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PLoS Comput Biol. 2016 Jun 21;12(6):e1004957. doi: 10.1371/journal.pcbi.1004957. eCollection 2016 Jun.

MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data.

Author information

1
Center for Bioinformatics, University of Tübingen, Tübingen, Germany.
2
Life Sciences Institute, National University of Singapore, Singapore.
3
CeMeT GmbH, Tübingen, Germany.
4
IMPRS 'From Molecules to Organisms', MPI for Developmental Biology and University of Tübingen, Tübingen, Germany.
5
Norwich Medical School, University of East Anglia, Norwich, United Kingdom.

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.

PMID:
27327495
PMCID:
PMC4915700
DOI:
10.1371/journal.pcbi.1004957
[Indexed for MEDLINE]
Free PMC Article

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