EPGA2: memory-efficient de novo assembler

Bioinformatics
Junwei LuoYi Pan

Abstract

In genome assembly, as coverage of sequencing and genome size growing, most current softwares require a large memory for handling a great deal of sequence data. However, most researchers usually cannot meet the requirements of computing resources which prevent most current softwares from practical applications. In this article, we present an update algorithm called EPGA2, which applies some new modules and can bring about improved assembly results in small memory. For reducing peak memory in genome assembly, EPGA2 adopts memory-efficient DSK to count K-mers and revised BCALM to construct De Bruijn Graph. Moreover, EPGA2 parallels the step of Contigs Merging and adds Errors Correction in its pipeline. Our experiments demonstrate that all these changes in EPGA2 are more useful for genome assembly. EPGA2 is publicly available for download at https://github.com/bioinfomaticsCSU/EPGA2.

References

Mar 20, 2008·Genome Research·Daniel R Zerbino, Ewan Birney
Mar 3, 2009·Genome Research·Jared T SimpsonInanç Birol
Dec 15, 2010·Bioinformatics·Pramila Nuwantha Ariyaratne, Wing-Kin Sung
Dec 8, 2011·Genome Research·Steven L SalzbergJames A Yorke
Jan 18, 2013·Bioinformatics·Guillaume RizkRayan Chikhi

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Citations

Sep 17, 2016·Bioinformatics·Junwei LuoFang-Xiang Wu
Feb 23, 2020·Frontiers in Genetics·Li TangJianxin Wang
Aug 28, 2020·Frontiers in Genetics·Junwei LuoZhanqiang Huo

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