Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG

Mitra, Suparna; Rupek, Paul; Richter, Daniel C.; Urich, Tim; Gilbert, Jack A.; Meyer, Folker; Wilke, Andreas; Huson, Daniel H.

doi:10.1186/1471-2105-12-s1-s21

Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG

Journal Article · Mon Feb 14 23:00:00 EST 2011 · BMC Bioinformatics

DOI:https://doi.org/10.1186/1471-2105-12-s1-s21· OSTI ID:1626285

Mitra, Suparna ^[1]; Rupek, Paul ^[2]; Richter, Daniel C. ^[3]; Urich, Tim ^[4]; Gilbert, Jack A. ^[5]; Meyer, Folker ^[6]; Wilke, Andreas ^[6]; Huson, Daniel H. ^[2]

Univ. of Tubingen (Germany). Center for Bioinformatics; DOE/OSTI
Univ. of Tubingen (Germany). Center for Bioinformatics
Univ. of Tubingen (Germany). Center for Bioinformatics; Eurofins Medigenomix GmbH, Ebersberg (Germany)
Univ. of Vienna (Austria). Vienna Ecology. Dept. of Genetics in Ecology; Bergen Univ. (Norway). Centre for Geobiology
Univ. of Chicago, IL (United States). Dept. of Ecology and Evolution; Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division
Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division

Background: Metagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Technological advances in next-generation sequencing methods are fueling a rapid increase in the number and scope of metagenome projects. While metagenomics provides information on the gene content, metatranscriptomics aims at understanding gene expression patterns in microbial communities. The initial computational analysis of a metagenome or metatranscriptome addresses three questions: (1) Who is out there? (2) What are they doing? and (3) How do different datasets compare? There is a need for new computational tools to answer these questions. In 2007, the program MEGAN (MEtaGenome ANalyzer) was released, as a standalone interactive tool for analyzing the taxonomic content of a single metagenome dataset. The program has subsequently been extended to support comparative analyses of multiple datasets. Results: The focus of this paper is to report on new features of MEGAN that allow the functional analysis of multiple metagenomes (and metatranscriptomes) based on the SEED hierarchy and KEGG pathways. We have compared our results with the MG-RAST service for different datasets. Conclusions: The MEGAN program now allows the interactive analysis and comparison of the taxonomical and functional content of multiple datasets. As a stand-alone tool, MEGAN provides an alternative to web portals for scientists that have concerns about uploading their unpublished data to a website.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1626285

Journal Information:: BMC Bioinformatics, Journal Name: BMC Bioinformatics Journal Issue: S1 Vol. 12; ISSN 1471-2105

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

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