De-Novo Genome Assembly and its Current State
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1 De-Novo Genome Assembly and its Current State Anne-Katrin Emde April 17, 2013 Freie Universität Berlin, Algorithmische Bioinformatik Max Planck Institut für Molekulare Genetik, Computational Molecular Biology
2 What is genome assembly and why is it hard? Original genome sequence (in multiple copies) Sequence short fragments Difficulties: - Genomes are very long and repeat-rich - Reads are very short and may contain errors and biases Reconstruct original sequence from reads Assembler 3
3 Assembly algorithms - introduction Assemblers use overlaps between reads, to first produce contigs, that are then used to build scaffolds. reads contig NNNN...NNNN scaffold Read pairs contribute long-range linking information, especially in the scaffolding phase. 4
4 2 Paradigmen 1) Overlap Layout Consensus: Focus is on sequence reads 2) De Bruijn graph: Focus is on k-tuples occuring in sequence reads NGS algorithms, March
5 Algorithms Overlap Graph 1) Overlap phase: pairwise overlap alignments 2) Layout phase: overlap graph construction and finding relative placement of reads 3) Consensus phase: Produce multiple read alignment and compute contig consensus sequences Kececioglu and Myers, 1995 ACGTAATT GTAATTCA ATTCAGTC GTCCATGT CATGTTGA TGTTGACT ACGTAATTCAGTCCATGTTGACT 7
6 Algorithms Overlap Graph 1) Overlap phase: for all pairs of reads pairwise overlap alignments read1 read2 Computationally expensive! 8
7 Algorithms Overlap Graph 1) Overlap phase: r4 r5 r3 r6 r2 2) Layout phase: r1 nodes = reads edges = overlaps r r2-5 r3-3 r4-5 r r6 9
8 Algorithms Overlap Graph 1) Overlap phase: r4 r5 r3 r6 r2 2) Layout phase: r1 nodes = reads edges = overlaps r r2-5 r3-3 r4-5 r r6 path through the graph read layout In theory Hamiltonian path (NP-complete), in practice heuristics 10
9 Algorithms Overlap Graph 2) Layout phase: nodes = reads edges = overlaps -6 r1-3 r2-5 r3-3 r4-5 r r6 3) Consensus phase: multiple read alignment contig r1 ACGTAATT r2 GTAATTCA r3 ATTCAGTC r4 GTCCATGT r5 CATGTTGA r6 TGTTGACT ACGTAATTCAGTCCATGTTGACT 11
10 Algorithms Overlap Graph 2) Layout phase: nodes = reads edges = overlaps -6 r1-3 r2-5 r3-3 r4-5 r r6 3) Consensus phase: multiple read alignment contig r1 ACGTAATT r2 GTAATTCA r3 AT - CAGTC r4 GTCCATGT r5 CATATTGA r6 TGTTGACT ACGTAATTCAGTCCATGTTGACT robust with alignment errors 12
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14 Overlap Graph There are not only simple paths A R B R C Approximate ordering in the overlap graph: B A R - Coverage is high - Path branches R is a repeat! C 16
15 Overlap Graph There are not only simple paths A R B R C Approximate ordering in the overlap graph: B A R C 17
16 Overlap Graph Now: R1 and R2 are nearly identical A R1 B R2 C A A A T T Approximate ordering in the overlap graph: A Rx A A A T T T B C Overlap strictness: Tradeoff between error tolerance and natural repeat separation 18
17 Overlap Graph Now: R1 and R2 are nearly identical A R1 B R2 C A A A T T Approximate ordering in the overlap graph: A R1 B Overlap strictness: Tradeoff between error tolerance and natural repeat separation R2 C 20
18 Algorithms - de Bruijn graph (Euler assembler) - No overlap phase, no consensus phase, basically just a layout phase Nodes = k-mers Edges = (k+1)-mers Given k = 4 and three read sequences: r1 r2 r3 CGTAATTC ATTC GTAATTCA TACGTAAT r3 TACG r2 ACGT CGTA GTAA TAAT AATT ATTC TTCA r1 contig: TACGTAATTCA r3 TACGTAAT r1 CGTAATTC r2 GTAATTCA In theory de Bruijn Superwalk Problem (NP-hard), in practice heuristics de Bruijn, 1946; Pevzner, 2001; Medvedev
19 de Bruijn graph Again, there are not only simple paths...g ACGT ACGTCA... GACGTACG CGTACGTC GTACGTCA GTCA CGTC GACGTCA is a read-incoherent path CGTA GACG ACGT GTAC Repetitive k-mers introduce cycles TACG 22
20 de Bruijn graph What if we increase k to 5?...GACGTACGTCA... GACGTACG CGTACGTC GTACGTCA GACGT ACGTA CGTAC GTACG TACGT ACGTC CGTCA Back to a linear graph structure Increasing k leads to better repeat resolution 23
21 de Bruijn graph What if we have sequencing errors?...gacgtacgtca... GACGTACG CGTACGTC GTACGGCA GACGT ACGTA CGTAC GTACG TACGT ACGTC CGTCA Additional nodes TACGG ACGGC CGGCA 24
22 A quick example One read: GTCGAGG GTCG (1x) TCGA (1x) CGAG GAGG (1x) (1x) 25
23 26 A quick example AGAT (8x) ATCC (7x) TCCG (7x) CCGA (7x) CGAT (6x) GATG (5x) ATGA (8x) TGAG (9x) GATC (8x) GATT (1x) TAGT (3x) AGTC (7x) GTCG (9x) TCGA (10x) GGCT (11x) TAGA (16x) AGAG (9x) GAGA (12x) GACA (8x) ACAG (5x) GCTT (8x) GCTC (2x) CTTT (8x) CTCT (1x) TTTA (8x) TCTA (2x) TTAG (12x) CTAG (2x) AGAC (9x) AGAA (1x) CGAG (8x) CGAC (1x) GAGG (16x) GACG (1x) AGGC (16x) ACGC (1x) All the others
24 27 A quick example AGAT (8x) ATCC (7x) TCCG (7x) CCGA (7x) CGAT (6x) GATG (5x) ATGA (8x) TGAG (9x) GATC (8x) GATT (1x) TAGT (3x) AGTC (7x) GTCG (9x) TCGA (10x) GGCT (11x) TAGA (16x) AGAG (9x) GAGA (12x) GACA (8x) ACAG (5x) GCTT (8x) GCTC (2x) CTTT (8x) CTCT (1x) TTTA (8x) TCTA (2x) TTAG (12x) CTAG (2x) AGAC (9x) AGAA (1x) CGAG (8x) CGAC (1x) GAGG (16x) GACG (1x) AGGC (16x) ACGC (1x) All the others
25 A quick example After simplification GATT AGAT TAGTCGA CGAG GATCCGATGAG GCTCTAG AGAA CGACGC GAGGCT GCTTTAG TAGA AGAGA AGACAG 28
26 Error removal Tips removed AGAT TAGTCGA CGAG GATCCGATGAG GCTCTAG GAGGCT GCTTTAG TAGA AGAGA AGACAG 29
27 Error removal Bubbles removed GATCCGATGAG AGAT TAGTCGA CGAG GAGGCT GCTTTAG TAGA AGAGA AGACAG 30
28 Error removal Final simplification AGATCCGATGAG TAGTCGAG GAGGCTTTAGA AGAGACAG 31
29 de Bruijn graph What if we have sequencing errors?...gacgtacgtca... GACGTACG CGTACGTC GTACCTCA GACGT ACGTA CGTAC GTACG TACGT ACGTC CGTCA GTACC TACCT ACCTC CCTCA Additional nodes & graph disconnected Choice of k: Tradeoff between error tolerance and repeat resolution 32
30 What is a good assembly? Some assembly evaluation metrics: - High N50 contig size (most commonly used metric) N50 contig size contigs sorted by size - Low number of assembly errors (sequence errors, structural misjoins) original sequence assembled as Only if a reference sequence is known! 36
31 Conclusions - Most assemblers use either the OLC or de Bruijn graph paradigm, both lead to NP-hard assembly models. - However, assembler performance is independent of the underlying paradigm and mainly depends on heuristics for repeat resolution and handling noise. - How to measure assembly accuracy is another important aspect, in general tradeoff between assembly contiguity and correctness. - Evaluations show that assembly is far from solved, assembler performance still quite inconsistent. For large genomes, the choice of assemblers is often limited to those that will run without crashing (GAGE paper, 2012) 39
32 References - GAGE: a critical evaluation of genome assemblies and assembly algorithms. Steven L. Salzberg et al., Genome Res Assemblathon 2: evaluating de-novo of genome assembly in three vertebrate species, Bradnam et al., not yet published - Assembly algorithms for next-generation sequencing data. Jason R. Miller, Sergey Koren, Granger Sutton, Genomics, Fragment assembly string graph, Myers, 2005 Figures: - geneed.nlm.nih.gov A Practical Comparison of De Novo Genome Assembly Software Tools for Next-Generation Sequencing Technologies, Zhang et al., PLOS one, 2011 Titel, Datum 40
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