genome assembly

Kelley Bullard, Henry Dewhurst, Kizee Etienne, Esha Jain, VivekSagar KR, Benjamin Metcalf, Raghav Sharma, Charles Wigington, Juliette Zerick

Genome Assembly

Outline Stake Holders Biology NGS Review Introduction to Genome Assembly Challenges Analysis pipeline/ strategy Tool selection Summary (final pipeline)

Stakeholders

CDC (Centers for Disease Control and Prevention) GaTech Immunocompromised individuals Consumers of seafood Prediction group (and subsequent groups)

Stakeholders / Biology / NGS Review / Introduction to Genome Assembly / Challenges /Analysis Pipeline-Strategy / Tool Selection / Summary

Biology…

Image of V. vulnificus

Vibrio vulnificus

Gram-negativeo Lipopolysaccharide membrane

Motile, facultative anaerobe Halophilic (salt-loving) organism

abundant in estuarine ecosystems Major cause of seafood related

deaths

Vibrio vulnificus – genome architecture Bacterial genomes are coding-

denseo Introns rare

Contains plasmids (pYJ016) V. vulnificus ~5.2mbp genome

(similar to E. coli, ~50%)o GC content: 45-47%

Vibrio navarrensis Gram-negative

Lipopolysaccharide membrane

Motile, facultative anaerobe Moderately halophilic organism

Some strains do not grow well in moderate to high salt concentrations

Vibrio navarrensis - genomic architecture

NGS - Review

Sample input: Genomic DNA, BACs, amplicons, cDNA

Generation of small DNA fragments via shearing

Ligation of A/B-Adaptors flanking single- stranded DNA fragments

Emulsification of beads and fragments in water-in-oil microreactors

Clonal amplification of fragments bound to beads in microreactors

Sequencing and base calling

One Fragment

One Bead

One Read

400,000 reads per run

Roche 454 sequencing workflow overview

Flowgram

GS FLX Data analysis – flowgram generation

T Flow Order4‐

3 mer‐

TTCTGCGAA

2 mer‐1 mer‐

Example of homopolymer errors from 454 sequencing data

Example of 454 sff file (text format)

cBot GAIIx0.1 - 1.0μg

User or core facility

Illumina sequencing overview

Example of Illumina *.fastq file

@C3PO_0001:2:1:17:1499#0/1 TGAATTCATTGACCATAACAATCATATGCATGATGCAAATTATAATATCATTTTTAGTGACGTCGTGAATCGTTT+C3PO_0001:2:1:17:1499#0/1abaaaaaaaaaaaààa_aaaaaaaaaaaaaaaa_a aaaàaaaaâaaaaà]^à YZYZ^`NJDJ\_Z@C3PO_0001:2:1:17:1291#0/1 TGTTTGAGCAAATGATTCATAATAATGTATTTCAATATTTTTAGGAATATCTCCCAATATTGCGCGTGCTGAATT+C3PO_0001:2:1:17:1291#0/1a`_`_\a_aaaa_a^Z^â[aâa]a_^_a_`àa àa`X^X^^àa_\_]VR`\a_]W\_`_a]a]][\RZV@C3PO_0001:2:2:1452:1316#0/1 GTCCATCCGCAGCAGCGAATTTTTGACGTCCCCCCCCGAANGGANGNGANNNNGNNGNNNTNTNNAAANGNNNNN+C3PO_0001:2:2:1452:1316#0/1_U a\ `]_`ZP\\_Z^[]aaâ_]XNBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB…

Genome Assembly

Input reads

V. navarrensis V. vulnificus2423-01 2009V-1368

08-2462 06-2432

2541-90 08-2435

2756-81 08-2439

07-2444

Introduction to genome assembly

An assembly is a hierarchical data structure that maps the sequence data to a putative reconstruction of the target.

In addition to contigs, a set of unassembled or partially assembled reads is also given as an output.

Contigs multiple sequence alignment of reads plus the consensus sequence.

Scaffolds - define the contig order and orientation

Output (FASTA)

• N50

• minimum/maximum contig length

• No. of contigs

• No. of errors

• FRC (feature response curve)

How do we check the quality of our assembly?

METRICS!

Feature-by-Feature – evaluating de-novo assembly• BREAKPOINT: Points in the assembly where leftover reads partially align; • COMPRESSION: Area representing a possible repeat col- lapse; • STRETCH: Area representing a possible repeat expansion; • LOW_GOOD_CVG: Area composed of paired reads at the right distance and with the right

orientation but at low coverage; • HIGH_NORMAL_CVG: Area composed of normal oriented reads but at high coverage;• HIGH_LINKING_CVG: Area composed of reads with associated mates in another scaffold;• HIGH_SPANNING_CVG: Area composed of reads with associated mates in another contig;• HIGH_OUTIE_CVG: Area composed of incorrectly oriented mates (--> -->, <-- -->); • HIGH_SINGLEMATE_CVG: Area composed of single reads (mate not present anywhere); • HIGH_READ_COVERAGE: Region in assembly with unexpectedly high local read coverage;• HIGH_SNP: SNP with high coverage; • KMER_COV: Problematic k-mer distribution.

Feature-by-Feature – evaluating de-novo assembly• Most of the traditional metrics used to evaluate assemblies (N50, mean contig size, etc.) emphasize only size, while nothing (or

almost nothing) is said about how correct the assemblies are.

• A typical such metric (especially, in the NGS context) consists in aligning contigs back to an available reference. However, this naive technique simply counts the number of mis-assemblies without attempting to distinguish or categorize them any further.

• After running amosvalidate, each contig is assigned the number of features that correspond to doubtful sequences in the assembly.

• For a fixed feature threshold w, the contigs are sorted by size and, starting from the longest, only those contigs are tallied, if their sum of features is ƒw. For this set of contigs, the corresponding approximate genome coverage is computed, leading to a single point of the Feature-Response curve (FRC).

Assembly Challenges

Challenges Intrinsic

Genome architecture Repeats Homopolymer runs Sequence complexity Chimeras?

Contaminants

Technical Short reads Poisson distribution of coverage Sequencing errors Variable quality Sequence tags

454 raw reads

Pre-processing

Illumina raw reads

Pre-processing

454 reads

Illumina reads

Statistical analysis

Read stats

Published Genomes from public databases

V. vulnificus

V. vulnificus CMCP6

V. vulnificus MO6-24/O

Align Illumina against the reference

• Fastqc• Prinseq• NGS QC

Compare mapping statistics

Reference genome

• samstats

• bwa

Reference selection

Hybrid DeNovo • Ray• MIRA

Illumina/ 454/ Hybrid DeNovo assembly

454 DeNovo• Newbler• CABOG• SUTTA

Illumina DeNovo• Allpaths LG• SOAP DeNovo• Velvet• Abyss• Taipan• Bambus2• SUTTA

contigs * 3

Align illumina reads against 454 contigs

Unmapped reads

• Mac vector• CLC wb

contigs

Unmapped reads

Evaluation

• GAGE• Hawk-eye

Illumina/(454?) reference based

assembly• AMOScmp

contigs

Unmapped reads

DeNovo assembly

Reference based assembly

Draft/ Finished genome

Reference evaluation

• DNA Diff• DNA Diff

Parameter optimization

Contig merging

All possible combinations of the

best 3• Mimimus• MAIA

• PAGIT• Mauve

Finished genomeScaffolds

• GAGE

Genome finishing

Gap filling Nulceotide identity

• DNA Diff

• GRASS• Built-in

Process

Illumina

Chosen Ref.

Assemblers

Illumina454

LEGEND

hybrid

Stakeholders / Biology / NGS Review / Introduction to Genome Assembly / Challenges / Analysis Pipeline-Strategy / Tool Selection / Summary

Tool Selection - Assembly Algorithm profile

Greedy Seed-and-extension Graph based Branch-and-Bound

Basic operation: given any read or contig, add one more read or contig until no more reads or contigs are available The contigs grow by “greedy extension” always incorporating a read that is found with the

highest scoring overlap

Makes locally optimal choice with the hope of finding a globally optimal choice No foresight -> misassembly

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of times, it was the

best of times, it was

times, it was the worst

was the best of times,

the best of times, it

times, it was the age

It was the best of

times, it was the worst

was the best of times,

the best of times, it

was the worst of times,

best of times, it

was it was the age

ofit was the worst of

times, it was the age

was the age of wisdom,

the age of wisdom, it

age of wisdom, it was

of wisdom, it was the

it was the age of

was the age of foolishness,

the worst of times, it

• It was the best of times, it was the [worst/age]

Greedy Seed-and-Extension Graph based Branch-and-Bound

Variation of the greedy assembler Common in aligners, thus some assemblers/aligners may incorporate this approach

Particularly designed for short reads based on a contig heuristic scheme

Prefix-tree data structure A contig is elongated at either end contingent upon the existence of reads with a prefix of

minimal length perfectly matching the end of the contig

Overlap: find potentially overlapping reads

Layout: layout the reads based on matching alignment

Consensus: derive the DNA sequence consensus by joining read sequences ..ACGATTACAATAGGTT..

Overlap-layout-consensus (OLC): pairwise consensus

Hamiltonian ApproachFind an assembled sequence that explains the observed sequence = finding a path through a graph that visits every vertex once

Repeat Repeat Repeat

Basic operation: k-mer approach Eulerian approach

GGA CTG

GGG TGG

GAC ACT CTT TTT

AAGA ACTT ACTC ACTG AGAG CCGA CGAC CTCC CTGG CTTT…

de Bruijn Graph

CCG TCC

Potential Genomes

AAGACTCCGACTGGGACTTT

AAGACTGGGACTCCGACTTTCTCCGA

AAG AGA

de-Brujin Graph

Basic operation: relies on “consistent layouts”; it generates all possible consistent layouts organizing them as paths in a “double tree” structure, rooted at a randomly selected seed read

Progressive evaluation of optimal criteria encoded by a set of score functions based on the set of overlaps along the layout

Tid-bits of advice

Greedy Seed-and-Extension

OLC De-Brujin Branch-and-Bound

Advantages Guaranteed to find a solution

sensitivity Suitable for low coverage long reads

Repeats are immediately recognized; suitable for high coverage short reads

Algorithm allows for checks

Disadvantages MisassemblyEasily confused by complex repeats

Can be very slow, memory usage

Computation of overlaps time intensive

RAM intensive Ambiguities delay pruning

Tools of Choice

454 platform assembly

Name Algorithm

Newbler 2.5 OLC Comparative analysis of algorithms for whole-genome assembly of pyrosequencing data

CABOG OLC Comparative analysis of algorithms for whole-genome assembly of pyrosequencing data

Branch-and-Bound

Feature-by-Feature – Evaluating De Novo Sequence Assembly

Evaluation of 454 assemblers Genomes Used For Comparison

Comparative analysis of algorithms for whole-genome assembly of pyrosequencing data Brief Bioinform (2012) 13(3): 269-280

Comparison of 454 assemblers using E. coli genome

Comparison of 454 assemblers using E. coli genome The maximum value reached by the bars is the hypothetical reconstruction HR, defined as the ratio between the assembled bases and the reference

length The white section represents the real reconstruction RR, i.e. the portion of genome correctly reconstructed by assemblers. The difference between hypothetical and RR, here called erroneous reconstruction ER, is shown in black

Illumina platform assembly

Name Algorithm Supporting Evidence

ALLPATHS-LG OLCGAGE: A critical evaluation of genome assemblies and assembly algorithms

Velvet de-BrujinComparative studies of de novo assembly tools for next-generation sequencing technologies

Taipan Hybrid(Greedy-based and graph)

A Practical Comparison of De Novo Genome Assembly Software Tools for Next-Generation Sequencing Technologies

SOAPdenovo de-Brujin Feature-by-Feature- Evaluating De Novo Sequence Assembly

SUTTABranch-and-

BoundFeature-by-Feature – Evaluating De Novo Sequence Assembly

Evaluation of illumina assemblers Genomes Used For Comparison

GAGE: A critical evaluation of genome assemblies and assembly algorithms. Steven L. Salzberg, Adam M. Phillippy, Aleksey Zimin, et al. Genome Res. 2012 22: 557-567

Comparison of illumina assemblers

• The best value for each column is shown in bold. For all assemblies

• The Errors column contains the number of misjoins plus indel errors >5 bp for contigs, and the total number of misjoins for scaffolds.

• Corrected N50 values were computed after correcting contigs and scaffolds by breaking them at each error. See the evaluation section in the text for details on how errors were identified.

Comparison of illumina assemblers• A ‘‘chaff’’ contig is defined as a single contig <200 bp in length. In many cases, these contigs can be as small as the k-mer size used to build

the de Bruijn graph (e.g., 36 bp) and are too short to support any further genomic analysis.

• A duplicated repeat is one that appears in more copies than necessary in the assembly, and a compressed repeat is one that occurs in fewer copies.

Comparison of illumina assemblers• ‘‘Misjoin’’ errors are perhaps the most harmful type, in that they represent a significant structural error. A misjoin occurs when an

assembler incorrectly joins two distant loci of the genome, which most often occurs within a repeat sequence. • We have tallied three types of misjoins: (1) inversions, where part of a contig or scaffold is reversed with respect to the true genome; (2)

relocations, or rearrangements that move a contig or scaffold within a chromosome; and (3) translocations, or rearrangements between chromosomes

Comparison of illumina assemblers• Average contig (A) and scaffold (B) sizes, measured by N50 values, versus error rates, averaged over all three genomes for which the true assembly

is known: S. aureus, R. sphaeroides, and human chromosome 14. • Errors (vertical axis) are measured as the average distance between errors, in kilobases. • In both plots, the best assemblers appear in the upper right.

Applicability of assemblers Genomes used for comparison

A Practical Comparison of De novo Genome Assembly Software Tools for Next-Generation Sequencing Technologies. Wenyu Zhang, et al. Plos One. 2011 6: 1-12

Hybrid Platform Assembly

Name Algorithm Supporting Evidence

RAYSBH Feature-by-Feature – Evaluating De Novo Sequence Assembly

Feature-by-Feature – evaluating de-novo assembly

• COMPRESSION: Area representing a possible repeat col- lapse; • LOW_GOOD_CVG: Area composed of paired reads at the right distance and with the right

orientation but at low coverage; • HIGH_OUTIE_CVG: Area composed of incorrectly oriented mates (--> -->, <-- -->); • HIGH_SINGLEMATE_CVG: Area composed of single reads (mate not present anywhere); • HIGH_READ_COVERAGE: Region in assembly with unexpectedly high local read coverage;• KMER_COV: Problematic k-mer distribution.

Feature-by-Feature: evaluating de-novo assembly Real Data - Long Reads

Feature-by-Feature – evaluating de-novo assembly Real Data - Short Reads

Final Approach

454 raw reads

Pre-processing

Illumina raw reads

Pre-processing

454 reads

Illumina reads

Statistical analysis

Read stats

Published Genomes from public databases

V. vulnificus

V. vulnificus CMCP6

V. vulnificus MO6-24/O

Align Illumina against the reference

• Fastqc• Prinseq• NGS QC

Compare mapping statistics

Reference genome

• samstats

• bwa

Reference selection

Hybrid DeNovo • Ray

Illumina/ 454/ Hybrid DeNovo assembly

454 DeNovo• Newbler• CABOG• SUTTA

Illumina DeNovo• Allpaths LG• SOAP DeNovo• Velvet• Taipan• SUTTA

contigs * 3

Align illumina reads against 454 contigs

Unmapped reads

• Mac vector• CLC wb

contigs

Unmapped reads

Evaluation

• GAGE• Hawk-eye

Illumina/(454?) reference based

assembly• AMOScmp

contigs

Unmapped reads

DeNovo assembly

Reference based assembly

Draft/ Finished genome

Reference evaluation

• DNA Diff• MUMer

Parameter optimization

Contig merging

All possible combinations of the

best 3• Mimimus• MAIA

• PAGIT• Mauve

Finished genomeScaffolds

• GAGE

Genome finishing

Gap filling Nulceotide identity

• MUMer

• GRASS• Built-in

Process

Illumina

Chosen Ref.

Assemblers

Illumina454

LEGEND

hybrid

References1. Finotello, F., et al., Comparative analysis of algorithms for whole-genome assembly of pyrosequencing data. Brief Bioinform, 2012. 13(3): p. 269-80.2. Vezzi, F., G. Narzisi, and B. Mishra, Feature-by-feature--evaluating de novo sequence assembly. PLoS One, 2012. 7(2): p. e31002.3. Zhang, W., et al., A practical comparison of de novo genome assembly software tools for next-generation sequencing technologies. PLoS One, 2011. 6(3): p. e17915.4. Salzberg, S.L., et al., GAGE: A critical evaluation of genome assemblies and assembly algorithms. Genome Res, 2012. 22(3): p. 557-67.5. Narzisi, G. and B. Mishra, Comparing de novo genome assembly: the long and short of it. PLoS One, 2011. 6(4): p. e19175.6. Miller, J.R., S. Koren, and G. Sutton, Assembly algorithms for next-generation sequencing data. Genomics, 2010. 95(6): p. 315-27.7. Li, Z., et al., Comparison of the two major classes of assembly algorithms: overlap-layout-consensus and de-bruijn-graph. Brief Funct Genomics, 2012. 11(1): p. 25-37.8. Lin, Y., et al., Comparative studies of de novo assembly tools for next-generation sequencing technologies. Bioinformatics, 2011. 27(15): p. 2031-7.9. Zhang, J., et al., The impact of next-generation sequencing on genomics. J Genet Genomics, 2011. 38(3): p. 95-109.

genome assembly

mbp genome similar

microreactors sequencing

genomic dna

sequencing dataexample

fastq file

disease control

charles wigington

kizee etienne

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