Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Daniel R. Zerbino and Ewan Birney 2008
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1 Velvet: Algorithms for de novo short read assembly using de Bruijn graphs Daniel R. Zerbino and Ewan Birney
2 Problem de novo assembly is the problem of figuring out the genome sequence without no prior information for example, sequencing a species for the first time inputs reads short, can be modelled as randomly broken pieces of the genome plus possibly paired end data 2
3 Illustration TAGTCGAGGCTTTAGATCCGATGAGGCTTTAGAGACAG AGTCGAG CTTTAGA CGATGAG CTTTAGA GTCGAGG TTAGATC ATGAGGC GAGACAG GAGGCTC ATCCGAT AGGCTTT GAGACAG AGTCGAG TAGATCC ATGAGGC TAGAGAA TAGTCGA CTTTAGA CCGATGA TTAGAGA CGAGGCT AGATCCG TGAGGCT AGAGACA TAGTCGA GCTTTAG TCCGATG GCTCTAG TCGACGC GATCCGA GAGGCTT AGAGACA TAGTCGA TTAGATC GATGAGG TTTAGAG GTCGAGG TCTAGAT ATGAGGC TAGAGAC AGGCTTT ATCCGAT AGGCTTT GAGACAG AGTCGAG TTAGATT ATGAGGC AGAGACA GGCTTTA TCCGATG TTTAGAG CGAGGCT TAGATCC TGAGGCT GAGACAG AGTCGAG TTTAGATC ATGAGGC TTAGAGA GAGGCTT GATCCGA GAGGCTT GAGACAG 3
4 Challenges Is this possible with short short reads? 4 25 = bp in the human genome repeats in the genome mistakes in the sequencing reads mistakes in the sequencing biology/ chemistry 4
5 de Bruijn graphs each sequence is a path through the graph the outer path:
6 construction sequence of each read is parsed into k- mers typical k=21 for read length of 25 series of matches(k-1 long) are aligned together called a block the information of each block is the last bp of each k-mer in of the block 6
7 alignment 7
8 links a directed link is drawn if there exists a (k-1)long match between two blocks if everything is perfect, an underlying sequence follows all links in the de Bruijn graph while tracing through every block however, due to the noisy measurement and sequence repeats, many more steps are required 8
9 Example TAGTCGAGGCTTTAGATCCGATGAGGCTTTAGAGACAG AGTCGAG CTTTAGA CGATGAG CTTTAGA GTCGAGG TTAGATC ATGAGGC GAGACAG GAGGCTC ATCCGAT AGGCTTT GAGACAG AGTCGAG TAGATCC ATGAGGC TAGAGAA TAGTCGA CTTTAGA CCGATGA TTAGAGA CGAGGCT AGATCCG TGAGGCT AGAGACA TAGTCGA GCTTTAG TCCGATG GCTCTAG TCGACGC GATCCGA GAGGCTT AGAGACA TAGTCGA TTAGATC GATGAGG TTTAGAG GTCGAGG TCTAGAT ATGAGGC TAGAGAC AGGCTTT ATCCGAT AGGCTTT GAGACAG AGTCGAG TTAGATT ATGAGGC AGAGACA GGCTTTA TCCGATG TTTAGAG CGAGGCT TAGATCC TGAGGCT GAGACAG AGTCGAG TTTAGATC ATGAGGC TTAGAGA GAGGCTT GATCCGA GAGGCTT GAGACAG red = sequencing mistakes 9
10 Example GAGGCTT GATCCGA GAGGCTT GAGACAG linear stretches (blocks) GATT (1x) TGAG (9x) ATGA (8x) GATG (5x) CGAT (6x) CCGA (7x) TCCG (7x) ATCC (7x) GATC (8x) AGAT (8x) TAGT (3x) AGTC (7x) GTCG (9x) TCGA (10x) CGAG (8x) CGAC (1x) GAGG (16x) GACG (1x) AGGC (16x) ACGC (1x) GGCT (11x) GCTC (2x) GCTT (8x) CTCT (1x) CTTT (8x) TCTA (2x) TTTA (8x) CTAG (2x) TTAG (12x) TAGA (16x) AGAG (9x) GAGA (12x) AGAA (1x) AGAC (9x) GACA (8x) ACAG (5x) read data form example is parsed into 4-mers matches are found and the de Bruijn graph is constructed problems? 10
11 merging blocks TAGTCGA CGAG CGACGC GAGG GATT AGAT GATCCGATGAG GCTCTAG AGGCT GCTTTAG TAGA AGAGA AGAA AGACAG Tips Bubble hanging tips(blocks that do not connect to anything) are likely due to mistakes, especially low-coverage ones bubbles(cycles of the in the graph) is likely due to errors 11
12 Tour Bus algorithm for bubble removal 12
13 result of example TAGTCGAGGCTTTAGATCCGATGAGGCTTTAGAGACAG AGATCCGATGAG TAGTCGAG GAGGCTTTAGA AGAGACAG in this example, sequence length=38 bp, read length=7bp, coverage~10x, error rate~ 3%, with one major repeat = 11bp k is chosen to be 5 bp Velvet is able to resolve this toy example! 13
14 N50 in problems of practical size, it is unlikely that any large genome can possibly be assembled through read data alone (more experiment needed) it is harder for to measure performance one measure of how well an assembler performs is the N50 (median length weighted contig length) 14
15 simulated results 15
16 real reads a 173 kbp human BAC was sequenced by Solexa with a coverage of 970X read length are 35 bp k set to 31 an virtual ideal sequencer(error free, gap free) that looks at the reference sequence is compared with Velvet 16
17 experimental reads Table 1. Efficiency of the Velvet error-correction pipeline on the BAC data set Step No. of nodes N50 (bp) Maximum length (bp) Coverage (percent >50 bp) Coverage (percent >100 bp) Initial 1,353, Simplified 945, Tips clipped Tour Bus Coverage cutoff Ideal Each line in this table represents a different stage in Velvet. The initial 17
18 conclusion Velvet is able to a reasonably well job of error removal efficiently with short reads complex genome assembly is difficult due to repeats de novo genome assembly is not a solved problem 18
19 pair end results 19
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