TY - JOUR A1 - Van Den Bossche, T. A1 - Kunath, B. A1 - Schallert, K. A1 - Schäpe, S. A1 - Abraham, P. E. A1 - Armengaud, J. A1 - Arntzen, M. Ø. A1 - Bassignani, A. A1 - Benndorf, D. A1 - Fuchs, S. A1 - Giannone, R. J. A1 - Griffin, T. J. A1 - Hagen, L. H. A1 - Halder, R. A1 - Henry, C. A1 - Hettich, R. L. A1 - Heyer, R. A1 - Jagtap, P. A1 - Jehmlich, N. A1 - Jensen, M. A1 - Juste, C. A1 - Kleiner, M. A1 - Langella, O. A1 - Lehmann, T. A1 - Leith, E. A1 - May, P. A1 - Mesuere, B. A1 - Miotello, G. A1 - Peters, S. L. A1 - Pible, O. A1 - Queiros, P. T. A1 - Reichl, U. A1 - Renard, B. Y. A1 - Schiebenhoefer, H. A1 - Sczyrba, A. A1 - Tanca, A. A1 - Trappe, K. A1 - Trezzi, J.-P. A1 - Uzzau, S. A1 - Verschaffelt, P. A1 - von Bergen, M. A1 - Wilmes, P. A1 - Wolf, M. A1 - Martens, L. A1 - Muth, Thilo T1 - Critical Assessment of MetaProteome Investigation (CAMPI): A multi-laboratory comparison of established workflows N2 - Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments. KW - Metaproteomics KW - Mass spectrometry KW - Data science KW - Benchmarking KW - Bioinformatics PY - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-541220 SN - 2041-1723 VL - 12 SP - 1 EP - 15 PB - Nature Publishing Group CY - London AN - OPUS4-54122 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Sajulga, R. A1 - Easterly, C. A1 - Riffle, M. A1 - Mesuere, B. A1 - Muth, Thilo A1 - Mehta, S. A1 - Kumar, P. A1 - Johnson, J. A1 - Gruening, B. A1 - Schiebenhoefer, H. A1 - Kolmeder, C. A1 - Fuchs, S. A1 - Nunn, B. A1 - Rudney, J. A1 - Griffin, T. A1 - Jagtap, P. T1 - Survey of metaproteomics software tools for functional microbiome analysis N2 - To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform. KW - Bioinformatics KW - Metaproteomics KW - Mass spectrometry PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-516358 SP - e0241503 AN - OPUS4-51635 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -