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 - Van den Bossche, T. A1 - Verschaffelt, P. A1 - Schallert, K. A1 - Barsnes, H. A1 - Dawyndt, P. A1 - Benndorf, D. A1 - Renard, B. Y. A1 - Mesuere, B. A1 - Martens, L. A1 - Muth, Thilo T1 - Connecting MetaProteomeAnalyzer and PeptideShaker to Unipept for Seamless End-to-End Metaproteomics Data Analysis N2 - Although metaproteomics, the study of the collective proteome of microbial communities, has become increasingly powerful and popular over the past few years, the field has lagged behind on the availability of user-friendly, end-to-end pipelines for data analysis. We therefore describe the Connection from two commonly used metaproteomics data processing tools in the field, MetaProteomeAnalyzer and PeptideShaker, to Unipept for downstream analysis. Through these connections, direct end-to-end pipelines are built from database searching to taxonomic and functional annotation. KW - Metaproteomics KW - Bioinformatics KW - Software KW - Pipelines PY - 2020 U6 - https://doi.org/10.1021/acs.jproteome.0c00136 VL - 19 IS - 8 SP - 3562 EP - 3566 PB - ACS Publications AN - OPUS4-51331 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schiebenhoefer, H. A1 - Schallert, K. A1 - Renard, B. Y. A1 - Trappe, K. A1 - Schmid, E. A1 - Benndorf, D. A1 - Riedel, K. A1 - Muth, Thilo A1 - Fuchs, S. T1 - A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane N2 - Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow. KW - Bioinformatics KW - Protocol KW - Microbial proteomics KW - Software KW - Mass spectrometry KW - Metaproteomics PY - 2020 U6 - https://doi.org/10.1038/s41596-020-0368-7 SN - 1750-2799 VL - 15 IS - 10 SP - 3212 EP - 3239 PB - Nature Publishing Group AN - OPUS4-51335 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kuhring, M. A1 - Doellinger, J. A1 - Nitsche, A. A1 - Muth, Thilo A1 - Renard, B. Y. T1 - TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples N2 - Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt. KW - Bioinformatics KW - Mass spectrometry KW - Microbial proteomics KW - Strain identification KW - MS/MS PY - 2020 U6 - https://doi.org/10.1021/acs.jproteome.9b00714 VL - 19 IS - 6 SP - 2501 EP - 2510 PB - ACS AN - OPUS4-50942 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Beslic, D. A1 - Tscheuschner, Georg A1 - Renard, B. Y. A1 - Weller, Michael G. A1 - Muth, Thilo T1 - Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly N2 - Monoclonal antibodies are biotechnologically produced proteins with various applications in research, therapeutics and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody–antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a valuable method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. In this study, we evaluated six recently developed de novo peptide sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, PointNovo and Casanovo), which were not specifically designed for antibody data. We validated their ability to identify and assemble antibody sequences on three multi-enzymatic data sets. The deep learning-based tools Casanovo and PointNovo showed an increased peptide recall across different enzymes and data sets compared with spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra and peptides of various lengths. We achieved a sequence coverage of 97.69–99.53% on the light chains of three different antibody data sets using the de Bruijn assembler ALPS and the predictions from Casanovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies and hybrid approaches such as homology search to achieve high accuracy on long protein sequences. KW - De novo peptide sequencing KW - Bioinformatics KW - Benchmarking study KW - Monoclonal antibody KW - Mass spectrometry KW - Sequence coverage KW - Light chains KW - Heavy chains KW - IgG KW - Immunoglobulins KW - Error correction KW - Sequencing algorithm KW - Preprocessing KW - Missing fragmentation sites KW - Deep learning-based tools PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-570363 VL - 24 IS - 1 SP - 1 EP - 12 PB - Oxford University Press AN - OPUS4-57036 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -