EPGA2: memory-efficient de novo assembler
EPGA2: memory-efficient de novo assembler
复制标题
EPGA2：内存高效的从头汇编器
DOI：
10.1093/bioinformatics/btv487
复制
发表时间：
2015-12-15
期刊：
影响因子：
5.8
通讯作者：
Pan, Yi
中科院分区：
文献类型：
--
作者：
Luo, Junwei;Wang, Jianxin;Pan, Yi
动机
在基因组组装中，随着测序覆盖范围和基因组大小的不断增长，当前大多数软件需要大内存来处理大量序列数据。然而，大多数研究人员通常无法满足计算资源的要求，这阻碍了大多数现有软件的实际应用。
结果
在本文中，我们提出了一种名为EPGA2的更新算法，该算法应用了一些新模块，可以在小内存下带来改进的装配结果。为了减少基因组组装中的峰值内存，EPGA2 采用内存高效的 DSK 来计数 K-mers 并修改 BCALM 来构建 De Bruijn Graph。此外，EPGA2 并行了 Contigs Merging 的步骤，并在其管道中添加了错误校正。我们的实验表明，EPGA2 中的所有这些变化对于基因组组装更有用。
可用性和实施
EPGA2 可在 https://github.com/bioinfomaticsCSU/EPGA2 公开下载。
MOTIVATION
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.
RESULTS
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.
AVAILABILITY AND IMPLEMENTATION
EPGA2 is publicly available for download at https://github.com/bioinfomaticsCSU/EPGA2.