sequencing techniques and genome assembly

Sequencing techniques and genome assembly

Yuzhen Ye (yye@indiana.edu)

School of Informatics & Computing, IUB

I519 Introduction to Bioinformatics, 2011

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What can be done Assemble the short reads into a genome

(hopefully a complete genome)– Assembly problem

Comparative analysis– Whole genome level: whole genome comparison– Individual gene level– Genome variation & SNP

Annotate the genome– What are the genes (gene structure prediction)– What are the functions of the genes

How genome sequences are generated? Limitation on read length (new sequencers produce even

shorter reads than Sanger sequencing machines)

Sequencing of long DNA sequences (a chromosome or a whole genome) relies on sequencing of short segments (carried in cloning vectors)

Two approaches to sequence large pieces of– Chromosome walking / primer walking; progresses through the

entire strand, piece by piece

– Shotgun sequencing; cut DNA randomly into smaller pieces; with sufficient oversampling (?), the sequence of the target can be inferred by piecing the sequence reads together into an assembly.

Cloning vectors

Cloning Vectors: DNA vehicles in which a foreign DNA can be inserted; and stay stable

Various types– Cosmid (plasmid, containing 37-52 kbp of DNA)– BAC (Bacterial Artificial Chromosome; takes in

100-300 kbp of foreign DNA)– YAC (Yeast Artificial Chromosome)

Shotgun sequencing

cut randomly (Shotgun)

DNA

Each short read can be sequenced

Too long to be sequenced

Fragment assembly

(an inverse problem)

Shotgun sequencing: from small viral genomes to larger genomes

Early applications of shotgun approach– small viral genomes (e.g., lambda virus; 1982) – 30- to 40-kbp segments of larger genomes that could be

manipulated and amplified in cosmids or other clones (physical mapping) -- hierarchical genome sequencing (divide-and-conquer sequencing)

1994, Haemophilus influenzae -- whole-genome shotgun (WGS) sequencing– Critical to this accomplishment: use of pairs of reads, called

mates, from the ends of 2-kbp and 16-kbp inserts randomly sampled from the genome (which used for ordering the contigs)

2001 whole-genome shotgun sequencing of Human genome

DNA sequencing technology Sanger sequencing

– The main method for sequencing DNA for the past thirty years!

2nd generation sequencing techniques (next generation sequencing)– Differ from Sanger sequencing in their basic chemistry– Massively increased throughput– Smaller DNA concentration– 454 pyrosequencing, Ilumina/Solexa, SOLiD

3rd generation? (single-molecule)

DNA sequencing: historySanger method (1977):

labeled ddNTPs terminate DNA copying at random points.

Both methods generate labeled

fragments of varying lengths that are

further electrophoresed

(electrophoretic separation)

Gilbert method (1977):

chemical method to cleave DNA at specific points (G, G+A, T+C, C).

Sanger method: generating reads

1. Start at primer

(restriction site)

2. Grow DNA chain

3. Include ddNTPs

4. Stops reaction at all

possible points

5. Separate products by

length, using gel

electrophoresisChain terminators:dideoxynucleotides triphosphates (ddNTPs)

Radioactive sequencing versus dye-terminator

sequencing

ddNTPs (chain terminators) are labeled with different fluorescent dyes, each fluorescing at a different wavelength.

Automatic DNA sequencing

Output: chromatograms (fluorescent peak trace)

Trace archive

NCBI trace archive: TI# 422835669

(http://www.ncbi.nlm.nih.gov/Traces/trace.cgi?)

New sequencing techniques Next Generation Sequencing (NGS) (Second Generation)

– Pyrosequencing– Illumina – SOLiD

Third generation sequencing– single-molecule sequencing technologies

NHGRI funds development of third generation DNA sequencing technologies

– “More than $18 million in grants to spur the development of a third generation of DNA sequencing technologies was announced today by the National Human Genome Research Institute (NHGRI). …The cost to sequence a human genome has now dipped below $40,000. Ultimately, NHGRI's vision is to cut the cost of whole-genome sequencing of an individual's genome to $1,000 or less, which will enable sequencing to be a part of routine medical care..”

– http://www.nih.gov/news/health/sep2010/nhgri-14.htm

Next-generation sequencing transforms today's biology

Sanger sequencers

454 sequencer

Ref: Nature Methods - 5, 16 - 18 (2008)

Next-generation sequencing transforms today's biology

Genome re-sequencing Metagenomics Transcriptomics (RNA-seq) Personal genomics ($1000 for sequencing a

person’s genome)

Pyrosequencing Pyrosequencing principles

– the polymerase reaction is modified to emit light as each base gets incorporated.

Roche (454) GS FLX sequencer

Solexa/Illumina sequencing Ultrahigh-throughput sequencing Keys

– attachment of randomly fragmented genomic DNA to a planar, optically transparent surface

– solid phase amplification to create an ultra-high density sequencing flow cell with > 10 million clusters, each containing ~1,000 copies of template per sq. cm.

Short reads Used for gene expression, small RNA discovery etc

Solexa/Illumina sequencing

More details at http://www.illumina.com/pages.ilmn?ID=203

Applied Biosystems SOLiD sequencer Commercial release in October 2007 Sequencing by Oligo Ligation and Detection ~5 days to run / produces 3-4Gb The chemistry is based on template-directed

ligation of short, “dinucleotide-encoding”, 8-mer oligonucleotides. Dinucleotide-encoding permits discrimination of SNP’s from most chemistry and imaging errors, and subsequent in silico correction of those errors.

Ref: http://appliedbiosystems.cnpg.com/Video/flatFiles/699/index.aspx

Comparison of new sequencing techniques

Applied Biosysems 3730 xl

454 GS FLX Pyrosequencer

Solexa 1G Genome Analyzer

Applied Biosystems 1G SOLiD Analyzer

1-2 Mbp per day/machine

100 Mbp per day/machine

800 Mbp per run/machine

1200 Mbp per run/machine

600-900bp 200-300 bp 25-40 bp 25-30

Mate pair No Mate pair No Mate pair Mate pair

Libraries No No Libraries

(“The new science of metagenomics” Table 4-2)

Increased!!

Yes now!

Next generation sequencing (NGS) techniques454 Sequencing Illumina/Solexa ABI SOLiD

Sequencing Chemistry

PyrosequencingPolymerase-based sequence-by-synthesis

Ligation-based sequencing

Amplification approach

Emulsion PCR Bridge amplification Emulsion PCR

Paired end (PED) separation

3 kb 200-500 bp 3 kb

Mb per run 100 Mb 1300 Mb 3000 Mb

Time per PED run <0.5 day 4 days 5 days

Read length (update)

250-400 bp 35, 75 and 100 bp 35 and 50 bp

Cost per run $ 8,438 USD $ 8,950 USD $ 17,447 USD

Cost per Mb $ 84.39 USD $ 5.97 USD $ 5.81 USD

Base calling Determine the sequence of nucleotides from chromatograms or flowgram (trace files often in SCF format) Peak detection Phrep quality score

Q = -10log10(Pe)

Phrep quality score

Phred Quality Score

Probability of incorrect base call

Base call accuracy

10 1/10 90%

20 1/100 99%

(for high values the two scores are asymptotically equal)

Fragment assembly (Genome assembly)

DNA

?

Assembly Comparative assembly

– comparative (re-sequencing) approaches that use the sequence of a closely related organism as a guide during the assembly process.

De novo assembly– reconstructing genomes that are not similar to any

organisms previously sequenced– proven to be difficult, falling within a class of

problems (NP-hard)– main strategies: greedy, overlap-layout-

consensus, and Eulerian

Fragment assembly: based on the overlap between reads

reads

Fragment assembly: overlap-layout-consensus

Assemblers: ARACHNE, PHRAP, CAP, TIGR, CELERA

Overlap: find potentially overlapping reads

Layout: merge reads into contigs

Consensus: derive the DNA sequence and correct read errors ..ACGATTACAATAGGTT..

Overlap Find the best match between the suffix of one

read and the prefix of another

Due to sequencing errors, need to use dynamic programming to find the optimal overlap alignment

Apply a filtration method to filter out pairs of fragments that do not share a significantly long common substring

Overlapping reads

TAGATTACACAGATTAC

TAGATTACACAGATTAC|||||||||||||||||

• Sort all k-mers in reads (k ~ 24)

• Find pairs of reads sharing a k-mer

• Extend to full alignment – throw away if not >95% similar

T GA

TAGA| ||

TACA

TAGT||

Layout

Create local multiple alignments from the overlapping reads

TAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG TTACACAGATTATTGATAGATTACACAGATTACTGATAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG TTACACAGATTATTGATAGATTACACAGATTACTGA

Derive consensus sequence

Derive multiple alignment from pairwise read alignments

Derive each consensus base by weighted voting

TAGATTACACAGATTACTGA TTGATGGCGTAA CTATAGATTACACAGATTACTGACTTGATGGCGTAAACTATAG TTACACAGATTATTGACTTCATGGCGTAA CTATAGATTACACAGATTACTGACTTGATGGCGTAA CTATAGATTACACAGATTACTGACTTGATGGGGTAA CTA

TAGATTACACAGATTACTGACTTGATGGCGTAA CTA

Consensus A consensus sequence is derived from a profile

of the assembled fragments

A sufficient number of reads are required to ensure a statistically significant consensus.

Reading errors are corrected

Gaps and contigs

Gap

Contig 1 Contig 2

Filling gap -- up the gaps by further experimentsMates for ordering the contigs

Read coverage

Assuming uniform distribution of reads:Length of genomic segment: L

Number of reads: n Coverage = n l / LLength of each read: l

How much coverage is enough (or what is sufficient oversampling)?

Lander-Waterman model: P(x) = (x * e- ) / x! P(x=0) = e-

where is coverage

C

Poisson distribution

Contig numbers vs read coverage

Using a genome of 1Mbp

How much coverage is needed

Cover region with >7-fold redundancy

Overlap reads and extend to reconstruct the original DNA sequence

reads

Repeats complicate fragment assembly

True overlap

Repeat overlap

Challenges in fragment assembly

Repeats: A major problem for fragment assembly > 50% of human genome are repeats:

- over 1 million Alu repeats (about 300 bp)

- about 200,000 LINE repeats (1000 bp and longer)

Repeat Repeat Repeat

Green and blue fragments are interchangeable when assembling repetitive DNA

Repeat types

Low-Complexity DNA (e.g. ATATATATACATA…)

Microsatellite repeats (a1…ak)N where k ~ 3-6(e.g.

CAGCAGTAGCAGCACCAG) Transposons/retrotransposons

– SINE Short Interspersed Nuclear Elements(e.g., Alu: ~300 bp long, 106 copies)

– LINE Long Interspersed Nuclear Elements~500 - 5,000 bp long, 200,000

copies

– LTR retroposons Long Terminal Repeats (~700 bp) at each end

Gene Families genes duplicate & then diverge

Segmental duplications ~very long, very similar copies

Celera assembler “The key to not being confused by repeats is the

exploitation of mate pair information to circumnavigate and to fill them”

A mate pair are two reads from the same clone -- we know the distance between the two reads

Myers et al. 2000 “A Whole-Genome Assembly of Drosophila”. Science, 287:2196 - 2204

Celera assembler: unitig

Unitig: a maximal interval subgraph of the graph of all fragment overlaps for which there are no conflicting overlaps to an interior vertexA-statistic: log-odds ratio of the probability that the distribution of fragment start points is representative of a “correct” unitig versus an overcollapsed unitig of two repeat copies.

Celera Assembler: scaffold

Contigs that are ordered and oriented into scaffolds with approximately known distances between them (using mate pairs or BAC ends)

Finishing: filling in gaps

Human genome

2001 Two assemblies of initial human genome sequences published– International Human

Genome project (Hierachical sequencing; BACshotgun)

– Celera Genomics: WGS approach;

Initial impact of the sequencing of the human genome (Nature 470:187–197, 2011)

Assembly of human genome

J. C. Venter et al., Science 291, 1304 -1351 (2001)

sequence tagged site (STS) markers

Finding a path visiting every VERTEX exactly once in the OVERLAP graph: Hamiltonian path problem

NP-complete problem: algorithms unknown

Find a path visiting every EDGE exactly once in the REPEAT graph:Eulerian path problem

Linear time algorithms are known

Fragment assembly: two alternative choicesFragment assembly: two alternative choices

Overlap graph

False overlaps induced by repeats

thick edges (a Hamiltonian cycle) correspond to the correct layout of the reads along the genome

Eulerian path approach

Pairwise overlaps between reads are never explicitly computed, hence no expensive overlap step is necessary

Overlap between two reads (bold) that can be inferred from the corresponding paths through the deBruijn graph

De Bruijn graph repeat graph (no sequencing errors)

ABCDEFCGHBCDIFCGJ

Vertices: (k-1)-mers from the sequence

AB BC CD DE EF FC CG

GHHB

DI IF

GJ

Every sub-repeat is represented as a repeat edge in the graph.

BCD FCG

Edges: k-mers from the sequence

8328 140 628 1185 2905 381 161442628 1185 140 628 1185 381140 628

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

repeat graph

Repeat graph

A-Bruijn graph

repeat graph

Removing bulges and whirls

Pevzner, Tang and Waterman. “A New Approach to Fragment Assembly in DNA Sequencing”. RECOMB01

Genome assembly viewer

EagleView

Assembly quality metrics

Number of contigs, the longest contig N50, defined as the contig length such that using

equal or longer contigs produces half the bases of the genome (or all the contigs). – sorting all contigs from largest to smallest– contig sizes: 2M, 1M, 0.5M, 0.3M, 0.2M, … 500bp

with total bases = 4M, then N50 = 0.2M

Genome assembly reborn Genome assembly reborn: recent computational

challenges (Briefings in Bioinformatics 2009 10(4):354-366)

Hybrid assembler (?)

Sequencing wars “Ion Torrent’s Fast and Cheap DNA

Sequencer Catches On, Even as Biologists Tighten Belts”– semiconductor-based and almost works like a pH

meter in some respects; Personal Genome Machine in December 2010

– Jonathan Rothberg founded 454 Life Sciences, sold to Roche in 2007

– Carlsbad, CA-based Life Technologies Sequencing Wars—The Third Generation