title: Metaproteomic data analysis at a glance: advances in computational microbial community proteomics.
abstract: Within the last decade, the study of microbial communities has gained increasing research interest also driven by the recognition of the important role of these consortia in human health and disease. Metaproteomics, the analysis of the entire set of proteins from all microorganisms present in one ecosystem, has become a prominent technique for studying the relation between taxonomic diversity and functional profile of microbial communities.
  
  
  
  The aim of this review is to address opportunities and challenges of metaproteomics from a computational perspective. Appealing to an audience of microbial ecologists and proteomic researchers alike, we provide an overview on state-of-the-art software and databases by which metaproteome data can be readily analyzed. Expert commentary: While tailored protein databases, combined search algorithms and iterative workflows are means to improve the identification yield, software tools for taxonomic and functional analysis are challenged by the vast amount of unannotated sequences in metaproteomics.
authors.affiliation: a Research Group Bioinformatics (NG 4) , Robert Koch Institute , Berlin , Germany.
authors.forename: Thilo
authors.initials: T
authors.lastname: Muth
authors.affiliation: a Research Group Bioinformatics (NG 4) , Robert Koch Institute , Berlin , Germany.
authors.forename: Bernhard Y
authors.initials: BY
authors.lastname: Renard
authors.affiliation: b Medical Biotechnology Center , VIB , Ghent , Belgium.|c Department of Biochemistry , Ghent University , Ghent , Belgium.|d Bioinformatics Institute Ghent , Ghent University , Ghent , Belgium.
authors.forename: Lennart
authors.initials: L
authors.lastname: Martens
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000235:genetics*; D017753:Ecosystem; D016680:Genome, Bacterial / Q000235:genetics; D006801:Humans; D056186:Metagenomics; D020543:Proteome / Q000235:genetics*; D012984:Software
chemical_list: D020543:Proteome
keywords: MS/MS; Metaproteomics; bioinformatics; computational proteomics; data analysis; microbial community samples; protein identification