Exploiting sparseness in de novo genome assembly.
Identifieur interne : 001C83 ( PubMed/Checkpoint ); précédent : 001C82; suivant : 001C84Exploiting sparseness in de novo genome assembly.
Auteurs : Chengxi Ye [États-Unis] ; Zhanshan Sam Ma ; Charles H. Cannon ; Mihai Pop ; Douglas W. YuSource :
- BMC bioinformatics [ 1471-2105 ] ; 2012.
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- MESH :
English descriptors
- KwdEn :
- MESH :
Abstract
The very large memory requirements for the construction of assembly graphs for de novo genome assembly limit current algorithms to super-computing environments.
DOI: 10.1186/1471-2105-13-S6-S1
PubMed: 22537038
Affiliations:
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<front><div type="abstract" xml:lang="en">The very large memory requirements for the construction of assembly graphs for de novo genome assembly limit current algorithms to super-computing environments.</div>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The very large memory requirements for the construction of assembly graphs for de novo genome assembly limit current algorithms to super-computing environments.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">In this paper, we demonstrate that constructing a sparse assembly graph which stores only a small fraction of the observed k-mers as nodes and the links between these nodes allows the de novo assembly of even moderately-sized genomes (~500 M) on a typical laptop computer.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">We implement this sparse graph concept in a proof-of-principle software package, SparseAssembler, utilizing a new sparse k-mer graph structure evolved from the de Bruijn graph. We test our SparseAssembler with both simulated and real data, achieving ~90% memory savings and retaining high assembly accuracy, without sacrificing speed in comparison to existing de novo assemblers.</AbstractText>
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