematical analysis Ge-nomics 2(3) 231ndash239

Li R H Zhu J Ruan W Qian X Fang et al 2010 De novoassembly of human genomes with massively parallel short readsequencing Genome Res 20(2) 265ndash272

Luo R B Liu Y Xie Z Li W Huang et al 2012 SOAPdenovo2an empirically improved memory-efficient short-read de novoassembler GigaScience DOI1011862047-217X-1-18

Mackay J J F Dean C Plomion D G Peterson F M Caacutenovaset al 2012 Towards decoding the conifer giga-genome PlantMol Biol 80 555ndash569

Magallon S and M J Sanderson 2005 Angiosperm divergencetimes the effects of genes codon positions and time con-straints Evolution 59 1653ndash1670

Marccedilais G and C Kingsford 2011 A fast lock-free approach forefficient parallel counting of occurrences of k-mers Bioinfor-matics 27(6) 764ndash770

Marcais G J Yorke and A Zimin 2013 QuorUM an error cor-rector for Illumina reads arXiv13073515 [q-bioGN]

Martin M 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet J 17(1) 10

McKeand S T Mullin T Byram and T White 2003 Deploymentof genetically improved loblolly and slash pines in the south J For101(3) 32ndash37

Miller J R A L Delcher S Koren E Venter B P Walenz et al2008 Aggressive assembly of pyrosequencing reads withmates Bioinformatics 24(24) 2818ndash2824

Mirov N T 1967 The Genus Pinus Ronald Press New YorkMorse A M D G Peterson M N Islam-Faridi K E Smith Z

Magbanua et al 2009 Evolution of genome size and complex-ity in Pinus PLoS ONE 4(2) e4332

Neale D B and A Kremer 2011 Forest tree genomics growingresources and applications Nat Rev Genet 12(2) 111ndash122

Sequencing and Assembling P taeda 885

otherwise the read pair was rejected This step should haveretained all pairs from the same haplotype

We also implemented a preprocessing step to removeapparently duplicate molecules from the long-insert libraries Thisstep is critical for long-insert libraries because of their reducedlibrary complexity By filtering these out we ensure that eachlink inferred from long-insert reads is independently ascertained

We discuss details of coverage and filtering results forboth types of long-insert linking libraries in the Results Thefiltered linking reads were combined with super-reads andassembled using an adapted version of CABOG

The MaSuRCA assembler was run on a 64-core IA64computer with 1 terabyte of RAM Running the assemblypipeline took 3 months The maximum memory usage duringthe assembly reached 800 GB during the super-reads step Theoutput of MaSuRCA was the 10 version of the assembly (seeTable 3)

Results and Discussion

Haploid megagametophyte library complexity

The complexity of a library is typically measured as thenumber of distinct molecules in the library (Daley and Smith2013) For the library construction protocols described herethis is less than the total number of molecules in the librarydue to a PCR amplification step as well as in vivo amplifi-cation for fosmid clone-based libraries

Library complexity curves which plot the number ofdistinct molecules against the number of molecules se-quenced were used to characterize the rate of diminishingreturns because a library of finite complexity is sequenced

more deeply We constructed these curves on QuORUMerror-corrected data as follows We represented each mol-ecule by a concatenation of a short prefix and suffix oflength k giving a sequence or tag of length 2k For strandconsistency the prefix was obtained from the first read andthe suffix was obtained from the reverse complemented sec-ond read We chose the parameter k to be large enough(here k = 24) so that the tags were typically unique Twotags were identified as the same molecule if their sequencesmatched exactly in the same or opposite orientations Fromthe tags we constructed a histogram of molecular frequencyin the sequenced sample which we sampled without re-placement to create the complexity curve

Figure 4 plots the number of distinct read pairs againstthe total number of sequenced pairs The shape of the curveindicates how fast the libraryrsquos complexity is being depletedie how the likelihood of a duplicate increases as sequenc-ing continues For our most deeply sequenced library Figure4 shows the limits of paired-end library complexity whenconstructed from a megagametophyte For our deeply se-quenced library the curve shows that additional sequencingwould sample new molecules at a rate of 40 It alsoindicates that for most of our libraries deeper sequencingwould yield additional useful data When considered withthe partition scheme described previously we see that ourmegagametophyte WGS protocol is a valuable method forobtaining highly broad and deep paired-end sequence datatypes for de novo assembly

Haploid k-mers and an estimate of the 1N genome size

Using short k-mers from all the reads we estimated the genomesize to provide an assessment of the overall paired-end library

Figure 3 Schematic for our two methods for converting a fosmid library into an Illumina compatible DiTag library using the fosmid vector created forthis project (A) A nick translation approach similar to the approach used in Williams et al (2012) was implemented for approximately one-half of thelibraries (B) An endonuclease digestion protocol was also used for approximately one-half of the libraries Although the location of the junction sites ismore constrained in practice we obtained higher yields from this method

880 A Zimin et al

bias and complexity and to appraise previous estimates basedon flow cytometry

Figure 5 shows a histogram of k-mer counts in our hap-loid error-corrected reads computed using the program jel-lyfish (Marccedilais and Kingsford 2011) for k = 24 and k = 31Small values were chosen for tractability as well as goodseparation between the unique haploid k-mers and uncor-rected errors Each point in the plot corresponds to the num-ber of k-mers with a particular count for example thenumber of 31-mers that occur 50 times is plotted in red atx = 50 Note that we expect a k-mer that occurs just once inthe genome (ie is nonrepetitive) to occur C times on aver-age where C is depth of coverage For haploid data this plotwould ideally take the form of a single-mode Poisson distri-bution with mean equal to C (Lander and Waterman 1988)Deviations are attributable to other properties of the ge-nome and to systematic errors in library construction andsequencing Each sequencing error may create up to k sin-gle-copy k-mers producing a peak around x = 1 The num-ber of these k-mers have been greatly reduced in Figure 5 bythe QuORUM error-correction algorithm The long tail in theplot contains conserved high-copy-number repeats

We estimated the genome size as the ratio of the totalnumber of k-mers divided by the mean number of occur-rences of the unique k-mer counts (an estimate of theexpected depth of distinct sequences of length k) as shownin Figure 5 Table 2 gives these quantities as well as thegenome size estimates for k= 24 and k= 31 Both estimatesare slightly smaller than the value determined by flowcytometry (216 Gbp) (OrsquoBrien et al 1996) and are consis-tent with our final total contig length

Haploid sequence data reduction from readsto super- reads

The goal of the super-read algorithm is to reduce thequantity of sequence data presented to the overlap-basedassembler Each maximal super-read included in the down-stream assembly passed two critical tests successful fillingof the gap between a pair of reads and determination thatthe extended super-read was not properly contained withinanother super-read (maximal)

Paired-end gap fill The extension of a read by the super-read algorithm is done conservatively with the philosophy ofrelegating the important assembly decisions to the overlap-based assembly Hence read extension will halt when itcannot be done unambiguously For a pair of reads to resultin a super-read the algorithm needs to successfully fill in the

sequence of the gap (see Zimin et al 2013) Two propertiesof the sequenced genome play a large role in the success rateof gap filling The first is the number and size of repetitivesequences in the genome An unresolved repetitive sequencewill prevent filling the gap More repeats can be resolved byusing a longer k-mer size as explained above we chose k =79 for P taeda We avoided the second property of concerndivergence between the two parental chromosomes in a dip-loid genome by constructing all short-insert libraries fromDNA of a single haploid megagametophyte

As the gap size increases so does the likelihood ofencountering ambiguity making it more difficult to fill gapsfor longer fragments For our haploid read pairs the rate ofsuccessful gap filling was as high as 96 for overlappingGAIIx reads from our shortest library Over the range of gapsizes in our data we observed an approximately linear 6reduction in the rate of successful gap filling for every 100-bp increase in the expected gap size (Appendix B Table B1)This is consistent with a relatively low per-base probability(6 3 1024) that a problem is encountered during gapfilling over the range of gap sizes observed

Maximality Only the subset of ldquomaximalrdquo super-reads ispassed to the overlap-based assembler because fully containedreads would not provide additional contiguity informationThere is a trade-off in producing maximal super-reads Assum-ing that reads have the same length it is easier to fill the gapbetween pairs that come from shorter fragments But for li-braries of longer fragments although successful gap filling isless frequent more of the gap-filled pairs generate maximalsuper-reads We observed that the percentage of maximalsuper-reads uniformly increased with library fragment lengthdespite reduced gap-filling effectiveness (Appendix B TableB1) Thus the lower gap-filling rate for longer fragments didnot detract from their utility

We can use the rate of maximal super-read creation toestimate the marginal impact of additional paired-endsequencing To improve the assembly additional paired-end reads must be able to generate new maximal super-reads In our analysis we found that 24 of the read pairsin longer (500 bp) libraries produced additional maximalsuper-reads vs only 8ndash10 of the pairs in libraries shorterthan 300 bp (Appendix B Table B1) This suggests that themost effective way to improve the assembly further wouldbe to prioritize additional paired-end data from 500-bp orlonger fragment libraries

Overall paired-end data reductionMaSuRCArsquos error-correctionand super-reads processes reduced the 14 Tbp of raw paired-end read data (in 15 billion reads) to 52 Gbp in super-readsa 27-fold reduction The reduced data contained 150 millionmaximal super-reads with an average length of 362 bp equiv-alent to 243 coverage of the genome This 100-fold reductionin the number of paired-end reads is critical for the next step ofassembly which computes pairwise overlaps between thesereads along with the linking libraries

Table 2 Haploid (1N) genome size estimates

k-mer length

31 24Total k-mers 108 3 1012 116 3 1012

E(distinct k-mer depth) 5376 occurrences 5699 occurrencesGenome size (Gbp) 2012 2042

Sequencing and Assembling P taeda 881

Our sequencing strategy used the lowest-cost and mostaccurate datamdashthe reads from the HiSeq platformmdashto pro-vide the bulk of the k-mers required for error correction Wethen generated super-reads from all error-corrected readsincluding the longer reads from the GAIIx and MiSeq instru-ments As expected the GAIIx and MiSeq reads contributeda greater proportion of maximal super-reads The optimalmix of read lengths is difficult to determine and likely variesfor every genome

Incorporating diploid mate pair sequences

We obtained 1726 million paired reads from 48 mate pairlibraries with insert sizes of 1300ndash5500 bp (Table 2 andAppendix A Table A1) After filtering to remove reads thatfailed to match the haploid DNA 1156 million reads (69)remained Another 70 million paired reads that failed tocontain a biotin junction and 171 million paired readsidentified as duplicates were removed Finally we elimi-nated pairs in which either read was shorter than 64 bpthe minimum read length for the CABOG assembler Thefinal set of jumping reads contained 540 million reads(270 million pairs) which is 37-fold physical coverageof the genome (Appendix B Table B2) Notably there waslittle variation in the rate at which reads were filtered dueto error correction and haplotype differences as the li-brary length increased

Fosmid DiTags

Particularly useful for establishing long-range links in anassembly are paired reads from the ends of fosmid (Kimet al 1992) or BAC clones (Shizuya et al 1992) which spantens of kilobases Long-range paired-end data are even morecritical for second-generation sequencing projects (Schatz

et al 2010) where read lengths are significantly shorterthan they are for first-generation sequencing projects Recentreports demonstrate that mammalian genome assemblies con-taining high-quality fosmid DiTags can attain contiguity nearlyas well as traditional Sanger-based assemblies (Gnerre et al2011)

We generated 46 million pairs of DiTag reads (AppendixB Table B3) Read pairs from DiTag libraries are subject tothe same artifacts as those from mate pair libraries namelyduplicate molecules and nonjumping pairs Application ofthe aforementioned MaSuRCA filtering procedures for jump-ing libraries was applied and yielded 45 million distinctmapped read pairs for downstream overlap layout consensusassembly The largest reduction (47 of reads removed)was due to the removal of duplicate read pairs a result ofthe limited library complexity (Appendix B Table B3) Thefiltering statistics also indicated that the nick translationprotocol was the most efficient at generating distinctDiTag read pairs To obtain an unbiased estimate of thelength distribution of these libraries we incorporated a setof D melanogaster fosmids into select pools at 1 concen-tration prior to DiTag construction The median insert sizemeasured after removal of nonjumping and chimeric out-liers was 357 kbp Using a genome-size estimate of 22 Gbpfor loblolly pine this represents 733 physical coverage of thehaploid genome

While the inclusion of fosmid DiTag libraries was helpfulonly approximately one-third of the V10 MaSuRCA assem-bled genome scaffolds (by length) contained the two ormore DiTags required to make a long-range link This leadsus to conclude that deeper fosmid DiTag coverage couldsignificantly boost scaffold contiguity in future assemblies

Figure 4 Library complexity curves quan-tify library complexity and the diminishingreturns as sequencing progresses Librarycomplexity is an estimate of the numberof distinct molecules in the library (Daleyand Smith 2013) The convexly shapedcomplexity curve plots the number of dis-tinct molecules observed against thenumber of sequenced reads Only ourdeeply sequenced library exhibited dimin-ishing returns Libraries sequenced tolower depth are shown in the inset

882 A Zimin et al

Additional scaffolding procedures for final assembly

To produce the final assembly (version 101) from the outputof MaSuRCA (version 10) we employed two additionalstrategies that improved the length and accuracy of some ofthe scaffolds First we ran an independent WGS assemblyusing SOAPdenovo2 including its gap-closing module (Luoet al 2012) We used all haploid paired-end data for the contigassembly and added the mate pair and DiTag libraries for thescaffolding step only Following sequencing and quality con-trol the adapter sequence was removed from reads using cuta-dapt (Martin 2011) and the input data were filtered toremove sequences similar to the chloroplast reference se-quence (Parks et al 2009) After error correction 1144 billionreads were used for contig assembly and 1198 billion reads forscaffolding We used a k-mer length of 79 for contig assembly

Although the SOAPdenovo2 contigs are much smallerthan in the 10 assembly with an N50 size of just 687 bp thescaffolds are larger with an N50 size of 547 kbp Thereforewe ran the SOAPdenovo2 scaffolder again using the MaSuRCAscaffolds as input ldquocontigsrdquo and using a conservatively filteredset of mate pairs as linking information This produced a newset of 834 million scaffolds with an N50 size of 868 kbp sub-stantially larger than the original value

As discussed above the DiTag sequences prior to MaSURCAprocessing contained a moderate number of nonjunction readpairs (Appendix B Table B3) To improve the assembly fidelityduring this step we broke all newly created intrascaffold links

for which all supporting mates originated from a DiTag libraryThe resulting modified assembly had 144 million scaffoldswith an N50 size of 646 kbp still over twice as large as theoriginal scaffold N50 size

We used transcripts assembled de novo from RNA-seqreads and predicted to encode full-length proteins (see NationalCenter for Biotechnology Information BioProject 174450) toidentify scaffolds that could be joined or rearranged to becongruent with transcripts Because transcripts can be assem-bled erroneously we followed a conservative strategy that pri-marily linked together scaffolds that contained a single gene

We aligned the 87241 assembled transcripts to all scaffoldsin the modified assembly described above using the nucmerprogram in the MUMmer package (Kurtz et al 2004) andcollecting only those transcripts with at least two exactmatches of 40 bp or longer This yielded 68497 transcriptsmapping to 47492 different scaffolds

Alignments between transcripts and the WGS assemblywere checked for consistency looking for instances wherethe scaffold would require an inversion or a translocation tomake it align We found 1348 inconsistencies 2 of thetotal These could represent errors in either the de novotranscriptome assembly or the WGS assembly and they willbe investigated and corrected if necessary in future assemblyreleases

We scanned the alignments looking for transcripts thatspanned two distinct scaffolds For each transcript we sorted

Figure 5 Plot of k-mer counts in the haploid paired-end WGS sequence After counting all 24- and 31-mers in the QuORUM error-corrected reads weplotted the number of distinct k-mers (y-axis) that occur exactly X times (x-axis) For our haploid data each plot has a single primary peak at the X-valuecorresponding to the depth of coverage of the 1N genome

Sequencing and Assembling P taeda 883

the scaffolds aligned to it and created a directed link betweeneach pair of scaffolds The links were weighted using the sumof the alignment lengths If multiple transcripts aligned to thesame pair of scaffolds the link between them was given thesum of all the weights (This can happen when for exampletwo transcripts represent alternative splice variants of the samegene) We rebuilt the scaffolds using these new links in orderof priority from strongest (greatest weight) to weakest checkingfor circular links at each step and discarding them

This transcript-based scaffolding step linked together31231 scaffolds into 9170 larger scaffolds slightly increas-ing the N50 size and substantially improving the number oftranscripts that align to a single scaffold The final assemblyversion 101 has 144 million scaffolds spanning 232 GbpExcluding gaps the total size of all contigs is 2015 Gbp Thelargest scaffold is 8891046 bp The scaffold N50 size is66920 bp based on a 22 Gbp total genome size (Table 3)

Assessing assembly contiguity and completeness

We used the CEGMA pipeline (Parra et al 2007) to assessthe contiguity of both the V10 and V101 assemblies withrespect to a set of highly conserved eukaryotic genes CEGMAuses a set of conserved protein families from the Clusters ofOrthologous Groups database (Tatusov et al 2003) to an-notate a genome by constructing DNA-protein alignmentsbetween the proteins and the assembled scaffolds Of 248highly conserved protein families CEGMA found full-lengthalignments to the V10 assembly for 113 (45) Includingpartial alignments brings this total to 197 (79) In theV101 assembly CEGMA found 185 (75) full-length align-ments and 203 (82) full-length and partial alignmentsThere was little change in the total number of alignmentsbetween V10 and V101 However the fraction of all align-ments to the assembly that were full length increased from57 in the V10 assembly to 91 in the V101 assemblyfurther quantifying the biological utility of the additionalscaffolding phase There are a number of possible explanationsfor the discrepancy between the coverage and the observedrates of conserved gene annotations principally assemblyfragmentation and potential ascertainment bias in the de-termination of the gene set

Comparison of fosmid and whole-genome assemblies

For validation purposes we constructed sequenced andassembled a large pool of 4600 fosmid clones (Appendix A)The assembly contained 3798 contigs longer than 20000 bp

(50 of the fosmid insert in each case) which we used tocheck the completeness and quality of the WGS assembly Be-cause the fosmids were selected at random the amount ofsequence covered by the WGS assembly should provide anestimate of how much of the entire genome has been capturedin that assembly The 3798 contigs contain 109 Mbp or05of the entire genome

We aligned contigs to the WGS assembly (V10V101contigs) using MUMmer (Kurtz et al 2004) and found thatall but 4 of the 3798 contigs aligned across nearly theirentire length at an average identity of 99 Nearly all(9863) of the combined length of the fosmid pool contigsis covered by alignments to the WGS assembly The un-aligned fraction consists of the 4 contigs that failed to align(117681 bp) plus the unaligned portions of the remainingcontigs Assuming that the fosmids represent a random sam-ple this suggests that the WGS assembly covers 98 ofthe whole genome (with the reservation that hard-to-sequence regions might be missed by both the WGS andfosmid sequencing approaches and thus not counted in thisevaluation)

Fosmid haplotype comparisons

We expect that half of the fosmid contigs would correspondto the same haplotype as the aligned WGS contig Thuswhile many contigs should be close to 100 identical contigsfrom the alternative haplotype (untransmitted) would showa divergence rate corresponding to the differences betweenhaplotypes estimated to be 1ndash2 We considered the pos-sibility that the fosmid contigs would thus fall into two dis-tinct bins near-identical and 1ndash2 divergent Howeverrelatively few contigs showed this degree of divergence2120 contigs (56) were 995 or more identical and theothers matched the WGS contigs at a range of identities withthe vast majority 98 identical (Appendix A Figure A1)Only 189 contigs (5) were 95 identical with the lowestidentity at 91 This suggests that divergence between thetwo parental haplotypes is 1 on average with a smallnumber of highly divergent regions

Conclusions

The sequencing and assembly strategy described here ofthe largest genome to date resulted in a haploid assemblycomposed of 2015 billion base pairs By many measures itis the most contiguous and complete draft assembly ofa conifer genome (Appendix C) This project required a close

Table 3 Comparison of P taeda whole-genome assemblies before and after additional scaffolding

P taeda 10 (before) P taeda 101 (after)

Total sequence in contigs (bp) 20148103497 20148103497Total span of scaffolds (bp) 22564679219 23180477227N50 contig size (bp) 8206 8206N50 scaffold size (bp) 30681 66920No of contigs 500 bp 4047642 4047642No of scaffolds 500 bp 2319749 2158326

The contigs are the same for both assemblies N50 statistics use a genome size of 22 3 109

884 A Zimin et al

coupling between sequencing and assembly strategy and thedevelopment of new computational methods to address thechallenges inherent to the assembly of mega-genomes

Conifer genomes are filled with massive amounts ofrepetitive DNA mostly transposable elements (Nystedt et al2013 Wegrzyn et al 2013 2014) which might have pre-sented a major barrier to successful genome assembly Fortu-nately most of these repeats are relatively ancient so theiraccumulated sequence differences allowed the assembly algo-rithms to distinguish individual copies

A major concern in assembly of diploid genomes is thedegree of divergence between the two parental copies ofeach chromosome Even small differences between the chro-mosomes can cause an assembler to construct two distinctcontigs which can be indistinguishable from two-copy repeatsequences Conifers offer an elegant biological solution to thisproblem by producing megagametophytes with a single copyof each chromosome from which sufficient DNA can beextracted to cover the entire genome

While the current assembly represents an important land-mark and will serve as a powerful resource for biologicalanalyses it remains incomplete To further increase sequencecontiguity we plan to generate additional long-range pairedreads particularly DiTag pairs that should substantiallyincrease scaffold lengths Noting the success of the smallerfosmid pool assemblies presented here inexpensive sequenc-ing allows for the possibility of sequencing many more of thesepools and merging the long contigs from these assemblies intothe WGS assembly (Zhang et al 2012 Nystedt et al 2013) Inparallel with efforts to improve the next P taeda referencesequence we are gathering a large set of genetically mappedSNPs that will allow placement of scaffolds onto the 12 chro-mosomes providing additional concrete physical anchors tothe assembly Finally we note that ongoing efforts to expandthe diversity of conifer genome reference sequences will pro-vide a solid foundation for comparative genomics thus improv-ing the assemblies and their annotations

Acknowledgments

This work was supported in part by US Department ofAgricultureNational Institute of Food and Agriculture(2011-67009-30030) We wish to thank C Dana Nelson atthe USDA Forest Service Southern Research Station forproviding and verifying the genotype of target tree material

Note added in proof See Wegrzyn et al 2014 (pp891ndash909) in this issue for a related work

Literature Cited

Ahuja M R and D B Neale 2005 Evolution of genome size inconifers Silvae Genet 54 126ndash137

Bai C W S Alverson A Follansbee and D M Waller 2012 Newreports of nuclear DNA content for 407 vascular plant taxa fromthe United States Ann Bot (Lond) 110 1623ndash1629

Bierhorst D W 1971 Morphology of Vascular Plants MacmillanNew York

Birol I A Raymond S D Jackman S Pleasance R Coope et al2013 Assembling the 20 Gb white spruce (Picea glauca) ge-nome from whole-genome shotgun sequencing data Bioinfor-matics 29 1492ndash1497

Bowe L M G Coat and C W Depamphilis 2000 Phylogeny ofseed plants based on all three genomic compartments extantgymnosperms are monophyletic and Gnetalesrsquo closest relativesare conifers Proc Natl Acad Sci USA 97 4092ndash4097

Daley T and A D Smith 2013 Predicting the molecular com-plexity of sequencing libraries Nat Methods 10 325ndash327

Frederick W J S J Lien C E Courchene N A DeMartini A JRagauskas et al 2008 Production of ethanol from carbohy-drates from loblolly pine a technical and economic assessmentBioresour Technol 99 5051ndash5057

Fuchs J G Jovtchev and I Schubert 2008 The chromosomaldistribution of histone methylation marks in gymnosperms dif-fers from that of angiosperms Chromosome Res 16 891ndash898

Gnerre S I MacCallum D Przybylski F J Ribeiro J N Burtonet al 2011 High-quality draft assemblies of mammalian ge-nomes from massively parallel sequence data Proc Natl AcadSci USA 108(4) 1513ndash1518

Kim U-J H Shizuya P J de Jong B Barren and M I Simon1992 Stable propagation of cosmid-sized human DNA insertsin an F factor based vector Nucleic Acids Res 20 1083ndash1085

Kovach A J L Wegrzyn G Parra C Holt G E Bruening et al2010 The Pinus taeda genome is characterized by diverse andhighly diverged repetitive sequences BMC Genomics 11(1) 420

Kurtz S A Phillippy A L Delcher M Smoot M Shumway et al2004 Versatile and open software for comparing large ge-nomes Genome Biol 5(2) R12

Lander E S and M S Waterman 1988 Genomic mapping byfingerprinting random clones a mathematical analysis Ge-nomics 2(3) 231ndash239

Li R H Zhu J Ruan W Qian X Fang et al 2010 De novoassembly of human genomes with massively parallel short readsequencing Genome Res 20(2) 265ndash272

Luo R B Liu Y Xie Z Li W Huang et al 2012 SOAPdenovo2an empirically improved memory-efficient short-read de novoassembler GigaScience DOI1011862047-217X-1-18

Mackay J J F Dean C Plomion D G Peterson F M Caacutenovaset al 2012 Towards decoding the conifer giga-genome PlantMol Biol 80 555ndash569

Magallon S and M J Sanderson 2005 Angiosperm divergencetimes the effects of genes codon positions and time con-straints Evolution 59 1653ndash1670

Marccedilais G and C Kingsford 2011 A fast lock-free approach forefficient parallel counting of occurrences of k-mers Bioinfor-matics 27(6) 764ndash770

Marcais G J Yorke and A Zimin 2013 QuorUM an error cor-rector for Illumina reads arXiv13073515 [q-bioGN]

Martin M 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet J 17(1) 10

McKeand S T Mullin T Byram and T White 2003 Deploymentof genetically improved loblolly and slash pines in the south J For101(3) 32ndash37

Miller J R A L Delcher S Koren E Venter B P Walenz et al2008 Aggressive assembly of pyrosequencing reads withmates Bioinformatics 24(24) 2818ndash2824

Mirov N T 1967 The Genus Pinus Ronald Press New YorkMorse A M D G Peterson M N Islam-Faridi K E Smith Z

Magbanua et al 2009 Evolution of genome size and complex-ity in Pinus PLoS ONE 4(2) e4332

Neale D B and A Kremer 2011 Forest tree genomics growingresources and applications Nat Rev Genet 12(2) 111ndash122

Sequencing and Assembling P taeda 885

bias and complexity and to appraise previous estimates basedon flow cytometry

Figure 5 shows a histogram of k-mer counts in our hap-loid error-corrected reads computed using the program jel-lyfish (Marccedilais and Kingsford 2011) for k = 24 and k = 31Small values were chosen for tractability as well as goodseparation between the unique haploid k-mers and uncor-rected errors Each point in the plot corresponds to the num-ber of k-mers with a particular count for example thenumber of 31-mers that occur 50 times is plotted in red atx = 50 Note that we expect a k-mer that occurs just once inthe genome (ie is nonrepetitive) to occur C times on aver-age where C is depth of coverage For haploid data this plotwould ideally take the form of a single-mode Poisson distri-bution with mean equal to C (Lander and Waterman 1988)Deviations are attributable to other properties of the ge-nome and to systematic errors in library construction andsequencing Each sequencing error may create up to k sin-gle-copy k-mers producing a peak around x = 1 The num-ber of these k-mers have been greatly reduced in Figure 5 bythe QuORUM error-correction algorithm The long tail in theplot contains conserved high-copy-number repeats

We estimated the genome size as the ratio of the totalnumber of k-mers divided by the mean number of occur-rences of the unique k-mer counts (an estimate of theexpected depth of distinct sequences of length k) as shownin Figure 5 Table 2 gives these quantities as well as thegenome size estimates for k= 24 and k= 31 Both estimatesare slightly smaller than the value determined by flowcytometry (216 Gbp) (OrsquoBrien et al 1996) and are consis-tent with our final total contig length

Haploid sequence data reduction from readsto super- reads

The goal of the super-read algorithm is to reduce thequantity of sequence data presented to the overlap-basedassembler Each maximal super-read included in the down-stream assembly passed two critical tests successful fillingof the gap between a pair of reads and determination thatthe extended super-read was not properly contained withinanother super-read (maximal)

Paired-end gap fill The extension of a read by the super-read algorithm is done conservatively with the philosophy ofrelegating the important assembly decisions to the overlap-based assembly Hence read extension will halt when itcannot be done unambiguously For a pair of reads to resultin a super-read the algorithm needs to successfully fill in the

sequence of the gap (see Zimin et al 2013) Two propertiesof the sequenced genome play a large role in the success rateof gap filling The first is the number and size of repetitivesequences in the genome An unresolved repetitive sequencewill prevent filling the gap More repeats can be resolved byusing a longer k-mer size as explained above we chose k =79 for P taeda We avoided the second property of concerndivergence between the two parental chromosomes in a dip-loid genome by constructing all short-insert libraries fromDNA of a single haploid megagametophyte

As the gap size increases so does the likelihood ofencountering ambiguity making it more difficult to fill gapsfor longer fragments For our haploid read pairs the rate ofsuccessful gap filling was as high as 96 for overlappingGAIIx reads from our shortest library Over the range of gapsizes in our data we observed an approximately linear 6reduction in the rate of successful gap filling for every 100-bp increase in the expected gap size (Appendix B Table B1)This is consistent with a relatively low per-base probability(6 3 1024) that a problem is encountered during gapfilling over the range of gap sizes observed

Maximality Only the subset of ldquomaximalrdquo super-reads ispassed to the overlap-based assembler because fully containedreads would not provide additional contiguity informationThere is a trade-off in producing maximal super-reads Assum-ing that reads have the same length it is easier to fill the gapbetween pairs that come from shorter fragments But for li-braries of longer fragments although successful gap filling isless frequent more of the gap-filled pairs generate maximalsuper-reads We observed that the percentage of maximalsuper-reads uniformly increased with library fragment lengthdespite reduced gap-filling effectiveness (Appendix B TableB1) Thus the lower gap-filling rate for longer fragments didnot detract from their utility

We can use the rate of maximal super-read creation toestimate the marginal impact of additional paired-endsequencing To improve the assembly additional paired-end reads must be able to generate new maximal super-reads In our analysis we found that 24 of the read pairsin longer (500 bp) libraries produced additional maximalsuper-reads vs only 8ndash10 of the pairs in libraries shorterthan 300 bp (Appendix B Table B1) This suggests that themost effective way to improve the assembly further wouldbe to prioritize additional paired-end data from 500-bp orlonger fragment libraries

Overall paired-end data reductionMaSuRCArsquos error-correctionand super-reads processes reduced the 14 Tbp of raw paired-end read data (in 15 billion reads) to 52 Gbp in super-readsa 27-fold reduction The reduced data contained 150 millionmaximal super-reads with an average length of 362 bp equiv-alent to 243 coverage of the genome This 100-fold reductionin the number of paired-end reads is critical for the next step ofassembly which computes pairwise overlaps between thesereads along with the linking libraries

Table 2 Haploid (1N) genome size estimates

k-mer length

31 24Total k-mers 108 3 1012 116 3 1012

E(distinct k-mer depth) 5376 occurrences 5699 occurrencesGenome size (Gbp) 2012 2042

Sequencing and Assembling P taeda 881

Our sequencing strategy used the lowest-cost and mostaccurate datamdashthe reads from the HiSeq platformmdashto pro-vide the bulk of the k-mers required for error correction Wethen generated super-reads from all error-corrected readsincluding the longer reads from the GAIIx and MiSeq instru-ments As expected the GAIIx and MiSeq reads contributeda greater proportion of maximal super-reads The optimalmix of read lengths is difficult to determine and likely variesfor every genome

Incorporating diploid mate pair sequences

We obtained 1726 million paired reads from 48 mate pairlibraries with insert sizes of 1300ndash5500 bp (Table 2 andAppendix A Table A1) After filtering to remove reads thatfailed to match the haploid DNA 1156 million reads (69)remained Another 70 million paired reads that failed tocontain a biotin junction and 171 million paired readsidentified as duplicates were removed Finally we elimi-nated pairs in which either read was shorter than 64 bpthe minimum read length for the CABOG assembler Thefinal set of jumping reads contained 540 million reads(270 million pairs) which is 37-fold physical coverageof the genome (Appendix B Table B2) Notably there waslittle variation in the rate at which reads were filtered dueto error correction and haplotype differences as the li-brary length increased

Fosmid DiTags

Particularly useful for establishing long-range links in anassembly are paired reads from the ends of fosmid (Kimet al 1992) or BAC clones (Shizuya et al 1992) which spantens of kilobases Long-range paired-end data are even morecritical for second-generation sequencing projects (Schatz

et al 2010) where read lengths are significantly shorterthan they are for first-generation sequencing projects Recentreports demonstrate that mammalian genome assemblies con-taining high-quality fosmid DiTags can attain contiguity nearlyas well as traditional Sanger-based assemblies (Gnerre et al2011)

We generated 46 million pairs of DiTag reads (AppendixB Table B3) Read pairs from DiTag libraries are subject tothe same artifacts as those from mate pair libraries namelyduplicate molecules and nonjumping pairs Application ofthe aforementioned MaSuRCA filtering procedures for jump-ing libraries was applied and yielded 45 million distinctmapped read pairs for downstream overlap layout consensusassembly The largest reduction (47 of reads removed)was due to the removal of duplicate read pairs a result ofthe limited library complexity (Appendix B Table B3) Thefiltering statistics also indicated that the nick translationprotocol was the most efficient at generating distinctDiTag read pairs To obtain an unbiased estimate of thelength distribution of these libraries we incorporated a setof D melanogaster fosmids into select pools at 1 concen-tration prior to DiTag construction The median insert sizemeasured after removal of nonjumping and chimeric out-liers was 357 kbp Using a genome-size estimate of 22 Gbpfor loblolly pine this represents 733 physical coverage of thehaploid genome

While the inclusion of fosmid DiTag libraries was helpfulonly approximately one-third of the V10 MaSuRCA assem-bled genome scaffolds (by length) contained the two ormore DiTags required to make a long-range link This leadsus to conclude that deeper fosmid DiTag coverage couldsignificantly boost scaffold contiguity in future assemblies

Figure 4 Library complexity curves quan-tify library complexity and the diminishingreturns as sequencing progresses Librarycomplexity is an estimate of the numberof distinct molecules in the library (Daleyand Smith 2013) The convexly shapedcomplexity curve plots the number of dis-tinct molecules observed against thenumber of sequenced reads Only ourdeeply sequenced library exhibited dimin-ishing returns Libraries sequenced tolower depth are shown in the inset

882 A Zimin et al

Additional scaffolding procedures for final assembly

To produce the final assembly (version 101) from the outputof MaSuRCA (version 10) we employed two additionalstrategies that improved the length and accuracy of some ofthe scaffolds First we ran an independent WGS assemblyusing SOAPdenovo2 including its gap-closing module (Luoet al 2012) We used all haploid paired-end data for the contigassembly and added the mate pair and DiTag libraries for thescaffolding step only Following sequencing and quality con-trol the adapter sequence was removed from reads using cuta-dapt (Martin 2011) and the input data were filtered toremove sequences similar to the chloroplast reference se-quence (Parks et al 2009) After error correction 1144 billionreads were used for contig assembly and 1198 billion reads forscaffolding We used a k-mer length of 79 for contig assembly

Although the SOAPdenovo2 contigs are much smallerthan in the 10 assembly with an N50 size of just 687 bp thescaffolds are larger with an N50 size of 547 kbp Thereforewe ran the SOAPdenovo2 scaffolder again using the MaSuRCAscaffolds as input ldquocontigsrdquo and using a conservatively filteredset of mate pairs as linking information This produced a newset of 834 million scaffolds with an N50 size of 868 kbp sub-stantially larger than the original value

As discussed above the DiTag sequences prior to MaSURCAprocessing contained a moderate number of nonjunction readpairs (Appendix B Table B3) To improve the assembly fidelityduring this step we broke all newly created intrascaffold links

for which all supporting mates originated from a DiTag libraryThe resulting modified assembly had 144 million scaffoldswith an N50 size of 646 kbp still over twice as large as theoriginal scaffold N50 size

We used transcripts assembled de novo from RNA-seqreads and predicted to encode full-length proteins (see NationalCenter for Biotechnology Information BioProject 174450) toidentify scaffolds that could be joined or rearranged to becongruent with transcripts Because transcripts can be assem-bled erroneously we followed a conservative strategy that pri-marily linked together scaffolds that contained a single gene

We aligned the 87241 assembled transcripts to all scaffoldsin the modified assembly described above using the nucmerprogram in the MUMmer package (Kurtz et al 2004) andcollecting only those transcripts with at least two exactmatches of 40 bp or longer This yielded 68497 transcriptsmapping to 47492 different scaffolds

Alignments between transcripts and the WGS assemblywere checked for consistency looking for instances wherethe scaffold would require an inversion or a translocation tomake it align We found 1348 inconsistencies 2 of thetotal These could represent errors in either the de novotranscriptome assembly or the WGS assembly and they willbe investigated and corrected if necessary in future assemblyreleases

We scanned the alignments looking for transcripts thatspanned two distinct scaffolds For each transcript we sorted

Figure 5 Plot of k-mer counts in the haploid paired-end WGS sequence After counting all 24- and 31-mers in the QuORUM error-corrected reads weplotted the number of distinct k-mers (y-axis) that occur exactly X times (x-axis) For our haploid data each plot has a single primary peak at the X-valuecorresponding to the depth of coverage of the 1N genome

Sequencing and Assembling P taeda 883

the scaffolds aligned to it and created a directed link betweeneach pair of scaffolds The links were weighted using the sumof the alignment lengths If multiple transcripts aligned to thesame pair of scaffolds the link between them was given thesum of all the weights (This can happen when for exampletwo transcripts represent alternative splice variants of the samegene) We rebuilt the scaffolds using these new links in orderof priority from strongest (greatest weight) to weakest checkingfor circular links at each step and discarding them

This transcript-based scaffolding step linked together31231 scaffolds into 9170 larger scaffolds slightly increas-ing the N50 size and substantially improving the number oftranscripts that align to a single scaffold The final assemblyversion 101 has 144 million scaffolds spanning 232 GbpExcluding gaps the total size of all contigs is 2015 Gbp Thelargest scaffold is 8891046 bp The scaffold N50 size is66920 bp based on a 22 Gbp total genome size (Table 3)

Assessing assembly contiguity and completeness

We used the CEGMA pipeline (Parra et al 2007) to assessthe contiguity of both the V10 and V101 assemblies withrespect to a set of highly conserved eukaryotic genes CEGMAuses a set of conserved protein families from the Clusters ofOrthologous Groups database (Tatusov et al 2003) to an-notate a genome by constructing DNA-protein alignmentsbetween the proteins and the assembled scaffolds Of 248highly conserved protein families CEGMA found full-lengthalignments to the V10 assembly for 113 (45) Includingpartial alignments brings this total to 197 (79) In theV101 assembly CEGMA found 185 (75) full-length align-ments and 203 (82) full-length and partial alignmentsThere was little change in the total number of alignmentsbetween V10 and V101 However the fraction of all align-ments to the assembly that were full length increased from57 in the V10 assembly to 91 in the V101 assemblyfurther quantifying the biological utility of the additionalscaffolding phase There are a number of possible explanationsfor the discrepancy between the coverage and the observedrates of conserved gene annotations principally assemblyfragmentation and potential ascertainment bias in the de-termination of the gene set

Comparison of fosmid and whole-genome assemblies

For validation purposes we constructed sequenced andassembled a large pool of 4600 fosmid clones (Appendix A)The assembly contained 3798 contigs longer than 20000 bp

(50 of the fosmid insert in each case) which we used tocheck the completeness and quality of the WGS assembly Be-cause the fosmids were selected at random the amount ofsequence covered by the WGS assembly should provide anestimate of how much of the entire genome has been capturedin that assembly The 3798 contigs contain 109 Mbp or05of the entire genome

We aligned contigs to the WGS assembly (V10V101contigs) using MUMmer (Kurtz et al 2004) and found thatall but 4 of the 3798 contigs aligned across nearly theirentire length at an average identity of 99 Nearly all(9863) of the combined length of the fosmid pool contigsis covered by alignments to the WGS assembly The un-aligned fraction consists of the 4 contigs that failed to align(117681 bp) plus the unaligned portions of the remainingcontigs Assuming that the fosmids represent a random sam-ple this suggests that the WGS assembly covers 98 ofthe whole genome (with the reservation that hard-to-sequence regions might be missed by both the WGS andfosmid sequencing approaches and thus not counted in thisevaluation)

Fosmid haplotype comparisons

We expect that half of the fosmid contigs would correspondto the same haplotype as the aligned WGS contig Thuswhile many contigs should be close to 100 identical contigsfrom the alternative haplotype (untransmitted) would showa divergence rate corresponding to the differences betweenhaplotypes estimated to be 1ndash2 We considered the pos-sibility that the fosmid contigs would thus fall into two dis-tinct bins near-identical and 1ndash2 divergent Howeverrelatively few contigs showed this degree of divergence2120 contigs (56) were 995 or more identical and theothers matched the WGS contigs at a range of identities withthe vast majority 98 identical (Appendix A Figure A1)Only 189 contigs (5) were 95 identical with the lowestidentity at 91 This suggests that divergence between thetwo parental haplotypes is 1 on average with a smallnumber of highly divergent regions

Conclusions

The sequencing and assembly strategy described here ofthe largest genome to date resulted in a haploid assemblycomposed of 2015 billion base pairs By many measures itis the most contiguous and complete draft assembly ofa conifer genome (Appendix C) This project required a close

Table 3 Comparison of P taeda whole-genome assemblies before and after additional scaffolding

P taeda 10 (before) P taeda 101 (after)

Total sequence in contigs (bp) 20148103497 20148103497Total span of scaffolds (bp) 22564679219 23180477227N50 contig size (bp) 8206 8206N50 scaffold size (bp) 30681 66920No of contigs 500 bp 4047642 4047642No of scaffolds 500 bp 2319749 2158326

The contigs are the same for both assemblies N50 statistics use a genome size of 22 3 109

884 A Zimin et al

coupling between sequencing and assembly strategy and thedevelopment of new computational methods to address thechallenges inherent to the assembly of mega-genomes

Conifer genomes are filled with massive amounts ofrepetitive DNA mostly transposable elements (Nystedt et al2013 Wegrzyn et al 2013 2014) which might have pre-sented a major barrier to successful genome assembly Fortu-nately most of these repeats are relatively ancient so theiraccumulated sequence differences allowed the assembly algo-rithms to distinguish individual copies

A major concern in assembly of diploid genomes is thedegree of divergence between the two parental copies ofeach chromosome Even small differences between the chro-mosomes can cause an assembler to construct two distinctcontigs which can be indistinguishable from two-copy repeatsequences Conifers offer an elegant biological solution to thisproblem by producing megagametophytes with a single copyof each chromosome from which sufficient DNA can beextracted to cover the entire genome

While the current assembly represents an important land-mark and will serve as a powerful resource for biologicalanalyses it remains incomplete To further increase sequencecontiguity we plan to generate additional long-range pairedreads particularly DiTag pairs that should substantiallyincrease scaffold lengths Noting the success of the smallerfosmid pool assemblies presented here inexpensive sequenc-ing allows for the possibility of sequencing many more of thesepools and merging the long contigs from these assemblies intothe WGS assembly (Zhang et al 2012 Nystedt et al 2013) Inparallel with efforts to improve the next P taeda referencesequence we are gathering a large set of genetically mappedSNPs that will allow placement of scaffolds onto the 12 chro-mosomes providing additional concrete physical anchors tothe assembly Finally we note that ongoing efforts to expandthe diversity of conifer genome reference sequences will pro-vide a solid foundation for comparative genomics thus improv-ing the assemblies and their annotations

Acknowledgments

This work was supported in part by US Department ofAgricultureNational Institute of Food and Agriculture(2011-67009-30030) We wish to thank C Dana Nelson atthe USDA Forest Service Southern Research Station forproviding and verifying the genotype of target tree material

Note added in proof See Wegrzyn et al 2014 (pp891ndash909) in this issue for a related work

Literature Cited

Ahuja M R and D B Neale 2005 Evolution of genome size inconifers Silvae Genet 54 126ndash137

Bai C W S Alverson A Follansbee and D M Waller 2012 Newreports of nuclear DNA content for 407 vascular plant taxa fromthe United States Ann Bot (Lond) 110 1623ndash1629

Bierhorst D W 1971 Morphology of Vascular Plants MacmillanNew York

Birol I A Raymond S D Jackman S Pleasance R Coope et al2013 Assembling the 20 Gb white spruce (Picea glauca) ge-nome from whole-genome shotgun sequencing data Bioinfor-matics 29 1492ndash1497

Bowe L M G Coat and C W Depamphilis 2000 Phylogeny ofseed plants based on all three genomic compartments extantgymnosperms are monophyletic and Gnetalesrsquo closest relativesare conifers Proc Natl Acad Sci USA 97 4092ndash4097

Daley T and A D Smith 2013 Predicting the molecular com-plexity of sequencing libraries Nat Methods 10 325ndash327

Frederick W J S J Lien C E Courchene N A DeMartini A JRagauskas et al 2008 Production of ethanol from carbohy-drates from loblolly pine a technical and economic assessmentBioresour Technol 99 5051ndash5057

Fuchs J G Jovtchev and I Schubert 2008 The chromosomaldistribution of histone methylation marks in gymnosperms dif-fers from that of angiosperms Chromosome Res 16 891ndash898

Gnerre S I MacCallum D Przybylski F J Ribeiro J N Burtonet al 2011 High-quality draft assemblies of mammalian ge-nomes from massively parallel sequence data Proc Natl AcadSci USA 108(4) 1513ndash1518

Kim U-J H Shizuya P J de Jong B Barren and M I Simon1992 Stable propagation of cosmid-sized human DNA insertsin an F factor based vector Nucleic Acids Res 20 1083ndash1085

Kovach A J L Wegrzyn G Parra C Holt G E Bruening et al2010 The Pinus taeda genome is characterized by diverse andhighly diverged repetitive sequences BMC Genomics 11(1) 420

Kurtz S A Phillippy A L Delcher M Smoot M Shumway et al2004 Versatile and open software for comparing large ge-nomes Genome Biol 5(2) R12

Lander E S and M S Waterman 1988 Genomic mapping byfingerprinting random clones a mathematical analysis Ge-nomics 2(3) 231ndash239

Li R H Zhu J Ruan W Qian X Fang et al 2010 De novoassembly of human genomes with massively parallel short readsequencing Genome Res 20(2) 265ndash272

Luo R B Liu Y Xie Z Li W Huang et al 2012 SOAPdenovo2an empirically improved memory-efficient short-read de novoassembler GigaScience DOI1011862047-217X-1-18

Mackay J J F Dean C Plomion D G Peterson F M Caacutenovaset al 2012 Towards decoding the conifer giga-genome PlantMol Biol 80 555ndash569

Magallon S and M J Sanderson 2005 Angiosperm divergencetimes the effects of genes codon positions and time con-straints Evolution 59 1653ndash1670

Marccedilais G and C Kingsford 2011 A fast lock-free approach forefficient parallel counting of occurrences of k-mers Bioinfor-matics 27(6) 764ndash770

Marcais G J Yorke and A Zimin 2013 QuorUM an error cor-rector for Illumina reads arXiv13073515 [q-bioGN]

Martin M 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet J 17(1) 10

McKeand S T Mullin T Byram and T White 2003 Deploymentof genetically improved loblolly and slash pines in the south J For101(3) 32ndash37

Miller J R A L Delcher S Koren E Venter B P Walenz et al2008 Aggressive assembly of pyrosequencing reads withmates Bioinformatics 24(24) 2818ndash2824

Mirov N T 1967 The Genus Pinus Ronald Press New YorkMorse A M D G Peterson M N Islam-Faridi K E Smith Z

Magbanua et al 2009 Evolution of genome size and complex-ity in Pinus PLoS ONE 4(2) e4332

Neale D B and A Kremer 2011 Forest tree genomics growingresources and applications Nat Rev Genet 12(2) 111ndash122

Sequencing and Assembling P taeda 885

Our sequencing strategy used the lowest-cost and mostaccurate datamdashthe reads from the HiSeq platformmdashto pro-vide the bulk of the k-mers required for error correction Wethen generated super-reads from all error-corrected readsincluding the longer reads from the GAIIx and MiSeq instru-ments As expected the GAIIx and MiSeq reads contributeda greater proportion of maximal super-reads The optimalmix of read lengths is difficult to determine and likely variesfor every genome

Incorporating diploid mate pair sequences

We obtained 1726 million paired reads from 48 mate pairlibraries with insert sizes of 1300ndash5500 bp (Table 2 andAppendix A Table A1) After filtering to remove reads thatfailed to match the haploid DNA 1156 million reads (69)remained Another 70 million paired reads that failed tocontain a biotin junction and 171 million paired readsidentified as duplicates were removed Finally we elimi-nated pairs in which either read was shorter than 64 bpthe minimum read length for the CABOG assembler Thefinal set of jumping reads contained 540 million reads(270 million pairs) which is 37-fold physical coverageof the genome (Appendix B Table B2) Notably there waslittle variation in the rate at which reads were filtered dueto error correction and haplotype differences as the li-brary length increased

Fosmid DiTags

Particularly useful for establishing long-range links in anassembly are paired reads from the ends of fosmid (Kimet al 1992) or BAC clones (Shizuya et al 1992) which spantens of kilobases Long-range paired-end data are even morecritical for second-generation sequencing projects (Schatz

et al 2010) where read lengths are significantly shorterthan they are for first-generation sequencing projects Recentreports demonstrate that mammalian genome assemblies con-taining high-quality fosmid DiTags can attain contiguity nearlyas well as traditional Sanger-based assemblies (Gnerre et al2011)

We generated 46 million pairs of DiTag reads (AppendixB Table B3) Read pairs from DiTag libraries are subject tothe same artifacts as those from mate pair libraries namelyduplicate molecules and nonjumping pairs Application ofthe aforementioned MaSuRCA filtering procedures for jump-ing libraries was applied and yielded 45 million distinctmapped read pairs for downstream overlap layout consensusassembly The largest reduction (47 of reads removed)was due to the removal of duplicate read pairs a result ofthe limited library complexity (Appendix B Table B3) Thefiltering statistics also indicated that the nick translationprotocol was the most efficient at generating distinctDiTag read pairs To obtain an unbiased estimate of thelength distribution of these libraries we incorporated a setof D melanogaster fosmids into select pools at 1 concen-tration prior to DiTag construction The median insert sizemeasured after removal of nonjumping and chimeric out-liers was 357 kbp Using a genome-size estimate of 22 Gbpfor loblolly pine this represents 733 physical coverage of thehaploid genome

While the inclusion of fosmid DiTag libraries was helpfulonly approximately one-third of the V10 MaSuRCA assem-bled genome scaffolds (by length) contained the two ormore DiTags required to make a long-range link This leadsus to conclude that deeper fosmid DiTag coverage couldsignificantly boost scaffold contiguity in future assemblies

Figure 4 Library complexity curves quan-tify library complexity and the diminishingreturns as sequencing progresses Librarycomplexity is an estimate of the numberof distinct molecules in the library (Daleyand Smith 2013) The convexly shapedcomplexity curve plots the number of dis-tinct molecules observed against thenumber of sequenced reads Only ourdeeply sequenced library exhibited dimin-ishing returns Libraries sequenced tolower depth are shown in the inset

882 A Zimin et al

Additional scaffolding procedures for final assembly

To produce the final assembly (version 101) from the outputof MaSuRCA (version 10) we employed two additionalstrategies that improved the length and accuracy of some ofthe scaffolds First we ran an independent WGS assemblyusing SOAPdenovo2 including its gap-closing module (Luoet al 2012) We used all haploid paired-end data for the contigassembly and added the mate pair and DiTag libraries for thescaffolding step only Following sequencing and quality con-trol the adapter sequence was removed from reads using cuta-dapt (Martin 2011) and the input data were filtered toremove sequences similar to the chloroplast reference se-quence (Parks et al 2009) After error correction 1144 billionreads were used for contig assembly and 1198 billion reads forscaffolding We used a k-mer length of 79 for contig assembly

Although the SOAPdenovo2 contigs are much smallerthan in the 10 assembly with an N50 size of just 687 bp thescaffolds are larger with an N50 size of 547 kbp Thereforewe ran the SOAPdenovo2 scaffolder again using the MaSuRCAscaffolds as input ldquocontigsrdquo and using a conservatively filteredset of mate pairs as linking information This produced a newset of 834 million scaffolds with an N50 size of 868 kbp sub-stantially larger than the original value

As discussed above the DiTag sequences prior to MaSURCAprocessing contained a moderate number of nonjunction readpairs (Appendix B Table B3) To improve the assembly fidelityduring this step we broke all newly created intrascaffold links

for which all supporting mates originated from a DiTag libraryThe resulting modified assembly had 144 million scaffoldswith an N50 size of 646 kbp still over twice as large as theoriginal scaffold N50 size

We used transcripts assembled de novo from RNA-seqreads and predicted to encode full-length proteins (see NationalCenter for Biotechnology Information BioProject 174450) toidentify scaffolds that could be joined or rearranged to becongruent with transcripts Because transcripts can be assem-bled erroneously we followed a conservative strategy that pri-marily linked together scaffolds that contained a single gene

We aligned the 87241 assembled transcripts to all scaffoldsin the modified assembly described above using the nucmerprogram in the MUMmer package (Kurtz et al 2004) andcollecting only those transcripts with at least two exactmatches of 40 bp or longer This yielded 68497 transcriptsmapping to 47492 different scaffolds

Alignments between transcripts and the WGS assemblywere checked for consistency looking for instances wherethe scaffold would require an inversion or a translocation tomake it align We found 1348 inconsistencies 2 of thetotal These could represent errors in either the de novotranscriptome assembly or the WGS assembly and they willbe investigated and corrected if necessary in future assemblyreleases

We scanned the alignments looking for transcripts thatspanned two distinct scaffolds For each transcript we sorted

Figure 5 Plot of k-mer counts in the haploid paired-end WGS sequence After counting all 24- and 31-mers in the QuORUM error-corrected reads weplotted the number of distinct k-mers (y-axis) that occur exactly X times (x-axis) For our haploid data each plot has a single primary peak at the X-valuecorresponding to the depth of coverage of the 1N genome

Sequencing and Assembling P taeda 883

the scaffolds aligned to it and created a directed link betweeneach pair of scaffolds The links were weighted using the sumof the alignment lengths If multiple transcripts aligned to thesame pair of scaffolds the link between them was given thesum of all the weights (This can happen when for exampletwo transcripts represent alternative splice variants of the samegene) We rebuilt the scaffolds using these new links in orderof priority from strongest (greatest weight) to weakest checkingfor circular links at each step and discarding them

This transcript-based scaffolding step linked together31231 scaffolds into 9170 larger scaffolds slightly increas-ing the N50 size and substantially improving the number oftranscripts that align to a single scaffold The final assemblyversion 101 has 144 million scaffolds spanning 232 GbpExcluding gaps the total size of all contigs is 2015 Gbp Thelargest scaffold is 8891046 bp The scaffold N50 size is66920 bp based on a 22 Gbp total genome size (Table 3)

Assessing assembly contiguity and completeness

We used the CEGMA pipeline (Parra et al 2007) to assessthe contiguity of both the V10 and V101 assemblies withrespect to a set of highly conserved eukaryotic genes CEGMAuses a set of conserved protein families from the Clusters ofOrthologous Groups database (Tatusov et al 2003) to an-notate a genome by constructing DNA-protein alignmentsbetween the proteins and the assembled scaffolds Of 248highly conserved protein families CEGMA found full-lengthalignments to the V10 assembly for 113 (45) Includingpartial alignments brings this total to 197 (79) In theV101 assembly CEGMA found 185 (75) full-length align-ments and 203 (82) full-length and partial alignmentsThere was little change in the total number of alignmentsbetween V10 and V101 However the fraction of all align-ments to the assembly that were full length increased from57 in the V10 assembly to 91 in the V101 assemblyfurther quantifying the biological utility of the additionalscaffolding phase There are a number of possible explanationsfor the discrepancy between the coverage and the observedrates of conserved gene annotations principally assemblyfragmentation and potential ascertainment bias in the de-termination of the gene set

Comparison of fosmid and whole-genome assemblies

For validation purposes we constructed sequenced andassembled a large pool of 4600 fosmid clones (Appendix A)The assembly contained 3798 contigs longer than 20000 bp

(50 of the fosmid insert in each case) which we used tocheck the completeness and quality of the WGS assembly Be-cause the fosmids were selected at random the amount ofsequence covered by the WGS assembly should provide anestimate of how much of the entire genome has been capturedin that assembly The 3798 contigs contain 109 Mbp or05of the entire genome

We aligned contigs to the WGS assembly (V10V101contigs) using MUMmer (Kurtz et al 2004) and found thatall but 4 of the 3798 contigs aligned across nearly theirentire length at an average identity of 99 Nearly all(9863) of the combined length of the fosmid pool contigsis covered by alignments to the WGS assembly The un-aligned fraction consists of the 4 contigs that failed to align(117681 bp) plus the unaligned portions of the remainingcontigs Assuming that the fosmids represent a random sam-ple this suggests that the WGS assembly covers 98 ofthe whole genome (with the reservation that hard-to-sequence regions might be missed by both the WGS andfosmid sequencing approaches and thus not counted in thisevaluation)

Fosmid haplotype comparisons

We expect that half of the fosmid contigs would correspondto the same haplotype as the aligned WGS contig Thuswhile many contigs should be close to 100 identical contigsfrom the alternative haplotype (untransmitted) would showa divergence rate corresponding to the differences betweenhaplotypes estimated to be 1ndash2 We considered the pos-sibility that the fosmid contigs would thus fall into two dis-tinct bins near-identical and 1ndash2 divergent Howeverrelatively few contigs showed this degree of divergence2120 contigs (56) were 995 or more identical and theothers matched the WGS contigs at a range of identities withthe vast majority 98 identical (Appendix A Figure A1)Only 189 contigs (5) were 95 identical with the lowestidentity at 91 This suggests that divergence between thetwo parental haplotypes is 1 on average with a smallnumber of highly divergent regions

Conclusions

The sequencing and assembly strategy described here ofthe largest genome to date resulted in a haploid assemblycomposed of 2015 billion base pairs By many measures itis the most contiguous and complete draft assembly ofa conifer genome (Appendix C) This project required a close

Table 3 Comparison of P taeda whole-genome assemblies before and after additional scaffolding

P taeda 10 (before) P taeda 101 (after)

Total sequence in contigs (bp) 20148103497 20148103497Total span of scaffolds (bp) 22564679219 23180477227N50 contig size (bp) 8206 8206N50 scaffold size (bp) 30681 66920No of contigs 500 bp 4047642 4047642No of scaffolds 500 bp 2319749 2158326

The contigs are the same for both assemblies N50 statistics use a genome size of 22 3 109

884 A Zimin et al

coupling between sequencing and assembly strategy and thedevelopment of new computational methods to address thechallenges inherent to the assembly of mega-genomes

Conifer genomes are filled with massive amounts ofrepetitive DNA mostly transposable elements (Nystedt et al2013 Wegrzyn et al 2013 2014) which might have pre-sented a major barrier to successful genome assembly Fortu-nately most of these repeats are relatively ancient so theiraccumulated sequence differences allowed the assembly algo-rithms to distinguish individual copies

A major concern in assembly of diploid genomes is thedegree of divergence between the two parental copies ofeach chromosome Even small differences between the chro-mosomes can cause an assembler to construct two distinctcontigs which can be indistinguishable from two-copy repeatsequences Conifers offer an elegant biological solution to thisproblem by producing megagametophytes with a single copyof each chromosome from which sufficient DNA can beextracted to cover the entire genome

While the current assembly represents an important land-mark and will serve as a powerful resource for biologicalanalyses it remains incomplete To further increase sequencecontiguity we plan to generate additional long-range pairedreads particularly DiTag pairs that should substantiallyincrease scaffold lengths Noting the success of the smallerfosmid pool assemblies presented here inexpensive sequenc-ing allows for the possibility of sequencing many more of thesepools and merging the long contigs from these assemblies intothe WGS assembly (Zhang et al 2012 Nystedt et al 2013) Inparallel with efforts to improve the next P taeda referencesequence we are gathering a large set of genetically mappedSNPs that will allow placement of scaffolds onto the 12 chro-mosomes providing additional concrete physical anchors tothe assembly Finally we note that ongoing efforts to expandthe diversity of conifer genome reference sequences will pro-vide a solid foundation for comparative genomics thus improv-ing the assemblies and their annotations

Acknowledgments

This work was supported in part by US Department ofAgricultureNational Institute of Food and Agriculture(2011-67009-30030) We wish to thank C Dana Nelson atthe USDA Forest Service Southern Research Station forproviding and verifying the genotype of target tree material

Note added in proof See Wegrzyn et al 2014 (pp891ndash909) in this issue for a related work

Literature Cited

Ahuja M R and D B Neale 2005 Evolution of genome size inconifers Silvae Genet 54 126ndash137

Bai C W S Alverson A Follansbee and D M Waller 2012 Newreports of nuclear DNA content for 407 vascular plant taxa fromthe United States Ann Bot (Lond) 110 1623ndash1629

Bierhorst D W 1971 Morphology of Vascular Plants MacmillanNew York

Birol I A Raymond S D Jackman S Pleasance R Coope et al2013 Assembling the 20 Gb white spruce (Picea glauca) ge-nome from whole-genome shotgun sequencing data Bioinfor-matics 29 1492ndash1497

Bowe L M G Coat and C W Depamphilis 2000 Phylogeny ofseed plants based on all three genomic compartments extantgymnosperms are monophyletic and Gnetalesrsquo closest relativesare conifers Proc Natl Acad Sci USA 97 4092ndash4097

Daley T and A D Smith 2013 Predicting the molecular com-plexity of sequencing libraries Nat Methods 10 325ndash327

Frederick W J S J Lien C E Courchene N A DeMartini A JRagauskas et al 2008 Production of ethanol from carbohy-drates from loblolly pine a technical and economic assessmentBioresour Technol 99 5051ndash5057

Fuchs J G Jovtchev and I Schubert 2008 The chromosomaldistribution of histone methylation marks in gymnosperms dif-fers from that of angiosperms Chromosome Res 16 891ndash898

Gnerre S I MacCallum D Przybylski F J Ribeiro J N Burtonet al 2011 High-quality draft assemblies of mammalian ge-nomes from massively parallel sequence data Proc Natl AcadSci USA 108(4) 1513ndash1518

Kim U-J H Shizuya P J de Jong B Barren and M I Simon1992 Stable propagation of cosmid-sized human DNA insertsin an F factor based vector Nucleic Acids Res 20 1083ndash1085

Kovach A J L Wegrzyn G Parra C Holt G E Bruening et al2010 The Pinus taeda genome is characterized by diverse andhighly diverged repetitive sequences BMC Genomics 11(1) 420

Kurtz S A Phillippy A L Delcher M Smoot M Shumway et al2004 Versatile and open software for comparing large ge-nomes Genome Biol 5(2) R12

Lander E S and M S Waterman 1988 Genomic mapping byfingerprinting random clones a mathematical analysis Ge-nomics 2(3) 231ndash239

Li R H Zhu J Ruan W Qian X Fang et al 2010 De novoassembly of human genomes with massively parallel short readsequencing Genome Res 20(2) 265ndash272

Luo R B Liu Y Xie Z Li W Huang et al 2012 SOAPdenovo2an empirically improved memory-efficient short-read de novoassembler GigaScience DOI1011862047-217X-1-18

Mackay J J F Dean C Plomion D G Peterson F M Caacutenovaset al 2012 Towards decoding the conifer giga-genome PlantMol Biol 80 555ndash569

Magallon S and M J Sanderson 2005 Angiosperm divergencetimes the effects of genes codon positions and time con-straints Evolution 59 1653ndash1670

Marccedilais G and C Kingsford 2011 A fast lock-free approach forefficient parallel counting of occurrences of k-mers Bioinfor-matics 27(6) 764ndash770

Marcais G J Yorke and A Zimin 2013 QuorUM an error cor-rector for Illumina reads arXiv13073515 [q-bioGN]

Martin M 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet J 17(1) 10

McKeand S T Mullin T Byram and T White 2003 Deploymentof genetically improved loblolly and slash pines in the south J For101(3) 32ndash37

Miller J R A L Delcher S Koren E Venter B P Walenz et al2008 Aggressive assembly of pyrosequencing reads withmates Bioinformatics 24(24) 2818ndash2824

Mirov N T 1967 The Genus Pinus Ronald Press New YorkMorse A M D G Peterson M N Islam-Faridi K E Smith Z

Magbanua et al 2009 Evolution of genome size and complex-ity in Pinus PLoS ONE 4(2) e4332

Neale D B and A Kremer 2011 Forest tree genomics growingresources and applications Nat Rev Genet 12(2) 111ndash122

Sequencing and Assembling P taeda 885

Additional scaffolding procedures for final assembly

To produce the final assembly (version 101) from the outputof MaSuRCA (version 10) we employed two additionalstrategies that improved the length and accuracy of some ofthe scaffolds First we ran an independent WGS assemblyusing SOAPdenovo2 including its gap-closing module (Luoet al 2012) We used all haploid paired-end data for the contigassembly and added the mate pair and DiTag libraries for thescaffolding step only Following sequencing and quality con-trol the adapter sequence was removed from reads using cuta-dapt (Martin 2011) and the input data were filtered toremove sequences similar to the chloroplast reference se-quence (Parks et al 2009) After error correction 1144 billionreads were used for contig assembly and 1198 billion reads forscaffolding We used a k-mer length of 79 for contig assembly

Although the SOAPdenovo2 contigs are much smallerthan in the 10 assembly with an N50 size of just 687 bp thescaffolds are larger with an N50 size of 547 kbp Thereforewe ran the SOAPdenovo2 scaffolder again using the MaSuRCAscaffolds as input ldquocontigsrdquo and using a conservatively filteredset of mate pairs as linking information This produced a newset of 834 million scaffolds with an N50 size of 868 kbp sub-stantially larger than the original value

As discussed above the DiTag sequences prior to MaSURCAprocessing contained a moderate number of nonjunction readpairs (Appendix B Table B3) To improve the assembly fidelityduring this step we broke all newly created intrascaffold links

for which all supporting mates originated from a DiTag libraryThe resulting modified assembly had 144 million scaffoldswith an N50 size of 646 kbp still over twice as large as theoriginal scaffold N50 size

We used transcripts assembled de novo from RNA-seqreads and predicted to encode full-length proteins (see NationalCenter for Biotechnology Information BioProject 174450) toidentify scaffolds that could be joined or rearranged to becongruent with transcripts Because transcripts can be assem-bled erroneously we followed a conservative strategy that pri-marily linked together scaffolds that contained a single gene

We aligned the 87241 assembled transcripts to all scaffoldsin the modified assembly described above using the nucmerprogram in the MUMmer package (Kurtz et al 2004) andcollecting only those transcripts with at least two exactmatches of 40 bp or longer This yielded 68497 transcriptsmapping to 47492 different scaffolds

Alignments between transcripts and the WGS assemblywere checked for consistency looking for instances wherethe scaffold would require an inversion or a translocation tomake it align We found 1348 inconsistencies 2 of thetotal These could represent errors in either the de novotranscriptome assembly or the WGS assembly and they willbe investigated and corrected if necessary in future assemblyreleases

We scanned the alignments looking for transcripts thatspanned two distinct scaffolds For each transcript we sorted

Figure 5 Plot of k-mer counts in the haploid paired-end WGS sequence After counting all 24- and 31-mers in the QuORUM error-corrected reads weplotted the number of distinct k-mers (y-axis) that occur exactly X times (x-axis) For our haploid data each plot has a single primary peak at the X-valuecorresponding to the depth of coverage of the 1N genome

Sequencing and Assembling P taeda 883

the scaffolds aligned to it and created a directed link betweeneach pair of scaffolds The links were weighted using the sumof the alignment lengths If multiple transcripts aligned to thesame pair of scaffolds the link between them was given thesum of all the weights (This can happen when for exampletwo transcripts represent alternative splice variants of the samegene) We rebuilt the scaffolds using these new links in orderof priority from strongest (greatest weight) to weakest checkingfor circular links at each step and discarding them

This transcript-based scaffolding step linked together31231 scaffolds into 9170 larger scaffolds slightly increas-ing the N50 size and substantially improving the number oftranscripts that align to a single scaffold The final assemblyversion 101 has 144 million scaffolds spanning 232 GbpExcluding gaps the total size of all contigs is 2015 Gbp Thelargest scaffold is 8891046 bp The scaffold N50 size is66920 bp based on a 22 Gbp total genome size (Table 3)

Assessing assembly contiguity and completeness

We used the CEGMA pipeline (Parra et al 2007) to assessthe contiguity of both the V10 and V101 assemblies withrespect to a set of highly conserved eukaryotic genes CEGMAuses a set of conserved protein families from the Clusters ofOrthologous Groups database (Tatusov et al 2003) to an-notate a genome by constructing DNA-protein alignmentsbetween the proteins and the assembled scaffolds Of 248highly conserved protein families CEGMA found full-lengthalignments to the V10 assembly for 113 (45) Includingpartial alignments brings this total to 197 (79) In theV101 assembly CEGMA found 185 (75) full-length align-ments and 203 (82) full-length and partial alignmentsThere was little change in the total number of alignmentsbetween V10 and V101 However the fraction of all align-ments to the assembly that were full length increased from57 in the V10 assembly to 91 in the V101 assemblyfurther quantifying the biological utility of the additionalscaffolding phase There are a number of possible explanationsfor the discrepancy between the coverage and the observedrates of conserved gene annotations principally assemblyfragmentation and potential ascertainment bias in the de-termination of the gene set

Comparison of fosmid and whole-genome assemblies

For validation purposes we constructed sequenced andassembled a large pool of 4600 fosmid clones (Appendix A)The assembly contained 3798 contigs longer than 20000 bp

(50 of the fosmid insert in each case) which we used tocheck the completeness and quality of the WGS assembly Be-cause the fosmids were selected at random the amount ofsequence covered by the WGS assembly should provide anestimate of how much of the entire genome has been capturedin that assembly The 3798 contigs contain 109 Mbp or05of the entire genome

We aligned contigs to the WGS assembly (V10V101contigs) using MUMmer (Kurtz et al 2004) and found thatall but 4 of the 3798 contigs aligned across nearly theirentire length at an average identity of 99 Nearly all(9863) of the combined length of the fosmid pool contigsis covered by alignments to the WGS assembly The un-aligned fraction consists of the 4 contigs that failed to align(117681 bp) plus the unaligned portions of the remainingcontigs Assuming that the fosmids represent a random sam-ple this suggests that the WGS assembly covers 98 ofthe whole genome (with the reservation that hard-to-sequence regions might be missed by both the WGS andfosmid sequencing approaches and thus not counted in thisevaluation)

Fosmid haplotype comparisons

We expect that half of the fosmid contigs would correspondto the same haplotype as the aligned WGS contig Thuswhile many contigs should be close to 100 identical contigsfrom the alternative haplotype (untransmitted) would showa divergence rate corresponding to the differences betweenhaplotypes estimated to be 1ndash2 We considered the pos-sibility that the fosmid contigs would thus fall into two dis-tinct bins near-identical and 1ndash2 divergent Howeverrelatively few contigs showed this degree of divergence2120 contigs (56) were 995 or more identical and theothers matched the WGS contigs at a range of identities withthe vast majority 98 identical (Appendix A Figure A1)Only 189 contigs (5) were 95 identical with the lowestidentity at 91 This suggests that divergence between thetwo parental haplotypes is 1 on average with a smallnumber of highly divergent regions

Conclusions

The sequencing and assembly strategy described here ofthe largest genome to date resulted in a haploid assemblycomposed of 2015 billion base pairs By many measures itis the most contiguous and complete draft assembly ofa conifer genome (Appendix C) This project required a close

Table 3 Comparison of P taeda whole-genome assemblies before and after additional scaffolding

P taeda 10 (before) P taeda 101 (after)

Total sequence in contigs (bp) 20148103497 20148103497Total span of scaffolds (bp) 22564679219 23180477227N50 contig size (bp) 8206 8206N50 scaffold size (bp) 30681 66920No of contigs 500 bp 4047642 4047642No of scaffolds 500 bp 2319749 2158326

The contigs are the same for both assemblies N50 statistics use a genome size of 22 3 109

884 A Zimin et al

coupling between sequencing and assembly strategy and thedevelopment of new computational methods to address thechallenges inherent to the assembly of mega-genomes

Conifer genomes are filled with massive amounts ofrepetitive DNA mostly transposable elements (Nystedt et al2013 Wegrzyn et al 2013 2014) which might have pre-sented a major barrier to successful genome assembly Fortu-nately most of these repeats are relatively ancient so theiraccumulated sequence differences allowed the assembly algo-rithms to distinguish individual copies

A major concern in assembly of diploid genomes is thedegree of divergence between the two parental copies ofeach chromosome Even small differences between the chro-mosomes can cause an assembler to construct two distinctcontigs which can be indistinguishable from two-copy repeatsequences Conifers offer an elegant biological solution to thisproblem by producing megagametophytes with a single copyof each chromosome from which sufficient DNA can beextracted to cover the entire genome

While the current assembly represents an important land-mark and will serve as a powerful resource for biologicalanalyses it remains incomplete To further increase sequencecontiguity we plan to generate additional long-range pairedreads particularly DiTag pairs that should substantiallyincrease scaffold lengths Noting the success of the smallerfosmid pool assemblies presented here inexpensive sequenc-ing allows for the possibility of sequencing many more of thesepools and merging the long contigs from these assemblies intothe WGS assembly (Zhang et al 2012 Nystedt et al 2013) Inparallel with efforts to improve the next P taeda referencesequence we are gathering a large set of genetically mappedSNPs that will allow placement of scaffolds onto the 12 chro-mosomes providing additional concrete physical anchors tothe assembly Finally we note that ongoing efforts to expandthe diversity of conifer genome reference sequences will pro-vide a solid foundation for comparative genomics thus improv-ing the assemblies and their annotations

Acknowledgments

This work was supported in part by US Department ofAgricultureNational Institute of Food and Agriculture(2011-67009-30030) We wish to thank C Dana Nelson atthe USDA Forest Service Southern Research Station forproviding and verifying the genotype of target tree material

Note added in proof See Wegrzyn et al 2014 (pp891ndash909) in this issue for a related work

Literature Cited

Ahuja M R and D B Neale 2005 Evolution of genome size inconifers Silvae Genet 54 126ndash137

Bai C W S Alverson A Follansbee and D M Waller 2012 Newreports of nuclear DNA content for 407 vascular plant taxa fromthe United States Ann Bot (Lond) 110 1623ndash1629

Bierhorst D W 1971 Morphology of Vascular Plants MacmillanNew York

Birol I A Raymond S D Jackman S Pleasance R Coope et al2013 Assembling the 20 Gb white spruce (Picea glauca) ge-nome from whole-genome shotgun sequencing data Bioinfor-matics 29 1492ndash1497

Bowe L M G Coat and C W Depamphilis 2000 Phylogeny ofseed plants based on all three genomic compartments extantgymnosperms are monophyletic and Gnetalesrsquo closest relativesare conifers Proc Natl Acad Sci USA 97 4092ndash4097

Daley T and A D Smith 2013 Predicting the molecular com-plexity of sequencing libraries Nat Methods 10 325ndash327

Frederick W J S J Lien C E Courchene N A DeMartini A JRagauskas et al 2008 Production of ethanol from carbohy-drates from loblolly pine a technical and economic assessmentBioresour Technol 99 5051ndash5057

Fuchs J G Jovtchev and I Schubert 2008 The chromosomaldistribution of histone methylation marks in gymnosperms dif-fers from that of angiosperms Chromosome Res 16 891ndash898

Gnerre S I MacCallum D Przybylski F J Ribeiro J N Burtonet al 2011 High-quality draft assemblies of mammalian ge-nomes from massively parallel sequence data Proc Natl AcadSci USA 108(4) 1513ndash1518

Kim U-J H Shizuya P J de Jong B Barren and M I Simon1992 Stable propagation of cosmid-sized human DNA insertsin an F factor based vector Nucleic Acids Res 20 1083ndash1085

Kovach A J L Wegrzyn G Parra C Holt G E Bruening et al2010 The Pinus taeda genome is characterized by diverse andhighly diverged repetitive sequences BMC Genomics 11(1) 420

Kurtz S A Phillippy A L Delcher M Smoot M Shumway et al2004 Versatile and open software for comparing large ge-nomes Genome Biol 5(2) R12

Lander E S and M S Waterman 1988 Genomic mapping byfingerprinting random clones a mathematical analysis Ge-nomics 2(3) 231ndash239

Li R H Zhu J Ruan W Qian X Fang et al 2010 De novoassembly of human genomes with massively parallel short readsequencing Genome Res 20(2) 265ndash272

Luo R B Liu Y Xie Z Li W Huang et al 2012 SOAPdenovo2an empirically improved memory-efficient short-read de novoassembler GigaScience DOI1011862047-217X-1-18

Mackay J J F Dean C Plomion D G Peterson F M Caacutenovaset al 2012 Towards decoding the conifer giga-genome PlantMol Biol 80 555ndash569

Magallon S and M J Sanderson 2005 Angiosperm divergencetimes the effects of genes codon positions and time con-straints Evolution 59 1653ndash1670

Marccedilais G and C Kingsford 2011 A fast lock-free approach forefficient parallel counting of occurrences of k-mers Bioinfor-matics 27(6) 764ndash770

Marcais G J Yorke and A Zimin 2013 QuorUM an error cor-rector for Illumina reads arXiv13073515 [q-bioGN]

Martin M 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet J 17(1) 10

McKeand S T Mullin T Byram and T White 2003 Deploymentof genetically improved loblolly and slash pines in the south J For101(3) 32ndash37

Miller J R A L Delcher S Koren E Venter B P Walenz et al2008 Aggressive assembly of pyrosequencing reads withmates Bioinformatics 24(24) 2818ndash2824

Mirov N T 1967 The Genus Pinus Ronald Press New YorkMorse A M D G Peterson M N Islam-Faridi K E Smith Z

Magbanua et al 2009 Evolution of genome size and complex-ity in Pinus PLoS ONE 4(2) e4332

Neale D B and A Kremer 2011 Forest tree genomics growingresources and applications Nat Rev Genet 12(2) 111ndash122

Sequencing and Assembling P taeda 885

the scaffolds aligned to it and created a directed link betweeneach pair of scaffolds The links were weighted using the sumof the alignment lengths If multiple transcripts aligned to thesame pair of scaffolds the link between them was given thesum of all the weights (This can happen when for exampletwo transcripts represent alternative splice variants of the samegene) We rebuilt the scaffolds using these new links in orderof priority from strongest (greatest weight) to weakest checkingfor circular links at each step and discarding them

This transcript-based scaffolding step linked together31231 scaffolds into 9170 larger scaffolds slightly increas-ing the N50 size and substantially improving the number oftranscripts that align to a single scaffold The final assemblyversion 101 has 144 million scaffolds spanning 232 GbpExcluding gaps the total size of all contigs is 2015 Gbp Thelargest scaffold is 8891046 bp The scaffold N50 size is66920 bp based on a 22 Gbp total genome size (Table 3)

Assessing assembly contiguity and completeness

We used the CEGMA pipeline (Parra et al 2007) to assessthe contiguity of both the V10 and V101 assemblies withrespect to a set of highly conserved eukaryotic genes CEGMAuses a set of conserved protein families from the Clusters ofOrthologous Groups database (Tatusov et al 2003) to an-notate a genome by constructing DNA-protein alignmentsbetween the proteins and the assembled scaffolds Of 248highly conserved protein families CEGMA found full-lengthalignments to the V10 assembly for 113 (45) Includingpartial alignments brings this total to 197 (79) In theV101 assembly CEGMA found 185 (75) full-length align-ments and 203 (82) full-length and partial alignmentsThere was little change in the total number of alignmentsbetween V10 and V101 However the fraction of all align-ments to the assembly that were full length increased from57 in the V10 assembly to 91 in the V101 assemblyfurther quantifying the biological utility of the additionalscaffolding phase There are a number of possible explanationsfor the discrepancy between the coverage and the observedrates of conserved gene annotations principally assemblyfragmentation and potential ascertainment bias in the de-termination of the gene set

Comparison of fosmid and whole-genome assemblies

For validation purposes we constructed sequenced andassembled a large pool of 4600 fosmid clones (Appendix A)The assembly contained 3798 contigs longer than 20000 bp

(50 of the fosmid insert in each case) which we used tocheck the completeness and quality of the WGS assembly Be-cause the fosmids were selected at random the amount ofsequence covered by the WGS assembly should provide anestimate of how much of the entire genome has been capturedin that assembly The 3798 contigs contain 109 Mbp or05of the entire genome

We aligned contigs to the WGS assembly (V10V101contigs) using MUMmer (Kurtz et al 2004) and found thatall but 4 of the 3798 contigs aligned across nearly theirentire length at an average identity of 99 Nearly all(9863) of the combined length of the fosmid pool contigsis covered by alignments to the WGS assembly The un-aligned fraction consists of the 4 contigs that failed to align(117681 bp) plus the unaligned portions of the remainingcontigs Assuming that the fosmids represent a random sam-ple this suggests that the WGS assembly covers 98 ofthe whole genome (with the reservation that hard-to-sequence regions might be missed by both the WGS andfosmid sequencing approaches and thus not counted in thisevaluation)

Fosmid haplotype comparisons

We expect that half of the fosmid contigs would correspondto the same haplotype as the aligned WGS contig Thuswhile many contigs should be close to 100 identical contigsfrom the alternative haplotype (untransmitted) would showa divergence rate corresponding to the differences betweenhaplotypes estimated to be 1ndash2 We considered the pos-sibility that the fosmid contigs would thus fall into two dis-tinct bins near-identical and 1ndash2 divergent Howeverrelatively few contigs showed this degree of divergence2120 contigs (56) were 995 or more identical and theothers matched the WGS contigs at a range of identities withthe vast majority 98 identical (Appendix A Figure A1)Only 189 contigs (5) were 95 identical with the lowestidentity at 91 This suggests that divergence between thetwo parental haplotypes is 1 on average with a smallnumber of highly divergent regions

Conclusions

The sequencing and assembly strategy described here ofthe largest genome to date resulted in a haploid assemblycomposed of 2015 billion base pairs By many measures itis the most contiguous and complete draft assembly ofa conifer genome (Appendix C) This project required a close

Table 3 Comparison of P taeda whole-genome assemblies before and after additional scaffolding

P taeda 10 (before) P taeda 101 (after)

Total sequence in contigs (bp) 20148103497 20148103497Total span of scaffolds (bp) 22564679219 23180477227N50 contig size (bp) 8206 8206N50 scaffold size (bp) 30681 66920No of contigs 500 bp 4047642 4047642No of scaffolds 500 bp 2319749 2158326

The contigs are the same for both assemblies N50 statistics use a genome size of 22 3 109

884 A Zimin et al

coupling between sequencing and assembly strategy and thedevelopment of new computational methods to address thechallenges inherent to the assembly of mega-genomes

Conifer genomes are filled with massive amounts ofrepetitive DNA mostly transposable elements (Nystedt et al2013 Wegrzyn et al 2013 2014) which might have pre-sented a major barrier to successful genome assembly Fortu-nately most of these repeats are relatively ancient so theiraccumulated sequence differences allowed the assembly algo-rithms to distinguish individual copies

A major concern in assembly of diploid genomes is thedegree of divergence between the two parental copies ofeach chromosome Even small differences between the chro-mosomes can cause an assembler to construct two distinctcontigs which can be indistinguishable from two-copy repeatsequences Conifers offer an elegant biological solution to thisproblem by producing megagametophytes with a single copyof each chromosome from which sufficient DNA can beextracted to cover the entire genome

While the current assembly represents an important land-mark and will serve as a powerful resource for biologicalanalyses it remains incomplete To further increase sequencecontiguity we plan to generate additional long-range pairedreads particularly DiTag pairs that should substantiallyincrease scaffold lengths Noting the success of the smallerfosmid pool assemblies presented here inexpensive sequenc-ing allows for the possibility of sequencing many more of thesepools and merging the long contigs from these assemblies intothe WGS assembly (Zhang et al 2012 Nystedt et al 2013) Inparallel with efforts to improve the next P taeda referencesequence we are gathering a large set of genetically mappedSNPs that will allow placement of scaffolds onto the 12 chro-mosomes providing additional concrete physical anchors tothe assembly Finally we note that ongoing efforts to expandthe diversity of conifer genome reference sequences will pro-vide a solid foundation for comparative genomics thus improv-ing the assemblies and their annotations

Acknowledgments

This work was supported in part by US Department ofAgricultureNational Institute of Food and Agriculture(2011-67009-30030) We wish to thank C Dana Nelson atthe USDA Forest Service Southern Research Station forproviding and verifying the genotype of target tree material

Note added in proof See Wegrzyn et al 2014 (pp891ndash909) in this issue for a related work

Literature Cited

Ahuja M R and D B Neale 2005 Evolution of genome size inconifers Silvae Genet 54 126ndash137

Bai C W S Alverson A Follansbee and D M Waller 2012 Newreports of nuclear DNA content for 407 vascular plant taxa fromthe United States Ann Bot (Lond) 110 1623ndash1629

Bierhorst D W 1971 Morphology of Vascular Plants MacmillanNew York

Birol I A Raymond S D Jackman S Pleasance R Coope et al2013 Assembling the 20 Gb white spruce (Picea glauca) ge-nome from whole-genome shotgun sequencing data Bioinfor-matics 29 1492ndash1497

Bowe L M G Coat and C W Depamphilis 2000 Phylogeny ofseed plants based on all three genomic compartments extantgymnosperms are monophyletic and Gnetalesrsquo closest relativesare conifers Proc Natl Acad Sci USA 97 4092ndash4097

Daley T and A D Smith 2013 Predicting the molecular com-plexity of sequencing libraries Nat Methods 10 325ndash327

Frederick W J S J Lien C E Courchene N A DeMartini A JRagauskas et al 2008 Production of ethanol from carbohy-drates from loblolly pine a technical and economic assessmentBioresour Technol 99 5051ndash5057

Fuchs J G Jovtchev and I Schubert 2008 The chromosomaldistribution of histone methylation marks in gymnosperms dif-fers from that of angiosperms Chromosome Res 16 891ndash898

Gnerre S I MacCallum D Przybylski F J Ribeiro J N Burtonet al 2011 High-quality draft assemblies of mammalian ge-nomes from massively parallel sequence data Proc Natl AcadSci USA 108(4) 1513ndash1518

Kim U-J H Shizuya P J de Jong B Barren and M I Simon1992 Stable propagation of cosmid-sized human DNA insertsin an F factor based vector Nucleic Acids Res 20 1083ndash1085

Kovach A J L Wegrzyn G Parra C Holt G E Bruening et al2010 The Pinus taeda genome is characterized by diverse andhighly diverged repetitive sequences BMC Genomics 11(1) 420

Kurtz S A Phillippy A L Delcher M Smoot M Shumway et al2004 Versatile and open software for comparing large ge-nomes Genome Biol 5(2) R12

Lander E S and M S Waterman 1988 Genomic mapping byfingerprinting random clones a mathematical analysis Ge-nomics 2(3) 231ndash239

Li R H Zhu J Ruan W Qian X Fang et al 2010 De novoassembly of human genomes with massively parallel short readsequencing Genome Res 20(2) 265ndash272

Luo R B Liu Y Xie Z Li W Huang et al 2012 SOAPdenovo2an empirically improved memory-efficient short-read de novoassembler GigaScience DOI1011862047-217X-1-18

Mackay J J F Dean C Plomion D G Peterson F M Caacutenovaset al 2012 Towards decoding the conifer giga-genome PlantMol Biol 80 555ndash569

Magallon S and M J Sanderson 2005 Angiosperm divergencetimes the effects of genes codon positions and time con-straints Evolution 59 1653ndash1670

Marccedilais G and C Kingsford 2011 A fast lock-free approach forefficient parallel counting of occurrences of k-mers Bioinfor-matics 27(6) 764ndash770

Marcais G J Yorke and A Zimin 2013 QuorUM an error cor-rector for Illumina reads arXiv13073515 [q-bioGN]

Martin M 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet J 17(1) 10

McKeand S T Mullin T Byram and T White 2003 Deploymentof genetically improved loblolly and slash pines in the south J For101(3) 32ndash37

Miller J R A L Delcher S Koren E Venter B P Walenz et al2008 Aggressive assembly of pyrosequencing reads withmates Bioinformatics 24(24) 2818ndash2824

Mirov N T 1967 The Genus Pinus Ronald Press New YorkMorse A M D G Peterson M N Islam-Faridi K E Smith Z

Magbanua et al 2009 Evolution of genome size and complex-ity in Pinus PLoS ONE 4(2) e4332

Neale D B and A Kremer 2011 Forest tree genomics growingresources and applications Nat Rev Genet 12(2) 111ndash122

Sequencing and Assembling P taeda 885

coupling between sequencing and assembly strategy and thedevelopment of new computational methods to address thechallenges inherent to the assembly of mega-genomes

Conifer genomes are filled with massive amounts ofrepetitive DNA mostly transposable elements (Nystedt et al2013 Wegrzyn et al 2013 2014) which might have pre-sented a major barrier to successful genome assembly Fortu-nately most of these repeats are relatively ancient so theiraccumulated sequence differences allowed the assembly algo-rithms to distinguish individual copies

A major concern in assembly of diploid genomes is thedegree of divergence between the two parental copies ofeach chromosome Even small differences between the chro-mosomes can cause an assembler to construct two distinctcontigs which can be indistinguishable from two-copy repeatsequences Conifers offer an elegant biological solution to thisproblem by producing megagametophytes with a single copyof each chromosome from which sufficient DNA can beextracted to cover the entire genome

While the current assembly represents an important land-mark and will serve as a powerful resource for biologicalanalyses it remains incomplete To further increase sequencecontiguity we plan to generate additional long-range pairedreads particularly DiTag pairs that should substantiallyincrease scaffold lengths Noting the success of the smallerfosmid pool assemblies presented here inexpensive sequenc-ing allows for the possibility of sequencing many more of thesepools and merging the long contigs from these assemblies intothe WGS assembly (Zhang et al 2012 Nystedt et al 2013) Inparallel with efforts to improve the next P taeda referencesequence we are gathering a large set of genetically mappedSNPs that will allow placement of scaffolds onto the 12 chro-mosomes providing additional concrete physical anchors tothe assembly Finally we note that ongoing efforts to expandthe diversity of conifer genome reference sequences will pro-vide a solid foundation for comparative genomics thus improv-ing the assemblies and their annotations

Acknowledgments

This work was supported in part by US Department ofAgricultureNational Institute of Food and Agriculture(2011-67009-30030) We wish to thank C Dana Nelson atthe USDA Forest Service Southern Research Station forproviding and verifying the genotype of target tree material

Note added in proof See Wegrzyn et al 2014 (pp891ndash909) in this issue for a related work

Literature Cited

Ahuja M R and D B Neale 2005 Evolution of genome size inconifers Silvae Genet 54 126ndash137

Bai C W S Alverson A Follansbee and D M Waller 2012 Newreports of nuclear DNA content for 407 vascular plant taxa fromthe United States Ann Bot (Lond) 110 1623ndash1629

Bierhorst D W 1971 Morphology of Vascular Plants MacmillanNew York

Birol I A Raymond S D Jackman S Pleasance R Coope et al2013 Assembling the 20 Gb white spruce (Picea glauca) ge-nome from whole-genome shotgun sequencing data Bioinfor-matics 29 1492ndash1497

Bowe L M G Coat and C W Depamphilis 2000 Phylogeny ofseed plants based on all three genomic compartments extantgymnosperms are monophyletic and Gnetalesrsquo closest relativesare conifers Proc Natl Acad Sci USA 97 4092ndash4097

Daley T and A D Smith 2013 Predicting the molecular com-plexity of sequencing libraries Nat Methods 10 325ndash327

Frederick W J S J Lien C E Courchene N A DeMartini A JRagauskas et al 2008 Production of ethanol from carbohy-drates from loblolly pine a technical and economic assessmentBioresour Technol 99 5051ndash5057

Fuchs J G Jovtchev and I Schubert 2008 The chromosomaldistribution of histone methylation marks in gymnosperms dif-fers from that of angiosperms Chromosome Res 16 891ndash898

Gnerre S I MacCallum D Przybylski F J Ribeiro J N Burtonet al 2011 High-quality draft assemblies of mammalian ge-nomes from massively parallel sequence data Proc Natl AcadSci USA 108(4) 1513ndash1518

Kim U-J H Shizuya P J de Jong B Barren and M I Simon1992 Stable propagation of cosmid-sized human DNA insertsin an F factor based vector Nucleic Acids Res 20 1083ndash1085

Kovach A J L Wegrzyn G Parra C Holt G E Bruening et al2010 The Pinus taeda genome is characterized by diverse andhighly diverged repetitive sequences BMC Genomics 11(1) 420

Kurtz S A Phillippy A L Delcher M Smoot M Shumway et al2004 Versatile and open software for comparing large ge-nomes Genome Biol 5(2) R12

Lander E S and M S Waterman 1988 Genomic mapping byfingerprinting random clones a mathematical analysis Ge-nomics 2(3) 231ndash239

Li R H Zhu J Ruan W Qian X Fang et al 2010 De novoassembly of human genomes with massively parallel short readsequencing Genome Res 20(2) 265ndash272

Luo R B Liu Y Xie Z Li W Huang et al 2012 SOAPdenovo2an empirically improved memory-efficient short-read de novoassembler GigaScience DOI1011862047-217X-1-18

Mackay J J F Dean C Plomion D G Peterson F M Caacutenovaset al 2012 Towards decoding the conifer giga-genome PlantMol Biol 80 555ndash569

Magallon S and M J Sanderson 2005 Angiosperm divergencetimes the effects of genes codon positions and time con-straints Evolution 59 1653ndash1670

Marccedilais G and C Kingsford 2011 A fast lock-free approach forefficient parallel counting of occurrences of k-mers Bioinfor-matics 27(6) 764ndash770

Marcais G J Yorke and A Zimin 2013 QuorUM an error cor-rector for Illumina reads arXiv13073515 [q-bioGN]

Martin M 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet J 17(1) 10

McKeand S T Mullin T Byram and T White 2003 Deploymentof genetically improved loblolly and slash pines in the south J For101(3) 32ndash37

Miller J R A L Delcher S Koren E Venter B P Walenz et al2008 Aggressive assembly of pyrosequencing reads withmates Bioinformatics 24(24) 2818ndash2824

Mirov N T 1967 The Genus Pinus Ronald Press New YorkMorse A M D G Peterson M N Islam-Faridi K E Smith Z

Magbanua et al 2009 Evolution of genome size and complex-ity in Pinus PLoS ONE 4(2) e4332

Neale D B and A Kremer 2011 Forest tree genomics growingresources and applications Nat Rev Genet 12(2) 111ndash122

Sequencing and Assembling P taeda 885

Neale D B J L Wegrzyn K A Stevens A V Zimin D Puiu et al2014 Decoding the massive genome of loblolly pine using hap-loid DNA and novel assembly strategies Genome Biol 15 R59

Nystedt B N R Street A Wetterbom A Zuccolo Y Lin et al2013 The Norway spruce genome sequence and conifer ge-nome evolution Nature 497 579ndash584

OrsquoBrien I E W D R Smith R C Gardner and B G Murray1996 Flow cytometric determination of genome size in PinusPlant Sci 115 91ndash99

Parks M R Cronn and A Liston 2009 Increasing phylogeneticresolution at low taxonomic levels using massively parallel se-quencing of chloroplast genomes BMC Biol 7(1) 84

Parra G K Bradnam and I Korf 2007 CEGMA a pipeline toaccurately annotate core genes in eukaryotic genomes Bioinfor-matics 23(9) 1061ndash1067

Peterson D G S R Schulze E B Sciara S A Lee J E Bowerset al 2002 Integration of Cot analysis DNA cloning and high-throughput sequencing facilitates genome characterization andgene discovery Genome Res 12 795ndash807

Peterson D G J P Tomkins D A Frisch R A Wing and A HPaterson 2000 Construction of plant bacterial artificial chro-mosome (BAC) libraries an illustrated guide J Agric Genomics5 1ndash100

Ross M G C Russ M Costello A Hollinger N J Lennon et al2013 Characterizing and measuring bias in sequence data Ge-nome Biol 14(5) R51

Schatz M C A L Delcher and S L Salzberg 2010 Assembly oflarge genomes using second-generation sequencing GenomeRes 20(9) 1165ndash1173

Schultz R P 1997 Loblolly pine the ecology and culture ofloblolly pine (Pinus taeda L) in Agriculture Handbook Wash-ington (713) US Department of Agriculture Forest ServiceWashington DC

Shizuya H B Birren U-J Kim V Mancino T Slepak et al1992 Cloning and stable maintenance of 300-kilobase-pairfragments of human DNA in Escherichia coli using an F-factor-based vector Proc Natl Acad Sci USA 89(18) 8794ndash8797

Tatusov R L N D Fedorova J D Jackson A R Jacobs BKiryutin et al 2003 The COG database an updated versionincludes eukaryotes BMC Bioinformatics 4(1) 41

Wang N M Thomson W J A Bodles R M M Crawford H VHunt et al 2013 Genome sequence of dwarf birch (Betulanana) and cross‐species RAD markers Mol Ecol 22 3098ndash3111

Wegrzyn J B Y Lin J J Zieve W M Dougherty P J Martiacutenez-Garciacutea et al 2013 Insights into the loblolly pine genomecharacterization of BAC and fosmid sequences PLoS ONE 8e72439

Wegrzyn J L J D Liechty K A Stevens L Wu C A Loopstraet al 2014 Unique Features of the Loblolly Pine (Pinus taedaL) Megagenome Revealed Through Sequence Annotation Ge-netics 196 891ndash909

Williams L J S D G Tabbaa N Li A M Berlin T P Shea et al2012 Paired-end sequencing of Fosmid libraries by IlluminaGenome Res 22(11) 2241ndash2249

Zhang G X Fang X Guo L Li R Luo et al 2012 The oystergenome reveals stress adaptation and complexity of shell forma-tion Nature 490(7418) 49ndash54

Zimin A V G Marccedilais D Puiu M Roberts S L Salzberg et al2013 The MaSuRCA genome assembler Bioinformatics 292669ndash2677

Zonneveld B J M 2012 Genome sizes of 172 species covering64 out of the 67 genera range from 8 to 72 picogram Nord JBot 30 490ndash502

Communicating editor M Johnston

886 A Zimin et al

Appendix A Fosmid Pool Sequencing and Assembly for Validation

Fosmid Pool Construction

Independently of the whole-genome shotgun libraries a library of fosmid particles was constructed in the vector pFosTHpFosTH is derived from pFosDT54 by replacing the cloning site region including the Illumina primers and NbBbvCl sites aspresent on a small PI-SceI fragment with a synthetic DNA fragment consisting of the triple-helix motif sites 1 and 2 flanking theunique Eco47-III cloning site Extracted genomic DNA from diploid needle tissue was sheared to an average size of 40 kbpconverted to blunt ends and size-purified by pulsed-field electrophoresis This was followed by ligation to excess vector (Eco47-III digested dephosphorylated ends) and l-phage packaging (extracts from mcrA -B -C strains) to create a particle library (seeFigure 2) The particle library was then titered portioned and transduced into E coli After 1 hr of recovery the cells were frozenand stored as glycerol stocks directly from the liquid media to minimize duplication This procedure resulted in a primary libraryof7 million E coli colony forming units each containing a single type of fosmid Smaller subpools of fosmids were also createdAfter titration of the frozen stock specific aliquots of the main pool were streaked on LB chloramphenicol agarose platesincubated to form primary colonies then recovered by scraping off the agarose suspended in media pooled and used toinoculate liquid cultures for DNA amplification and purification The resulting defined (low complexity) DNA samples weresequenced and assembled using independent software for the purposes of validating the WGS assembly

Libraries and Sequencing

From the primary fosmid library described above 11 smaller subpools of 580 6 10 E coli colonies were created Thefosmid DNA of the 11 pools of harvested bacterial colonies was then amplified in vivo Fosmid DNA was subsequentlypurified and digested with the homing endonuclease PI-SceI which has a 35-bp recognition site With the isolated DNAsquantified by PicoGreen the purified insert DNAs were portioned out to create an equal molar super-pool of all 11 compo-nent fosmid pools as well as a set of 3 nested equal molar super-pools of 8 4 and 2 small fosmid pools

The fosmid DNA was then subsequently processed into Illumina libraries using the reagents and protocols as previouslydescribed The largest super pool was converted to an Illumina long-insert mate pair library while the smaller nested superpools were converted into short-insert paired-end libraries Paired-end libraries were subsequently sequenced on an IlluminaGAIIx (SCS version 2935 RTA version 1935) The two smallest fosmid pools were primarily used for calibration purposesand are therefore not considered further

Subsequent ungapped single-seed alignment using Illuminarsquos CASAVA pipeline (version 170) to the E coli and fosmidvector genomes was used to determine insert-size statistics The paired-end library had a mean fragment length of 261 bpwhile the mate pair library had a median fragment length of 3345 bp (Table A1) The reported rates of the noncanonicalpaired-end alignment orientations were also consistent with high-quality libraries

Fosmid Assembly

We cleaned the raw read data by identifying and filtering out contaminating sequence from reads that contained E coli DNA(10 in the short library 176 in the jumping library) fosmid vector (179 in the short library 293 in the jumpinglibrary) or adapter sequence (04 in the short library 12 in the jumping library) After experiments with differentassembly parameters and multiple assemblers we concluded that slightly better assemblies resulted when all reads were

Table A1 Libraries and read statistics for sequences contributing to our largest fosmid pool assembly

Library Pool size (fosmids) Read lengths (bp) Mean fragment length (bp) No of pairs No of pairs after cleaning

Paired-end 4600 6 10 160 156 261 90392267 73325963Mate pair 6400 6 10 160 156 3345 6 151 11978560 8390844

Table A2 Assembly statistics for the largest pool of fosmids

No Sum of lengths Vector on either end

Contigs 20000 bp 3798 109412316 623Contigs 30000 bp 1651 56742427 585Scaffolds 20000 bp 5323 171852787 2001Scaffolds 30000 bp 3719 131752852 1911

The final column shows the number of contigsscaffolds for which one or both ends contained fosmid vector sequence indicating that the contig extended all the way to theend of the insert

Sequencing and Assembling P taeda 887

trimmed to 125 bp thereby removing low-quality bases from the 39 ends of the reads We assembled the fosmid pool withSOAPdenovo (release 1) (Li et al 2010) using a k-mer size of 63

Our largest pool contained 4600 fosmids If the expected size of the loblolly DNA in the fosmids was 36 kbp (seeResults) then the total pool size was 166 Mbp or 1 of the total genome size for loblolly pine Thus we would expect1 of the randomly chosen fosmids to overlap another fosmid in the same pool

The total amount of sequence from this pool was 817 million read pairs (Table A1) which provided 1203 coverage onaverage for each fosmid The resulting pooled assembly showed remarkably high contiguity with 3719 scaffolds spanning30 kbp (80 of the maximum length for a fosmid) and 5323 scaffolds longer than 20 kbp as shown in Table A2

The high level of contiguity for the fosmid pool assembly is likely a result of the haploid nature of the fosmids combinedwith the use of a jumping library containing 35-kbp paired reads

For validation purposes the contigs 20000 bp in Table A2 were aligned against the WGS assembly using MUMmerDetailed results from these alignments are presented above Figure A1 gives additional detail as to the distribution of thepercentage of identity of each fosmid contig aligned to the WGS assembly

Appendix B Additional Library Statistics

Haploid Paired-End Libraries

We sequenced 11 paired-end libraries derived from the DNA of a single megagametophyte using three Illumina platforms(HiSeq2000 GAIIx and MiSeq) Table B1 shows that we obtained 14 Tbp of short-insert paired-end high-qualitysequence corresponding to nearly 64-fold coverage of the loblolly pine genome As expected from the narrow and consistentdistributions in Figure 2 the empirical fragment size coefficient of variation for the 11 haploid paired-end libraries wasuniformly small between 4 and 6

Mate Pair Libraries

We sequenced 48 mate pair libraries with insert sizes ranging from 1300 to 5500 bp Table B2 groups these by insert sizeand summarizes the outcome of the MaSuRCA jumping library filters on these as well as the estimated clone coveragepresented to the downstream overlap assembler

DiTag Libraries

We sequenced nine DiTag libraries in 40 fractional GAIIx lanes yielding 46 million read pairs summarized in Table B3

Figure A1 The percentage of identity of long contigs (20000 bp) from the fosmid pool assembly when aligned to the WGS assembly A total of 109contigs were 100 identical and another 2011 were between 995 and 100 identical

888 A Zimin et al

Table B2 Summary of outcomes from preprocessing stages for long fragment libraries with lengths ranging from 1000 to 5500 bp

Estimated jumpsize (bp)

Librarycount

Readssequenced

After error correctionand mappinga

Nonjunctionpairs

Redundantreads

Final reads withboth 63 bp

Estimatedclone coverage

1000ndash1999 5 1273 853 (67) 56 (7) 97 (12) 421 1532000ndash2999 16 6519 4300 (66) 266 (6) 430 (11) 2074 11933000ndash3999 18 7054 4744 (67) 264 (6) 881 (20) 2132 16234000ndash4999 6 1866 1278 (69) 66 (5) 136 (11) 638 6335000ndash5500 3 553 382 (69) 57 (15) 198 (61) 576 213

Read counts are given in millionsa Reads that successfully passed error correction and mapped to the haplotype of the target megagametophyte

Table B1 Selected statistics for haploid paired-end sequence data by platform and fragment size

Platform

Medianfragmentsize (bp)

SequencedGbp

Error-correctedGbp

Read 1expected

error-correctedlength (bp)

Read 2expected

error-correctedlength (bp)

Expectedgap size (bp)

Gapsfilled ()

Maximalsuper-reads ()

GAIIx 160+156 209 23 22 1570 1513 2993 96 6220 29 28 1571 1513 2884 96 7234 33 31 1576 1497 2733 95 8246 23 22 1578 1517 2635 94 9260 24 23 1578 1509 2487 94 9273 263 252 1575 1500 2345 92 12285 24 23 1578 1514 2242 92 12325 22 21 1575 1493 183 91 15441 19 18 1571 1417 1422 84 18565 15 14 1569 1405 2676 76 22637 240 11 1566 1348 3456 71 23

HiSeq 2x125 273 3260 3154 1238 1220 271 83 9HiSeq 2x128 220 966 929 1265 1241 2306 90 6

234 595 577 1272 1254 2186 87 6246 616 596 1266 1253 259 87 7260 1002 971 1271 1251 223 84 9285 956 917 1264 1232 354 86 10325 923 889 1262 1243 745 80 12441 358 344 1262 1240 1908 73 15565 434 414 1257 1222 3171 68 19

MiSeq 2x255 325 25 238 2482 2388 21620 95 14441 20 185 2475 2322 2386 89 18565 16 144 2469 2469 960 83 23637 12 103 2466 2466 1779 79 24

The percentage of reads that are gap filled is from the total number of reads entering gap fill and the percentage of reads becoming maximal super-reads is from thosepassing error correction As fragment size increases so does the expected gap size Both the error-corrected read length and the rate at which the gaps are successfully filledare decreasing functions of the fragment size Nevertheless the overall trend is that participation in the formation of super-reads increases with insert size Only the paired-end reads that are successfully transformed into maximal super-reads are passed to subsequent assembly steps

Table B3 Summary of DiTag sequencing

LibraryLanes

sequenced

Median readssequenced perlane (millions)

Median reads aftererror correction

and mapping (millions)Median nonjunction

reads (millions)Median redundantreads (millions)

Median finalreads with both63 bp (millions)

Median clonecoverage perlane (sum)

N1 6 25 15 (57) 02 (16) 05 (37) 03 (12) 033 (183)N2 6 18 12 (68) 003 (3) 06 (53) 03 (15) 023 (123)N3 6 09 06 (59) 005 (8) 02 (46) 01 (13) 013 (063)N4 3 15 10 (66) 01 (5) 03 (37) 03 (22) 033 (093)R1 2 41 18 (44) 07 (39) 09 (85) 02 (4) 013 (023)R2 2 29 11 (39) 04 (33) 05 (63) 01 (5) 013 (023)R3 2 57 22 (39) 10 (45) 12 (97) 02 (3) 013 (023)All lanes 40 93 39 (42) 7 (18) 15 (47) 9 (10) (753)

Four nick translation libraries (N1ndashN4) and three endonuclease digestion libraries (R1ndashR3) are detailed Each library was sequenced in replicate in a number of lanes All valuesreported other than the totals are medians To allow for a more direct comparison between libraries median values per lane are used

Sequencing and Assembling P taeda 889

Table C1 Loblolly pine V101 assembly compared to contemporary draft conifer genomes

Species Loblolly pine (Pinus taeda) Norway spruce (Picea abies) White spruce (Picea glauca)

Cytometrically estimated genome size (Gbp) 216a 196b 158c

Total scaffold span (Gbp) 226 123 236Total contig spand(Gbp) 201 120 208Referenced genome-size estimate (Gbp) 22 18 20N50 contig size (kbp) 82 06 54N50 scaffold size (kbp) 669 072 229No of scaffolds 14412985 10253693 7084659Annotation of 248 conserved

CEGMA genes (Parra et al 2007)185 (74) complete 124 (50) complete 95 (38) complete

203 (82) complete + partial91 annotated full length

189 (76) complete + partial66 annotated full length

184 (74) complete + partial52 annotated full length

N50 contig and scaffold sizes are based on the estimated genome size listed in the tablea OrsquoBrien et al (1996)b Fuchs et al (2008)c Bai et al (2012)d Determined as the number of non-N characters in the published reference sequence

Appendix C Conifer Assembly Statistics

The loblolly pine genome (Neale et al 2014) whose sequencing and assembly we described here joins two other conifergenome sequences Norway spruce (Nystedt et al 2013) and white spruce (Birol et al 2013) as a foundation of conifergenomics We report selected assembly statistics from these three genomes for comparison (Table C1 and Figure C1)

Figure C1 The contiguity of the loblolly pine v101 assembly is compared to contemporary draft conifer assemblies Total scaffold span is plottedagainst a minimum scaffold size threshold Loblolly pine is relatively more complete when considering large-gene-sized ( 10 kbp) scaffolds This isreflected in the CEGMA results (Table C1)

890 A Zimin et al

Appendix A Fosmid Pool Sequencing and Assembly for Validation

Fosmid Pool Construction

Independently of the whole-genome shotgun libraries a library of fosmid particles was constructed in the vector pFosTHpFosTH is derived from pFosDT54 by replacing the cloning site region including the Illumina primers and NbBbvCl sites aspresent on a small PI-SceI fragment with a synthetic DNA fragment consisting of the triple-helix motif sites 1 and 2 flanking theunique Eco47-III cloning site Extracted genomic DNA from diploid needle tissue was sheared to an average size of 40 kbpconverted to blunt ends and size-purified by pulsed-field electrophoresis This was followed by ligation to excess vector (Eco47-III digested dephosphorylated ends) and l-phage packaging (extracts from mcrA -B -C strains) to create a particle library (seeFigure 2) The particle library was then titered portioned and transduced into E coli After 1 hr of recovery the cells were frozenand stored as glycerol stocks directly from the liquid media to minimize duplication This procedure resulted in a primary libraryof7 million E coli colony forming units each containing a single type of fosmid Smaller subpools of fosmids were also createdAfter titration of the frozen stock specific aliquots of the main pool were streaked on LB chloramphenicol agarose platesincubated to form primary colonies then recovered by scraping off the agarose suspended in media pooled and used toinoculate liquid cultures for DNA amplification and purification The resulting defined (low complexity) DNA samples weresequenced and assembled using independent software for the purposes of validating the WGS assembly

Libraries and Sequencing

From the primary fosmid library described above 11 smaller subpools of 580 6 10 E coli colonies were created Thefosmid DNA of the 11 pools of harvested bacterial colonies was then amplified in vivo Fosmid DNA was subsequentlypurified and digested with the homing endonuclease PI-SceI which has a 35-bp recognition site With the isolated DNAsquantified by PicoGreen the purified insert DNAs were portioned out to create an equal molar super-pool of all 11 compo-nent fosmid pools as well as a set of 3 nested equal molar super-pools of 8 4 and 2 small fosmid pools

The fosmid DNA was then subsequently processed into Illumina libraries using the reagents and protocols as previouslydescribed The largest super pool was converted to an Illumina long-insert mate pair library while the smaller nested superpools were converted into short-insert paired-end libraries Paired-end libraries were subsequently sequenced on an IlluminaGAIIx (SCS version 2935 RTA version 1935) The two smallest fosmid pools were primarily used for calibration purposesand are therefore not considered further

Subsequent ungapped single-seed alignment using Illuminarsquos CASAVA pipeline (version 170) to the E coli and fosmidvector genomes was used to determine insert-size statistics The paired-end library had a mean fragment length of 261 bpwhile the mate pair library had a median fragment length of 3345 bp (Table A1) The reported rates of the noncanonicalpaired-end alignment orientations were also consistent with high-quality libraries

Fosmid Assembly

We cleaned the raw read data by identifying and filtering out contaminating sequence from reads that contained E coli DNA(10 in the short library 176 in the jumping library) fosmid vector (179 in the short library 293 in the jumpinglibrary) or adapter sequence (04 in the short library 12 in the jumping library) After experiments with differentassembly parameters and multiple assemblers we concluded that slightly better assemblies resulted when all reads were

Table A1 Libraries and read statistics for sequences contributing to our largest fosmid pool assembly

Library Pool size (fosmids) Read lengths (bp) Mean fragment length (bp) No of pairs No of pairs after cleaning

Paired-end 4600 6 10 160 156 261 90392267 73325963Mate pair 6400 6 10 160 156 3345 6 151 11978560 8390844

Table A2 Assembly statistics for the largest pool of fosmids

No Sum of lengths Vector on either end

Contigs 20000 bp 3798 109412316 623Contigs 30000 bp 1651 56742427 585Scaffolds 20000 bp 5323 171852787 2001Scaffolds 30000 bp 3719 131752852 1911

The final column shows the number of contigsscaffolds for which one or both ends contained fosmid vector sequence indicating that the contig extended all the way to theend of the insert

Sequencing and Assembling P taeda 887

trimmed to 125 bp thereby removing low-quality bases from the 39 ends of the reads We assembled the fosmid pool withSOAPdenovo (release 1) (Li et al 2010) using a k-mer size of 63

Our largest pool contained 4600 fosmids If the expected size of the loblolly DNA in the fosmids was 36 kbp (seeResults) then the total pool size was 166 Mbp or 1 of the total genome size for loblolly pine Thus we would expect1 of the randomly chosen fosmids to overlap another fosmid in the same pool

The total amount of sequence from this pool was 817 million read pairs (Table A1) which provided 1203 coverage onaverage for each fosmid The resulting pooled assembly showed remarkably high contiguity with 3719 scaffolds spanning30 kbp (80 of the maximum length for a fosmid) and 5323 scaffolds longer than 20 kbp as shown in Table A2

The high level of contiguity for the fosmid pool assembly is likely a result of the haploid nature of the fosmids combinedwith the use of a jumping library containing 35-kbp paired reads

For validation purposes the contigs 20000 bp in Table A2 were aligned against the WGS assembly using MUMmerDetailed results from these alignments are presented above Figure A1 gives additional detail as to the distribution of thepercentage of identity of each fosmid contig aligned to the WGS assembly

Appendix B Additional Library Statistics

Haploid Paired-End Libraries

We sequenced 11 paired-end libraries derived from the DNA of a single megagametophyte using three Illumina platforms(HiSeq2000 GAIIx and MiSeq) Table B1 shows that we obtained 14 Tbp of short-insert paired-end high-qualitysequence corresponding to nearly 64-fold coverage of the loblolly pine genome As expected from the narrow and consistentdistributions in Figure 2 the empirical fragment size coefficient of variation for the 11 haploid paired-end libraries wasuniformly small between 4 and 6

Mate Pair Libraries

We sequenced 48 mate pair libraries with insert sizes ranging from 1300 to 5500 bp Table B2 groups these by insert sizeand summarizes the outcome of the MaSuRCA jumping library filters on these as well as the estimated clone coveragepresented to the downstream overlap assembler

DiTag Libraries

We sequenced nine DiTag libraries in 40 fractional GAIIx lanes yielding 46 million read pairs summarized in Table B3

Figure A1 The percentage of identity of long contigs (20000 bp) from the fosmid pool assembly when aligned to the WGS assembly A total of 109contigs were 100 identical and another 2011 were between 995 and 100 identical

888 A Zimin et al

Table B2 Summary of outcomes from preprocessing stages for long fragment libraries with lengths ranging from 1000 to 5500 bp

Estimated jumpsize (bp)

Librarycount

Readssequenced

After error correctionand mappinga

Nonjunctionpairs

Redundantreads

Final reads withboth 63 bp

Estimatedclone coverage

1000ndash1999 5 1273 853 (67) 56 (7) 97 (12) 421 1532000ndash2999 16 6519 4300 (66) 266 (6) 430 (11) 2074 11933000ndash3999 18 7054 4744 (67) 264 (6) 881 (20) 2132 16234000ndash4999 6 1866 1278 (69) 66 (5) 136 (11) 638 6335000ndash5500 3 553 382 (69) 57 (15) 198 (61) 576 213

Read counts are given in millionsa Reads that successfully passed error correction and mapped to the haplotype of the target megagametophyte

Table B1 Selected statistics for haploid paired-end sequence data by platform and fragment size

Platform

Medianfragmentsize (bp)

SequencedGbp

Error-correctedGbp

Read 1expected

error-correctedlength (bp)

Read 2expected

error-correctedlength (bp)

Expectedgap size (bp)

Gapsfilled ()

Maximalsuper-reads ()

GAIIx 160+156 209 23 22 1570 1513 2993 96 6220 29 28 1571 1513 2884 96 7234 33 31 1576 1497 2733 95 8246 23 22 1578 1517 2635 94 9260 24 23 1578 1509 2487 94 9273 263 252 1575 1500 2345 92 12285 24 23 1578 1514 2242 92 12325 22 21 1575 1493 183 91 15441 19 18 1571 1417 1422 84 18565 15 14 1569 1405 2676 76 22637 240 11 1566 1348 3456 71 23

HiSeq 2x125 273 3260 3154 1238 1220 271 83 9HiSeq 2x128 220 966 929 1265 1241 2306 90 6

234 595 577 1272 1254 2186 87 6246 616 596 1266 1253 259 87 7260 1002 971 1271 1251 223 84 9285 956 917 1264 1232 354 86 10325 923 889 1262 1243 745 80 12441 358 344 1262 1240 1908 73 15565 434 414 1257 1222 3171 68 19

MiSeq 2x255 325 25 238 2482 2388 21620 95 14441 20 185 2475 2322 2386 89 18565 16 144 2469 2469 960 83 23637 12 103 2466 2466 1779 79 24

The percentage of reads that are gap filled is from the total number of reads entering gap fill and the percentage of reads becoming maximal super-reads is from thosepassing error correction As fragment size increases so does the expected gap size Both the error-corrected read length and the rate at which the gaps are successfully filledare decreasing functions of the fragment size Nevertheless the overall trend is that participation in the formation of super-reads increases with insert size Only the paired-end reads that are successfully transformed into maximal super-reads are passed to subsequent assembly steps

Table B3 Summary of DiTag sequencing

LibraryLanes

sequenced

Median readssequenced perlane (millions)

Median reads aftererror correction

and mapping (millions)Median nonjunction

reads (millions)Median redundantreads (millions)

Median finalreads with both63 bp (millions)

Median clonecoverage perlane (sum)

N1 6 25 15 (57) 02 (16) 05 (37) 03 (12) 033 (183)N2 6 18 12 (68) 003 (3) 06 (53) 03 (15) 023 (123)N3 6 09 06 (59) 005 (8) 02 (46) 01 (13) 013 (063)N4 3 15 10 (66) 01 (5) 03 (37) 03 (22) 033 (093)R1 2 41 18 (44) 07 (39) 09 (85) 02 (4) 013 (023)R2 2 29 11 (39) 04 (33) 05 (63) 01 (5) 013 (023)R3 2 57 22 (39) 10 (45) 12 (97) 02 (3) 013 (023)All lanes 40 93 39 (42) 7 (18) 15 (47) 9 (10) (753)

Four nick translation libraries (N1ndashN4) and three endonuclease digestion libraries (R1ndashR3) are detailed Each library was sequenced in replicate in a number of lanes All valuesreported other than the totals are medians To allow for a more direct comparison between libraries median values per lane are used

Sequencing and Assembling P taeda 889

Table C1 Loblolly pine V101 assembly compared to contemporary draft conifer genomes

Species Loblolly pine (Pinus taeda) Norway spruce (Picea abies) White spruce (Picea glauca)

Cytometrically estimated genome size (Gbp) 216a 196b 158c

Total scaffold span (Gbp) 226 123 236Total contig spand(Gbp) 201 120 208Referenced genome-size estimate (Gbp) 22 18 20N50 contig size (kbp) 82 06 54N50 scaffold size (kbp) 669 072 229No of scaffolds 14412985 10253693 7084659Annotation of 248 conserved

CEGMA genes (Parra et al 2007)185 (74) complete 124 (50) complete 95 (38) complete

203 (82) complete + partial91 annotated full length

189 (76) complete + partial66 annotated full length

184 (74) complete + partial52 annotated full length

N50 contig and scaffold sizes are based on the estimated genome size listed in the tablea OrsquoBrien et al (1996)b Fuchs et al (2008)c Bai et al (2012)d Determined as the number of non-N characters in the published reference sequence

Appendix C Conifer Assembly Statistics

The loblolly pine genome (Neale et al 2014) whose sequencing and assembly we described here joins two other conifergenome sequences Norway spruce (Nystedt et al 2013) and white spruce (Birol et al 2013) as a foundation of conifergenomics We report selected assembly statistics from these three genomes for comparison (Table C1 and Figure C1)

Figure C1 The contiguity of the loblolly pine v101 assembly is compared to contemporary draft conifer assemblies Total scaffold span is plottedagainst a minimum scaffold size threshold Loblolly pine is relatively more complete when considering large-gene-sized ( 10 kbp) scaffolds This isreflected in the CEGMA results (Table C1)

890 A Zimin et al

trimmed to 125 bp thereby removing low-quality bases from the 39 ends of the reads We assembled the fosmid pool withSOAPdenovo (release 1) (Li et al 2010) using a k-mer size of 63

Our largest pool contained 4600 fosmids If the expected size of the loblolly DNA in the fosmids was 36 kbp (seeResults) then the total pool size was 166 Mbp or 1 of the total genome size for loblolly pine Thus we would expect1 of the randomly chosen fosmids to overlap another fosmid in the same pool

The total amount of sequence from this pool was 817 million read pairs (Table A1) which provided 1203 coverage onaverage for each fosmid The resulting pooled assembly showed remarkably high contiguity with 3719 scaffolds spanning30 kbp (80 of the maximum length for a fosmid) and 5323 scaffolds longer than 20 kbp as shown in Table A2

The high level of contiguity for the fosmid pool assembly is likely a result of the haploid nature of the fosmids combinedwith the use of a jumping library containing 35-kbp paired reads

For validation purposes the contigs 20000 bp in Table A2 were aligned against the WGS assembly using MUMmerDetailed results from these alignments are presented above Figure A1 gives additional detail as to the distribution of thepercentage of identity of each fosmid contig aligned to the WGS assembly

Appendix B Additional Library Statistics

Haploid Paired-End Libraries

We sequenced 11 paired-end libraries derived from the DNA of a single megagametophyte using three Illumina platforms(HiSeq2000 GAIIx and MiSeq) Table B1 shows that we obtained 14 Tbp of short-insert paired-end high-qualitysequence corresponding to nearly 64-fold coverage of the loblolly pine genome As expected from the narrow and consistentdistributions in Figure 2 the empirical fragment size coefficient of variation for the 11 haploid paired-end libraries wasuniformly small between 4 and 6

Mate Pair Libraries

We sequenced 48 mate pair libraries with insert sizes ranging from 1300 to 5500 bp Table B2 groups these by insert sizeand summarizes the outcome of the MaSuRCA jumping library filters on these as well as the estimated clone coveragepresented to the downstream overlap assembler

DiTag Libraries

We sequenced nine DiTag libraries in 40 fractional GAIIx lanes yielding 46 million read pairs summarized in Table B3

Figure A1 The percentage of identity of long contigs (20000 bp) from the fosmid pool assembly when aligned to the WGS assembly A total of 109contigs were 100 identical and another 2011 were between 995 and 100 identical

888 A Zimin et al

Table B2 Summary of outcomes from preprocessing stages for long fragment libraries with lengths ranging from 1000 to 5500 bp

Estimated jumpsize (bp)

Librarycount

Readssequenced

After error correctionand mappinga

Nonjunctionpairs

Redundantreads

Final reads withboth 63 bp

Estimatedclone coverage

1000ndash1999 5 1273 853 (67) 56 (7) 97 (12) 421 1532000ndash2999 16 6519 4300 (66) 266 (6) 430 (11) 2074 11933000ndash3999 18 7054 4744 (67) 264 (6) 881 (20) 2132 16234000ndash4999 6 1866 1278 (69) 66 (5) 136 (11) 638 6335000ndash5500 3 553 382 (69) 57 (15) 198 (61) 576 213

Read counts are given in millionsa Reads that successfully passed error correction and mapped to the haplotype of the target megagametophyte

Table B1 Selected statistics for haploid paired-end sequence data by platform and fragment size

Platform

Medianfragmentsize (bp)

SequencedGbp

Error-correctedGbp

Read 1expected

error-correctedlength (bp)

Read 2expected

error-correctedlength (bp)

Expectedgap size (bp)

Gapsfilled ()

Maximalsuper-reads ()

GAIIx 160+156 209 23 22 1570 1513 2993 96 6220 29 28 1571 1513 2884 96 7234 33 31 1576 1497 2733 95 8246 23 22 1578 1517 2635 94 9260 24 23 1578 1509 2487 94 9273 263 252 1575 1500 2345 92 12285 24 23 1578 1514 2242 92 12325 22 21 1575 1493 183 91 15441 19 18 1571 1417 1422 84 18565 15 14 1569 1405 2676 76 22637 240 11 1566 1348 3456 71 23

HiSeq 2x125 273 3260 3154 1238 1220 271 83 9HiSeq 2x128 220 966 929 1265 1241 2306 90 6

234 595 577 1272 1254 2186 87 6246 616 596 1266 1253 259 87 7260 1002 971 1271 1251 223 84 9285 956 917 1264 1232 354 86 10325 923 889 1262 1243 745 80 12441 358 344 1262 1240 1908 73 15565 434 414 1257 1222 3171 68 19

MiSeq 2x255 325 25 238 2482 2388 21620 95 14441 20 185 2475 2322 2386 89 18565 16 144 2469 2469 960 83 23637 12 103 2466 2466 1779 79 24

The percentage of reads that are gap filled is from the total number of reads entering gap fill and the percentage of reads becoming maximal super-reads is from thosepassing error correction As fragment size increases so does the expected gap size Both the error-corrected read length and the rate at which the gaps are successfully filledare decreasing functions of the fragment size Nevertheless the overall trend is that participation in the formation of super-reads increases with insert size Only the paired-end reads that are successfully transformed into maximal super-reads are passed to subsequent assembly steps

Table B3 Summary of DiTag sequencing

LibraryLanes

sequenced

Median readssequenced perlane (millions)

Median reads aftererror correction

and mapping (millions)Median nonjunction

reads (millions)Median redundantreads (millions)

Median finalreads with both63 bp (millions)

Median clonecoverage perlane (sum)

N1 6 25 15 (57) 02 (16) 05 (37) 03 (12) 033 (183)N2 6 18 12 (68) 003 (3) 06 (53) 03 (15) 023 (123)N3 6 09 06 (59) 005 (8) 02 (46) 01 (13) 013 (063)N4 3 15 10 (66) 01 (5) 03 (37) 03 (22) 033 (093)R1 2 41 18 (44) 07 (39) 09 (85) 02 (4) 013 (023)R2 2 29 11 (39) 04 (33) 05 (63) 01 (5) 013 (023)R3 2 57 22 (39) 10 (45) 12 (97) 02 (3) 013 (023)All lanes 40 93 39 (42) 7 (18) 15 (47) 9 (10) (753)

Four nick translation libraries (N1ndashN4) and three endonuclease digestion libraries (R1ndashR3) are detailed Each library was sequenced in replicate in a number of lanes All valuesreported other than the totals are medians To allow for a more direct comparison between libraries median values per lane are used

Sequencing and Assembling P taeda 889

Table C1 Loblolly pine V101 assembly compared to contemporary draft conifer genomes

Species Loblolly pine (Pinus taeda) Norway spruce (Picea abies) White spruce (Picea glauca)

Cytometrically estimated genome size (Gbp) 216a 196b 158c

Total scaffold span (Gbp) 226 123 236Total contig spand(Gbp) 201 120 208Referenced genome-size estimate (Gbp) 22 18 20N50 contig size (kbp) 82 06 54N50 scaffold size (kbp) 669 072 229No of scaffolds 14412985 10253693 7084659Annotation of 248 conserved

CEGMA genes (Parra et al 2007)185 (74) complete 124 (50) complete 95 (38) complete

203 (82) complete + partial91 annotated full length

189 (76) complete + partial66 annotated full length

184 (74) complete + partial52 annotated full length

N50 contig and scaffold sizes are based on the estimated genome size listed in the tablea OrsquoBrien et al (1996)b Fuchs et al (2008)c Bai et al (2012)d Determined as the number of non-N characters in the published reference sequence

Appendix C Conifer Assembly Statistics

The loblolly pine genome (Neale et al 2014) whose sequencing and assembly we described here joins two other conifergenome sequences Norway spruce (Nystedt et al 2013) and white spruce (Birol et al 2013) as a foundation of conifergenomics We report selected assembly statistics from these three genomes for comparison (Table C1 and Figure C1)

Figure C1 The contiguity of the loblolly pine v101 assembly is compared to contemporary draft conifer assemblies Total scaffold span is plottedagainst a minimum scaffold size threshold Loblolly pine is relatively more complete when considering large-gene-sized ( 10 kbp) scaffolds This isreflected in the CEGMA results (Table C1)

890 A Zimin et al

Table B2 Summary of outcomes from preprocessing stages for long fragment libraries with lengths ranging from 1000 to 5500 bp

Estimated jumpsize (bp)

Librarycount

Readssequenced

After error correctionand mappinga

Nonjunctionpairs

Redundantreads

Final reads withboth 63 bp

Estimatedclone coverage

1000ndash1999 5 1273 853 (67) 56 (7) 97 (12) 421 1532000ndash2999 16 6519 4300 (66) 266 (6) 430 (11) 2074 11933000ndash3999 18 7054 4744 (67) 264 (6) 881 (20) 2132 16234000ndash4999 6 1866 1278 (69) 66 (5) 136 (11) 638 6335000ndash5500 3 553 382 (69) 57 (15) 198 (61) 576 213

Read counts are given in millionsa Reads that successfully passed error correction and mapped to the haplotype of the target megagametophyte

Table B1 Selected statistics for haploid paired-end sequence data by platform and fragment size

Platform

Medianfragmentsize (bp)

SequencedGbp

Error-correctedGbp

Read 1expected

error-correctedlength (bp)

Read 2expected

error-correctedlength (bp)

Expectedgap size (bp)

Gapsfilled ()

Maximalsuper-reads ()

GAIIx 160+156 209 23 22 1570 1513 2993 96 6220 29 28 1571 1513 2884 96 7234 33 31 1576 1497 2733 95 8246 23 22 1578 1517 2635 94 9260 24 23 1578 1509 2487 94 9273 263 252 1575 1500 2345 92 12285 24 23 1578 1514 2242 92 12325 22 21 1575 1493 183 91 15441 19 18 1571 1417 1422 84 18565 15 14 1569 1405 2676 76 22637 240 11 1566 1348 3456 71 23

HiSeq 2x125 273 3260 3154 1238 1220 271 83 9HiSeq 2x128 220 966 929 1265 1241 2306 90 6

234 595 577 1272 1254 2186 87 6246 616 596 1266 1253 259 87 7260 1002 971 1271 1251 223 84 9285 956 917 1264 1232 354 86 10325 923 889 1262 1243 745 80 12441 358 344 1262 1240 1908 73 15565 434 414 1257 1222 3171 68 19

MiSeq 2x255 325 25 238 2482 2388 21620 95 14441 20 185 2475 2322 2386 89 18565 16 144 2469 2469 960 83 23637 12 103 2466 2466 1779 79 24

The percentage of reads that are gap filled is from the total number of reads entering gap fill and the percentage of reads becoming maximal super-reads is from thosepassing error correction As fragment size increases so does the expected gap size Both the error-corrected read length and the rate at which the gaps are successfully filledare decreasing functions of the fragment size Nevertheless the overall trend is that participation in the formation of super-reads increases with insert size Only the paired-end reads that are successfully transformed into maximal super-reads are passed to subsequent assembly steps

Table B3 Summary of DiTag sequencing

LibraryLanes

sequenced

Median readssequenced perlane (millions)

Median reads aftererror correction

and mapping (millions)Median nonjunction

reads (millions)Median redundantreads (millions)

Median finalreads with both63 bp (millions)

Median clonecoverage perlane (sum)

N1 6 25 15 (57) 02 (16) 05 (37) 03 (12) 033 (183)N2 6 18 12 (68) 003 (3) 06 (53) 03 (15) 023 (123)N3 6 09 06 (59) 005 (8) 02 (46) 01 (13) 013 (063)N4 3 15 10 (66) 01 (5) 03 (37) 03 (22) 033 (093)R1 2 41 18 (44) 07 (39) 09 (85) 02 (4) 013 (023)R2 2 29 11 (39) 04 (33) 05 (63) 01 (5) 013 (023)R3 2 57 22 (39) 10 (45) 12 (97) 02 (3) 013 (023)All lanes 40 93 39 (42) 7 (18) 15 (47) 9 (10) (753)

Four nick translation libraries (N1ndashN4) and three endonuclease digestion libraries (R1ndashR3) are detailed Each library was sequenced in replicate in a number of lanes All valuesreported other than the totals are medians To allow for a more direct comparison between libraries median values per lane are used

Sequencing and Assembling P taeda 889

Table C1 Loblolly pine V101 assembly compared to contemporary draft conifer genomes

Species Loblolly pine (Pinus taeda) Norway spruce (Picea abies) White spruce (Picea glauca)

Cytometrically estimated genome size (Gbp) 216a 196b 158c

Total scaffold span (Gbp) 226 123 236Total contig spand(Gbp) 201 120 208Referenced genome-size estimate (Gbp) 22 18 20N50 contig size (kbp) 82 06 54N50 scaffold size (kbp) 669 072 229No of scaffolds 14412985 10253693 7084659Annotation of 248 conserved

CEGMA genes (Parra et al 2007)185 (74) complete 124 (50) complete 95 (38) complete

203 (82) complete + partial91 annotated full length

189 (76) complete + partial66 annotated full length

184 (74) complete + partial52 annotated full length

N50 contig and scaffold sizes are based on the estimated genome size listed in the tablea OrsquoBrien et al (1996)b Fuchs et al (2008)c Bai et al (2012)d Determined as the number of non-N characters in the published reference sequence

Appendix C Conifer Assembly Statistics

The loblolly pine genome (Neale et al 2014) whose sequencing and assembly we described here joins two other conifergenome sequences Norway spruce (Nystedt et al 2013) and white spruce (Birol et al 2013) as a foundation of conifergenomics We report selected assembly statistics from these three genomes for comparison (Table C1 and Figure C1)

Figure C1 The contiguity of the loblolly pine v101 assembly is compared to contemporary draft conifer assemblies Total scaffold span is plottedagainst a minimum scaffold size threshold Loblolly pine is relatively more complete when considering large-gene-sized ( 10 kbp) scaffolds This isreflected in the CEGMA results (Table C1)

890 A Zimin et al

Table C1 Loblolly pine V101 assembly compared to contemporary draft conifer genomes

Species Loblolly pine (Pinus taeda) Norway spruce (Picea abies) White spruce (Picea glauca)

Cytometrically estimated genome size (Gbp) 216a 196b 158c

Total scaffold span (Gbp) 226 123 236Total contig spand(Gbp) 201 120 208Referenced genome-size estimate (Gbp) 22 18 20N50 contig size (kbp) 82 06 54N50 scaffold size (kbp) 669 072 229No of scaffolds 14412985 10253693 7084659Annotation of 248 conserved

CEGMA genes (Parra et al 2007)185 (74) complete 124 (50) complete 95 (38) complete

203 (82) complete + partial91 annotated full length

189 (76) complete + partial66 annotated full length

184 (74) complete + partial52 annotated full length

N50 contig and scaffold sizes are based on the estimated genome size listed in the tablea OrsquoBrien et al (1996)b Fuchs et al (2008)c Bai et al (2012)d Determined as the number of non-N characters in the published reference sequence

Appendix C Conifer Assembly Statistics

The loblolly pine genome (Neale et al 2014) whose sequencing and assembly we described here joins two other conifergenome sequences Norway spruce (Nystedt et al 2013) and white spruce (Birol et al 2013) as a foundation of conifergenomics We report selected assembly statistics from these three genomes for comparison (Table C1 and Figure C1)

Figure C1 The contiguity of the loblolly pine v101 assembly is compared to contemporary draft conifer assemblies Total scaffold span is plottedagainst a minimum scaffold size threshold Loblolly pine is relatively more complete when considering large-gene-sized ( 10 kbp) scaffolds This isreflected in the CEGMA results (Table C1)

890 A Zimin et al