MEGAN community edition - Interactive exploration and analysis of large-scale microbiome sequencing data

Huson, Daniel H., Beier, Sina, Flade, Isabell, Górska, Anna, El-Hadidi, Mohamed, Mitra, Suparna, Ruscheweyh, Hans-Joachim and Tappu, Rewati (2016) MEGAN community edition - Interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Computational Biology, 12 (6). ISSN 1553-734X

Preview

PDF (Manuscript) - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview

Abstract

There is increasing interest in employing shotgun sequencing, rather than amplicon sequencing, to analyze microbiome samples. Typical projects may involve hundreds of samples and billions of sequencing reads. The comparison of such samples against a protein reference database generates billions of alignments and the analysis of such data is computationally challenging. To address this, we have substantially rewritten and extended our widely-used microbiome analysis tool MEGAN so as to facilitate the interactive analysis of the taxonomic and functional content of very large microbiome datasets. Other new features include a functional classifier called InterPro2GO, gene-centric read assembly, principal coordinate analysis of taxonomy and function, and support for metadata. The new program is called MEGAN Community Edition (CE) and is open source. By integrating MEGAN CE with our high-throughput DNA-to-protein alignment tool DIAMOND and by providing a new program MeganServer that allows access to metagenome analysis files hosted on a server, we provide a straightforward, yet powerful and complete pipeline for the analysis of metagenome shotgun sequences. We illustrate how to perform a full-scale computational analysis of a metagenomic sequencing project, involving 12 samples and 800 million reads, in less than three days on a single server. All source code is available here: https://github.com/danielhuson/megan-ce

Item Type:	Article
Additional Information:	© 2016 Huson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
Depositing User:	Pure Connector
Date Deposited:	30 Jun 2016 10:00
Last Modified:	03 Jul 2023 11:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/59629
DOI:	10.1371/journal.pcbi.1004957

Downloads

Actions (login required)

View Item