Challenges and perspectives of metaproteomic data analysis

• Published in: Journal of biotechnology Vol. 261; pp. 24 - 36
• Main Authors: Heyer, Robert, Schallert, Kay, Zoun, Roman, Becher, Beatrice, Saake, Gunter, Benndorf, Dirk
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 10.11.2017
• Subjects: algorithms; Big data; biogas; Bioinformatics; carbon; computer software; diagnostic techniques; Environmental Microbiology; Environmental proteomics; Mass Spectrometry; Metagenomics; microbial activity; Microbial communities; Microbial Consortia; microorganisms; nitrogen cycle; protein content; proteins; Proteomics; Software; soil; wastewater treatment; iPath; CPU; Microbial communities; MS/MS; GPU; NoSQL; FDR; Environmental proteomics; COG; Big data; Bioinformatics; EC; de.NBI; KO; MS; m/z-ratio; GO; LCA; SQL; eggNOG; LC; Software; DBMS; Mass spectrometry; MPA
• ISSN: 0168-1656; 1873-4863; 1873-4863
• DOI: 10.1016/j.jbiotec.2017.06.1201

•Metaproteomic studies profit from dedicated software tools.•Metagenomes and protein database constraints improve protein identification.•Grouping of proteins by shared peptides or sequence similarity reduce redundancy.•Several possibilities for taxonomic and functional classification of proteins exist.•Scalability of software and databases enables handling of big data amounts. In nature microorganisms live in complex microbial communities. Comprehensive taxonomic and functional knowledge about microbial communities supports medical and technical application such as fecal diagnostics as well as operation of biogas plants or waste water treatment plants. Furthermore, microbial communities are crucial for the global carbon and nitrogen cycle in soil and in the ocean. Among the methods available for investigation of microbial communities, metaproteomics can approximate the activity of microorganisms by investigating the protein content of a sample. Although metaproteomics is a very powerful method, issues within the bioinformatic evaluation impede its success. In particular, construction of databases for protein identification, grouping of redundant proteins as well as taxonomic and functional annotation pose big challenges. Furthermore, growing amounts of data within a metaproteomics study require dedicated algorithms and software. This review summarizes recent metaproteomics software and addresses the introduced issues in detail.