2008
DOI: 10.1101/gr.074492.107
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Velvet: Algorithms for de novo short read assembly using de Bruijn graphs

Abstract: We have developed a new set of algorithms, collectively called "Velvet," to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25-50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to … Show more

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Cited by 8,717 publications
(7,308 citation statements)
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References 28 publications
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“…We assembled the simulated reads into contigs with velvet v1.2.09 19 , then improved and annotated the resulting scaffolds 20 . We generated alignments by mapping reads to the TIGR4 reference using bwa-mem v0.7.10 with default settings 21 , and called variants from these alignments using samtools v1.2 mpileup and bcftools call 22 .…”
Section: Methodsmentioning
confidence: 99%
“…We assembled the simulated reads into contigs with velvet v1.2.09 19 , then improved and annotated the resulting scaffolds 20 . We generated alignments by mapping reads to the TIGR4 reference using bwa-mem v0.7.10 with default settings 21 , and called variants from these alignments using samtools v1.2 mpileup and bcftools call 22 .…”
Section: Methodsmentioning
confidence: 99%
“…DNA was sequenced on the Illumina GAII platform using 54‐bp paired‐end reads, resulting in a mean genome coverage of 1527 ×. A subset of Illumina reads was assembled using Velvet, version 1.0.12 (Zerbino & Birney, 2008), producing contiguous nucleotide sequences (contigs), which were scaffolded against the genome of strain F/SW4 (EMBL accession no. HE601804) (Harris et al ., 2012), using abacas (Assefa et al ., 2009).…”
Section: Methodsmentioning
confidence: 99%
“…The Rnnotator 16 , Multiple-k 21 , and Trans-ABySS 19 assemblers follow the same strategy; they assemble the dataset multiple times using a De Bruijn graph-based approach [6][7][8]58 to reconstruct transcripts from a broad range of expression levels, and then post-process the assembly to merge contigs and remove redundancy (Figure 2b). By contrast, other assemblers (Trinity 59 , and Oases 20 ) traverse the De Bruijn graph directly to assemble each isoform.…”
Section: De Novo Strategymentioning
confidence: 99%