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 DO - 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 - 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 DO - 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 DO - 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 - Grossegesse, M. A1 - Hartkopf, F. A1 - Nitsche, A. A1 - Schaade, L. A1 - Doellinger, J. A1 - Muth, Thilo T1 - Perspective on Proteomics for Virus Detection in Clinical Samples N2 - One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted. KW - COVID-19 KW - Mass spectrometry KW - Virus diagnostics KW - Virus detection KW - Targeted mass spectrometry KW - Proteomics PY - 2020 DO - https://doi.org/10.1021/acs.jproteome.0c00674 SN - 1535-3907 VL - 19 IS - 11 SP - 4380 EP - 4388 PB - ACS AN - OPUS4-51633 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 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-516358 DO - https://doi.org/10.1371/journal.pone.0241503 SP - e0241503 AN - OPUS4-51635 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Portella, Pedro Dolabella A1 - Skrotzki, Birgit A1 - Muth, Thilo T1 - Die Digitalisierung der Materialwissenschaft und Werkstofftechnik - ein Überblick N2 - Die Bereitstellung von zuverlässigen Werkstoffdaten stand für die Werkstoffprüfung seit dem 19. Jahrhundert in zentraler Stelle. Mit der zunehmender Digitalisierung unserer Gesellschaft gewinnt das Datenmanagement insbesondere im Hinblick auf die Modellierungs- und Simulationsaktivitäten an Bedeutung. In unserem Beitrag beschreiben wir die gegenwärtigen Aktivitäten in Deutschland – insbesondere die NFDI-MatWerk und die BMBF-Initiative MaterialDigital – sowie in Europa und Übersee. Abschließend heben wir die Auswirkungen auf die technische und akademische Aus- und Weiterbildung. T2 - DVM - Tagung Werkstoffprüfung CY - Online meeting DA - 03.12.2020 KW - Werkstoffdaten KW - Digitalisierung KW - Materialwissenschaft PY - 2020 AN - OPUS4-52044 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -