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	K-mer Counting for Genomic Big Data
	Shengzhong Feng; Jianqiu Ge; Ning Guo; Jintao Meng; Bingqiang Wang; Pavan Balaji; Jiaxiu Zhou; Yanjie Wei
	2018
会议日期	2018
英文摘要	Counting the abundance of all the k-mers (substrings of length k) in sequencing reads is an important step of many bioinformatics applications, including de novo assembly, error correction and multiple sequence alignment. However, processing large amount of genomic dataset (TB range) has become a bottle neck in these bioinformatics pipelines. At present, most of the k-mer counting tools are based on single node, and cannot handle the data at TB level efficiently. In this paper, we propose a new distributed method for k-mer counting with high scalability. We test our k-mer counting tool on Mira supercomputer at Argonne National Lab, the experimental results show that it can scale to 8192 cores with an efficiency of 43% when processing 2 TB simulated genome dataset with 200 billion distinct k-mers (graph size), and only 578 s is used for the whole genome statistical analysis.
语种	英语
URL标识	查看原文
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/14063]
专题	深圳先进技术研究院_数字所
推荐引用方式 GB/T 7714	Shengzhong Feng,Jianqiu Ge,Ning Guo,et al. K-mer Counting for Genomic Big Data[C]. 见:. 2018.

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