Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-12-S1-S21
Title: Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG
Authors: Mitra, S
Rupek, P
Richter, D.C
Urich, T
Gilbert, J.A
Meyer, F
Wilke, A
Huson, D.H 
Keywords: Comparative analysis
Computational analysis
Environmental sample
Gene expression patterns
Interactive analysis
Microbial communities
Next-generation sequencing
Technological advances
Computational methods
Gene expression
Microorganisms
Tools
Websites
Functional analysis
Functional analysis
Bioinformatics
article
biology
comparative study
computer program
gene expression profiling
metagenomics
methodology
Computational Biology
Gene Expression Profiling
Metagenomics
Software
Issue Date: 2011
Citation: Mitra, S, Rupek, P, Richter, D.C, Urich, T, Gilbert, J.A, Meyer, F, Wilke, A, Huson, D.H (2011). Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG. BMC Bioinformatics 12 (SUPPL. 1) : S21. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-12-S1-S21
Rights: Attribution 4.0 International
Abstract: 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. © 2011 Mitra et al; licensee BioMed Central Ltd.
Source Title: BMC Bioinformatics
URI: https://scholarbank.nus.edu.sg/handle/10635/181644
ISSN: 14712105
DOI: 10.1186/1471-2105-12-S1-S21
Rights: Attribution 4.0 International
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