Parallel-META 2.0: enhanced metagenomic data analysis with functional annotation, high performance computing and advanced visualization

PLoS One. 2014 Mar 3;9(3):e89323. doi: 10.1371/journal.pone.0089323. eCollection 2014.

Abstract

The metagenomic method directly sequences and analyses genome information from microbial communities. The main computational tasks for metagenomic analyses include taxonomical and functional structure analysis for all genomes in a microbial community (also referred to as a metagenomic sample). With the advancement of Next Generation Sequencing (NGS) techniques, the number of metagenomic samples and the data size for each sample are increasing rapidly. Current metagenomic analysis is both data- and computation- intensive, especially when there are many species in a metagenomic sample, and each has a large number of sequences. As such, metagenomic analyses require extensive computational power. The increasing analytical requirements further augment the challenges for computation analysis. In this work, we have proposed Parallel-META 2.0, a metagenomic analysis software package, to cope with such needs for efficient and fast analyses of taxonomical and functional structures for microbial communities. Parallel-META 2.0 is an extended and improved version of Parallel-META 1.0, which enhances the taxonomical analysis using multiple databases, improves computation efficiency by optimized parallel computing, and supports interactive visualization of results in multiple views. Furthermore, it enables functional analysis for metagenomic samples including short-reads assembly, gene prediction and functional annotation. Therefore, it could provide accurate taxonomical and functional analyses of the metagenomic samples in high-throughput manner and on large scale.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology*
  • Metagenomics*
  • Software

Grant support

This work is supported in part by Chinese Academy of Sciences' e-Science grant INFO-115-D01-Z006, Ministry of Science and Technology's high-tech (863) grant 2009AA02Z310 & microbial database, as well as National Science Foundation of China grant 61103167, 31271410 and 61303161. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.