genome sequencing and assembly mayo/uiuc summer course in computational biology

GENOME SEQUENCING AND ASSEMBLY

Mayo/UIUC Summer Course in Computational Biology

Session Outline

Planning a genome sequencing project

Assembly strategies and algorithms

Assessing the quality of the assembly

Assessing the quality of the assemblers

Genome annotation

Genome sequencing

Schematic overview of genome assembly. (a) DNA is collected from the biological sample and sequenced. (b) The output from the sequencer consists of many billions of short, unordered DNA fragments from random positions in the genome. (c) The short fragments are compared with each other to discover how they overlap. (d) The overlap relationships are captured in a large assembly graph shown as nodes representing kmers or reads, with edges drawn between overlapping kmers or reads. (e) The assembly graph is refined to correct errors and simplify into the initial set of contigs, shown as large ovals connected by edges. (f) Finally, mates, markers and other long-range information are used to order and orient the initial contigs into large scaffolds, as shown as thin black lines connecting the initial contigs.Schatz et al. Genome Biology 2012 13:243

Planning a genome sequencing project

How large is my genome?How much of it is repetitive, and what is the repeat size distribution?Is a good quality genome of a related species available?What will be my strategy for performing the assembly?

How large is my genome?

The size of the genome can be estimated from the ploidy of the organism and the DNA content per cellThis will affect:

» How many reads will be required to attain sufficient coverage (typically 10x to 100x)

»What sequencing technology to use»What computational resources will be needed

Repetitive sequences

Most common source of assembly errorsIf sequencing technology produces reads > repeat size, impact is much smallerMost common solution: generate reads or mate pairs with spacing > largest known repeat

Assemblies can collapse around repetitive sequences.

Salzberg S L , and Yorke J A Bioinformatics 2005;21:4320-4321

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Genome(s) from related species

Preferably of good quality, with large reliable scaffoldsHelp guiding the assembly of the target speciesHelp verifying the completeness of the assemblyCan themselves be improved in some casesBut to be used with caution – can cause errors when architectures are different!

Strategies for assembly

The sequencing approaches and assembly strategies are interdependent!

» E.g., for bacterial genome assembly, can generate PacBio reads and assemble with Celera Assembler, or generate Illumina reads and assemble with Velvet or SPAdes

» Optimal sequencing strategies very different for a SOAPdenovo or an ALLPATHS-LG assembly

Typical sequencing strategies

Bacterial genome:» 2x300 overlapping paired-end reads from Illumina MiSeq machine,

assembly with SPAdes» PacBio CLR sequences at 200x coverage, self-correction and/or hybrid

correction and assembly using Celera Assembler or PBJelly

Vertebrate genome:» Combination paired-end (2x250 nt overlapping fragments) and mate-pair

(1, 3 and 10 kb libraries) 100 nt reads from Illumina machine at 100x coverage (~1B reads for 1 GB genome), assembly with ALLPATHS-LG

Illumina paired end and mate pair sequencing

Additional useful data

Fosmid libraries» End sequencing adds long-range contiguity information» Pooled fosmids (~5000) can often be assembled more efficiently

Moleculo (Illumina TSLR) libraries» Technology acquired by Illumina, allows generation of fully assembled 10

kb sequences

Pacbio reads» Provide 5-8 kb reads, but in most cases need parallel coverage by

Illumina data for error correction

Assembly strategies and algorithms

In all cases, start with cleanup and error correction of raw readsFor long reads (>500 nt), Overlap/Layout/Consensus (OLC) algorithms work bestFor short reads, De Bruijn graph-based assemblers are most widely used

Cleaning up the data

Trim reads with low quality callsRemove short readsCorrect errors:

» Find all distinct k-mers (typically k=15) in input data

» Plot coverage distribution» Correct low-coverage k-mers to match high-

coverage» Part of several assemblers, also stand-alone

Quake or khmer programs

Overlap-layout-consensus

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Main entity: readRelationship between reads: overlap

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3

45

6

78

9

1 2 3 4 5 6 7 8 9

1 2 3

1 2 3

1 2 3 12

3

1 3

2

13

2

ACCTGAACCTGAAGCTGAACCAGA

OLC assembly steps

Calculate overlays» Can use BLAST-like method, but finding common k-

mers more efficient

Assemble layout graph, try to simplify graph and remove nodes (reads) – find Hamiltonian pathGenerate consensus from the alignments between reads (overlays)

Some OLC-based assemblers

Celera Assembler with the Best Overlap Graph (CABOG)

» Designed for Sanger sequences, but works with 454 and PacBio reads (with or without error correction)

Newbler, a.k.a. GS de novo Assembler» Designed for 454 sequences, but works with Sanger

reads

De Bruijn graphs - concept

Converting reads to a De Bruijn graph

Reads are 7 nt long

Graph with k=3

Deduced sequence (main branch)

DBG implementation in the Velvet assembler

Examples of DBG-based assemblers

EULER (P. Pevzner), the first assembler to use DBGVelvet (D. Zerbino), a popular choice for small genomesSOAPdenovo (BGI), widely used by BGI, best for relatively unstructured assembliesALLPATHS-LG, probably the most reliable assembler for large genomes (but with strict input requirements)

Repeats often split genome into contigs

Contig derived from unique sequencesReads from multiple repeatscollapse into artefactual contig

Consensus (15- 30Kbp)

Reads

ContigAssembly without pairs results in contigs whose order and orientation are not known.

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Pairs, especially groups of corroborating ones, link the contigs into scaffolds where the size of gaps is well characterized.

2-pair

Mean & Std.Dev.is known

Scaffold

Pairs Give Order & Orientation

ChromosomeSTS

STS-mapped Scaffolds

Contig

Gap (mean & std. dev. Known)Read pair (mates)

Consensus

Reads (of several haplotypes)

SNPsExternal “Reads”

Anatomy of a WGS Assembly

Assembly gaps

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sequencing gap - we know the order and orientation of the contigs and have at least one clone spanning the gap

physical gap - no information known about the adjacent contigs, nor about the DNA spanning the gap

Sequencing gaps

Physical gaps

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Handling repeats

1. Repeat detection» pre-assembly: find fragments that belong to repeats

• statistically (most existing assemblers)• repeat database (RepeatMasker)

» during assembly: detect "tangles" indicative of repeats (Pevzner, Tang, Waterman 2001)

» post-assembly: find repetitive regions and potential mis-assemblies. • Reputer, RepeatMasker• "unhappy" mate-pairs (too close, too far, mis-oriented)

2. Repeat resolution» find DNA fragments belonging to the repeat» determine correct tiling across the repeat» Obtain long reads spanning repeats

How good is my assembly?

How much total sequence is in the assembly relative to estimated genome size?How many pieces, and what is their size distribution?Are the contigs assembled correctly?Are the scaffolds connected in the right order / orientation?How were the repeats handled?Are all the genes I expected in the assembly?

N50: the most common measure of assembly quality

N50 = length of the shortestcontig in a set making up 50%of the total assembly length

Order and orientation of contigs – more errors in one assembly than in another

REAPR overview

REAPR Summary

REAPR is a toolkit that assesses the quality of a genome assembly independently of the assembler, and without needing a “gold” reference assemblyREAPR is not a variant calling tool; it examines the consistency of a genome assembly with the same data that were used to assemble itREAPR output can be visualized in many ways, and helps genome finishing projectsEvery genome assembly project should use REAPR or a similar toolkit to perform quality checks on the assemblies being produced

BUSCO and CEGMA: conserved gene sets

From Ian Korf’s group, UC DavisMapping Core Eukaryotic Genes

From Evgeny Zdobnov’s group,University of Geneva

Coverage is indicative of qualityand completeness of assembly

Even the best genomes are not perfect

There is no such thing as a “perfect” assembler (results from GAGE competition)

The computational demands and effectiveness of assemblers are very different

Assessing assembly strategies

Assemblathon (UC Davis and UC Santa Cruz)» Provide challenging datasets to assemble in open competition (synthetic for

edition 1, real for edition 2)» Assess competitor assemblies by many different metrics» Publish extensive reports

GAGE (U. of Maryland and Johns Hopkins)» Select datasets associated with known high-quality genomes» Run a set of open source assemblers with parameter sweeps on these datasets» Compare the results, publish in scholarly Journals with complete documentation

of parameters

Some advice on running assemblies

Perform parameter sweeps» Use many different values of key parameters, especially k-mer size for DBG

assemblers, and evaluate the output (some assemblers can do this automatically)

Try different subsets of the data» Sometimes libraries are of poor quality and degrade the quality of the assembly» Artefacts in the data (e.g. PCR duplicates, homopolymer runs, …) can also badly

affect output quality

Try more than one assembler» There is no such thing as “the best” assembler

Genome annotation

A genome sequence is useless without annotationThree steps in genome annotation:

» Find features not associated with protein-coding genes (e.g. tRNA, rRNA, snRNA, SINE/LINE, miRNA precursors)

» Build models for protein-coding genes, including exons, coding regions, regulatory regions

» Associate biologically relevant information with the genome features and genes

Methods for genome annotation

Ab initio, i.e. based on sequence alone» INFERNAL/rFAM (RNA genes), miRBase (miRNAs), RepeatMasker

(repeat families), many gene prediction algorithms (e.g. AUGUSTUS, Glimmer, GeneMark, …)

Evidence-based» Require transcriptome data for the target organism (the more the

better)» Align cDNA sequences to assembled genome and generate gene

models: TopHat/Cufflinks, Scripture

Methods for biological annotation

BLAST of gene models against protein databases» Sequence similarity to known proteins

InterProScan of predicted proteins against databases of protein domains (Pfam, Prosite, HAMAP, PANTHER, …)Mapping against Gene Ontology terms (BLAST2GO)

MAKER, integration framework for genome annotation

MAKER runs many software tools on the assembled genome and collates the outputsSee http://gmod.org/wiki/MAKER

Acknowledgements

For this slide deck I “borrowed” figures and slides from many publications, Web pages and presentations by

»M. Schatz, S. Salzberg, K. Bradnam, K. Krampis, D. Zerbino, J. J. Cook, M. Pop, G. Sutton, T. Seemann

Thank you!