Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG

BackgroundMetagenomics 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.ResultsThe 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.ConclusionsThe 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stand-alone tool, MEGAN provides an alternative to web portals for scientists that have concerns about uploading their unpublished data to a website.

[1]  J. Gilbert,et al.  Comparison of multiple metagenomes using phylogenetic networks based on ecological indices , 2010, The ISME Journal.

[2]  Naryttza N. Diaz,et al.  Phylogenetic classification of short environmental DNA fragments , 2008, Nucleic acids research.

[3]  Ying He,et al.  Signature, a web server for taxonomic characterization of sequence samples using signature genes , 2008, Nucleic Acids Res..

[4]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[5]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[6]  I-Min A. Chen,et al.  IMG/M: a data management and analysis system for metagenomes , 2007, Nucleic Acids Res..

[7]  David L. Wheeler,et al.  GenBank , 2015, Nucleic Acids Res..

[8]  Andreas Wilke,et al.  phylogenetic and functional analysis of metagenomes , 2022 .

[9]  Alexander F. Auch,et al.  MEGAN analysis of metagenomic data. , 2007, Genome research.

[10]  Naryttza N. Diaz,et al.  The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes , 2005, Nucleic acids research.

[11]  Daniel H. Huson,et al.  Visual and statistical comparison of metagenomes , 2009, Bioinform..

[12]  I. Rigoutsos,et al.  Accurate phylogenetic classification of variable-length DNA fragments , 2007, Nature Methods.

[13]  Frank Oliver Glöckner,et al.  TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences , 2004, BMC Bioinformatics.

[14]  S. Kravitz,et al.  CAMERA: A Community Resource for Metagenomics , 2007, PLoS biology.

[15]  J. Gilbert,et al.  Detection of Large Numbers of Novel Sequences in the Metatranscriptomes of Complex Marine Microbial Communities , 2008, PloS one.

[16]  Rob Knight,et al.  UniFrac – An online tool for comparing microbial community diversity in a phylogenetic context , 2006, BMC Bioinformatics.

[17]  Inna Dubchak,et al.  The integrated microbial genomes (IMG) system , 2005, Nucleic Acids Res..

[18]  S. Tringe,et al.  Quantitative Phylogenetic Assessment of Microbial Communities in Diverse Environments , 2007, Science.