SOAPMetaS: profiling large metagenome datasets efficiently on distributed clusters

Shixu He; Zhibo Huang; Xiaohan Wang; Lin Fang; Shengkang Li; Yong Zhang; Gengyun Zhang

doi:10.1093/bioinformatics/btaa697

SOAPMetaS: profiling large metagenome datasets efficiently on distributed clusters

Bioinformatics. 2021 May 17;37(7):1021-1023. doi: 10.1093/bioinformatics/btaa697.

Authors

Shixu He¹, Zhibo Huang², Xiaohan Wang², Lin Fang^{2

3}, Shengkang Li², Yong Zhang^{2

3}, Gengyun Zhang²

Affiliations

¹ BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China.
² BGI-Shenzhen, Shenzhen 518083, China.
³ Department of Biology, University of Copenhagen, Copenhagen 2100, Departmentenmark.

PMID: 32766813
DOI: 10.1093/bioinformatics/btaa697

Abstract

Summary: Rapid increase of the data size in metagenome researches has raised the demand for new tools to process large datasets efficiently. To accelerate the metagenome profiling process in the scenario of big data, we developed SOAPMetaS, a marker gene-based multiple-sample metagenome profiling tool built on Apache Spark. SOAPMetaS demonstrates high performance and scalability to process large datasets. It can process 80 samples of FASTQ data, summing up to 416 GiB, in around half an hour; and the accuracy of species profiling results of SOAPMetaS is similar to that of MetaPhlAn2. SOAPMetaS can deal with a large volume of metagenome data more efficiently than common-used single-machine tools.

Availability and implementation: Source code is implemented in Java and freely available at https://github.com/BGI-flexlab/SOAPMetaS.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Big Data
Metagenome*
Software*