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  <doc>
    <id>50942</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2501</pageFirst>
    <pageLast>2510</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>19</volume>
    <type>article</type>
    <publisherName>ACS</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Journal of Proteome Research</parentTitle>
    <identifier type="doi">10.1021/acs.jproteome.9b00714</identifier>
    <enrichment key="date_peer_review">29.06.2020</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>M. Kuhring</author>
    <author>J. Doellinger</author>
    <author>A. Nitsche</author>
    <author>Thilo Muth</author>
    <author>B. Y. Renard</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bioinformatics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microbial proteomics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Strain identification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MS/MS</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="themenfelder" number="">Chemische Charakterisierung und Spurenanalytik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
  </doc>
  <doc>
    <id>51331</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3562</pageFirst>
    <pageLast>3566</pageLast>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>19</volume>
    <type>article</type>
    <publisherName>ACS Publications</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Connecting MetaProteomeAnalyzer and PeptideShaker to Unipept for Seamless End-to-End Metaproteomics Data Analysis</title>
    <abstract language="eng">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.&#13;
Through these connections, direct end-to-end pipelines are built from database searching to taxonomic and functional annotation.</abstract>
    <parentTitle language="eng">Journal of Proteome Research</parentTitle>
    <identifier type="doi">10.1021/acs.jproteome.0c00136</identifier>
    <enrichment key="date_peer_review">30.09.2020</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>T. Van den Bossche</author>
    <author>P. Verschaffelt</author>
    <author>K. Schallert</author>
    <author>H. Barsnes</author>
    <author>P. Dawyndt</author>
    <author>D. Benndorf</author>
    <author>B. Y. Renard</author>
    <author>B. Mesuere</author>
    <author>L. Martens</author>
    <author>Thilo Muth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Metaproteomics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bioinformatics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Software</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pipelines</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
  </doc>
  <doc>
    <id>51335</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3212</pageFirst>
    <pageLast>3239</pageLast>
    <pageNumber/>
    <edition/>
    <issue>10</issue>
    <volume>15</volume>
    <type>article</type>
    <publisherName>Nature Publishing Group</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Nature Protocols</parentTitle>
    <identifier type="doi">10.1038/s41596-020-0368-7</identifier>
    <identifier type="issn">1750-2799</identifier>
    <enrichment key="date_peer_review">30.09.2020</enrichment>
    <author>H. Schiebenhoefer</author>
    <author>K. Schallert</author>
    <author>B. Y. Renard</author>
    <author>K. Trappe</author>
    <author>E. Schmid</author>
    <author>D. Benndorf</author>
    <author>K. Riedel</author>
    <author>Thilo Muth</author>
    <author>S. Fuchs</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bioinformatics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Protocol</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microbial proteomics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Software</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Metaproteomics</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
  </doc>
  <doc>
    <id>51633</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4380</pageFirst>
    <pageLast>4388</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>19</volume>
    <type>article</type>
    <publisherName>ACS</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Perspective on Proteomics for Virus Detection in Clinical Samples</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Journal of Proteome Research Special Issue: Proteomics in Pandemic Disease</parentTitle>
    <identifier type="doi">10.1021/acs.jproteome.0c00674</identifier>
    <identifier type="issn">1535-3907</identifier>
    <enrichment key="date_peer_review">25.11.2020</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>M. Grossegesse</author>
    <author>F. Hartkopf</author>
    <author>A. Nitsche</author>
    <author>L. Schaade</author>
    <author>J. Doellinger</author>
    <author>Thilo Muth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>COVID-19</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Virus diagnostics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Virus detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Targeted mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Proteomics</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
  </doc>
  <doc>
    <id>51635</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>e0241503</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Survey of metaproteomics software tools for functional microbiome analysis</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">PLoS ONE</parentTitle>
    <identifier type="doi">10.1371/journal.pone.0241503</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-516358</identifier>
    <enrichment key="date_peer_review">25.11.2020</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC0 1.0 - Universell - Public Domain Dedication</licence>
    <author>R. Sajulga</author>
    <author>C. Easterly</author>
    <author>M. Riffle</author>
    <author>B. Mesuere</author>
    <author>Thilo Muth</author>
    <author>S. Mehta</author>
    <author>P. Kumar</author>
    <author>J. Johnson</author>
    <author>B. Gruening</author>
    <author>H. Schiebenhoefer</author>
    <author>C. Kolmeder</author>
    <author>S. Fuchs</author>
    <author>B. Nunn</author>
    <author>J. Rudney</author>
    <author>T. Griffin</author>
    <author>P. Jagtap</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bioinformatics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Metaproteomics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mass spectrometry</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/51635/journal.pone.0241503.pdf</file>
  </doc>
  <doc>
    <id>54122</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>15</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>12</volume>
    <type>article</type>
    <publisherName>Nature Publishing Group</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Critical Assessment of MetaProteome Investigation (CAMPI): A multi-laboratory comparison of established workflows</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Nature communications</parentTitle>
    <identifier type="doi">10.1038/s41467-021-27542-8</identifier>
    <identifier type="issn">2041-1723</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-541220</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">22.12.2021</enrichment>
    <enrichment key="RelatedIdentifier">https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/58087</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>T. Van Den Bossche</author>
    <author>B. Kunath</author>
    <author>K. Schallert</author>
    <author>S. Schäpe</author>
    <author>P. E. Abraham</author>
    <author>J. Armengaud</author>
    <author>M. Ø. Arntzen</author>
    <author>A. Bassignani</author>
    <author>D. Benndorf</author>
    <author>S. Fuchs</author>
    <author>R. J. Giannone</author>
    <author>T. J. Griffin</author>
    <author>L. H. Hagen</author>
    <author>R. Halder</author>
    <author>C. Henry</author>
    <author>R. L. Hettich</author>
    <author>R. Heyer</author>
    <author>P. Jagtap</author>
    <author>N. Jehmlich</author>
    <author>M. Jensen</author>
    <author>C. Juste</author>
    <author>M. Kleiner</author>
    <author>O. Langella</author>
    <author>T. Lehmann</author>
    <author>E. Leith</author>
    <author>P. May</author>
    <author>B. Mesuere</author>
    <author>G. Miotello</author>
    <author>S. L. Peters</author>
    <author>O. Pible</author>
    <author>P. T. Queiros</author>
    <author>U. Reichl</author>
    <author>B. Y. Renard</author>
    <author>H. Schiebenhoefer</author>
    <author>A. Sczyrba</author>
    <author>A. Tanca</author>
    <author>K. Trappe</author>
    <author>J.-P. Trezzi</author>
    <author>S. Uzzau</author>
    <author>P. Verschaffelt</author>
    <author>M. von Bergen</author>
    <author>P. Wilmes</author>
    <author>M. Wolf</author>
    <author>L. Martens</author>
    <author>Thilo Muth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Metaproteomics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data science</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Benchmarking</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bioinformatics</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/54122/VanDenBossche_et_al_2021_NComms.pdf</file>
  </doc>
  <doc>
    <id>52202</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>35</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>9</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action</title>
    <abstract language="eng">Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability.&#13;
&#13;
Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin.</abstract>
    <parentTitle language="eng">F1000 Research</parentTitle>
    <identifier type="doi">10.12688/f1000research.23224.2</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-522029</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">03.03.2021</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>H. Anzt</author>
    <author>F. Bach</author>
    <author>S. Druskat</author>
    <author>F. Löffler</author>
    <author>A. Loewe</author>
    <author>B. Y. Renard</author>
    <author>G. Seemann</author>
    <author>A. Struck</author>
    <author>E. Achhammer</author>
    <author>P. Aggarwal</author>
    <author>F. Appel</author>
    <author>M. Bader</author>
    <author>L. Brusch</author>
    <author>C. Busse</author>
    <author>G. Chourdakis</author>
    <author>P. W. Dabrowski</author>
    <author>P. Ebert</author>
    <author>B. Flemisch</author>
    <author>S. Friedl</author>
    <author>B. Fritzsch</author>
    <author>M. D. Funk</author>
    <author>V. Gast</author>
    <author>F. Goth</author>
    <author>J. Grad</author>
    <author>J. Hegewald</author>
    <author>S. Hermann</author>
    <author>F. Hohmann</author>
    <author>S. Janosch</author>
    <author>D. Kutra</author>
    <author>J. Linxweiler</author>
    <author>Thilo Muth</author>
    <author>W. Peters-Kottig</author>
    <author>F. Rack</author>
    <author>F. H. C. Raters</author>
    <author>S. Rave</author>
    <author>G. Reina</author>
    <author>M. Reißig</author>
    <author>T. Ropinski</author>
    <author>J. Schaarschmidt</author>
    <author>H. Seibold</author>
    <author>J. P. Thiele</author>
    <author>B. Uekermann</author>
    <author>S. Unger</author>
    <author>R. Weeber</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Research Software</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sustainable Software Development</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Academic Software</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Software Infrastructure</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Software Training</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Software Licensing</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/52202/Anzt2021_revised.pdf</file>
  </doc>
  <doc>
    <id>52207</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1861</pageFirst>
    <pageLast>1874</pageLast>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>105</volume>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Tracking changes in adaptation to suspension growth for MDCK cells: cell growth correlates with levels of metabolites, enzymes and proteins</title>
    <abstract language="eng">Adaptations of animal cells to growth in suspension culture concern in particular viral vaccine production, where very specific aspects of virus-host cell interaction need to be taken into account to achieve high cell specific yields and overall process productivity. So far, the complexity of alterations on the metabolism, enzyme, and proteome level required for adaptation is only poorly understood. In this study, for the first time, we combined several complex analytical approaches with the aim to track cellular changes on different levels and to unravel interconnections and correlations. Therefore, a Madin-Darby canine kidney (MDCK) suspension cell line, adapted earlier to growth in suspension, was cultivated in a 1-L bioreactor. Cell concentrations and cell volumes, extracellular metabolite concentrations, and intracellular enzyme activities were determined. The experimental data set was used as the input for a segregated growth model that was already applied to describe the growth dynamics of the parental adherent cell line. In addition, the cellular proteome was analyzed by liquid chromatography coupled to tandem mass spectrometry using a label-free protein quantification method to unravel altered cellular processes for the suspension and the adherent cell line. Four regulatory mechanisms were identified as a response of the adaptation of adherent MDCK cells to growth in suspension. These regulatory mechanisms were linked to the proteins caveolin, cadherin-1, and pirin. Combining cell, metabolite, enzyme, and protein measurements with mathematical modeling generated a more holistic view on cellular processes involved in the adaptation of an adherent cell line to suspension growth.</abstract>
    <parentTitle language="eng">Applied Microbiology and Biotechnology</parentTitle>
    <identifier type="doi">10.1007/s00253-021-11150-z</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-522070</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">03.03.2021</enrichment>
    <author>S. Pech</author>
    <author>M. Rehberg</author>
    <author>R. Janke</author>
    <author>D. Benndorf</author>
    <author>Y. Genzel</author>
    <author>Thilo Muth</author>
    <author>A. Sickmann</author>
    <author>E. Rapp</author>
    <author>U. Reichl</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MDCK cell</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Proteome</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Metabolism</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Enzyme activity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Suspension growth</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/52207/Pech2021_Article_TrackingChangesInAdaptationToS.pdf</file>
  </doc>
  <doc>
    <id>54229</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>243</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>9</volume>
    <type>article</type>
    <publisherName>BMC</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Metaproteomics Initiative: a coordinated approach for propelling the functional characterization of microbiomes</title>
    <abstract language="eng">Through connecting genomic and metabolic information, metaproteomics is an essential approach for understanding how microbiomes function in space and time. The international metaproteomics community is delighted to announce the launch of the Metaproteomics Initiative (www.metaproteomics.org), the goal of which is to promote dissemination of metaproteomics fundamentals, advancements, and applications through collaborative networking in microbiome research. The Initiative aims to be the central information hub and open meeting place where newcomers and experts interact to communicate, standardize, and accelerate experimental and bioinformatic methodologies in this feld. We invite the entire microbiome community to join and discuss potential synergies at the interfaces with other disciplines, and to collectively promote innovative approaches to gain deeper insights into microbiome functions and dynamics.</abstract>
    <parentTitle language="eng">Microbiome</parentTitle>
    <identifier type="doi">10.1186/s40168-021-01176-w</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-542290</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">24.01.2022</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC0 1.0 - Universell - Public Domain Dedication</licence>
    <author>T. Van Den Bossche</author>
    <author>M. O. Arntzen</author>
    <author>D. Becher</author>
    <author>D. Benndorf</author>
    <author>V. G. H. Eijsink</author>
    <author>C. Henry</author>
    <author>P. D. Jagtap</author>
    <author>N. Jehmlich</author>
    <author>C. Juste</author>
    <author>B. J. Kunath</author>
    <author>B. Mesuere</author>
    <author>Thilo Muth</author>
    <author>P. B. Pope</author>
    <author>J. Seifert</author>
    <author>A. Tanca</author>
    <author>S. Uzzau</author>
    <author>P. Wilmes</author>
    <author>R. L. Hettich</author>
    <author>J. Armengaud</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microbiome</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Metaproteomics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Networking</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Meta-Omics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Interactions</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Education</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/54229/VanDenBossche2021.pdf</file>
  </doc>
  <doc>
    <id>54758</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>378</pageFirst>
    <pageLast>388</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>4</volume>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides</title>
    <abstract language="eng">Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce ‘ad hoc learning of fragmentation’ (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%.</abstract>
    <parentTitle language="eng">Nature Machine Intelligence</parentTitle>
    <identifier type="doi">10.1038/s42256-022-00467-7</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-547580</identifier>
    <identifier type="issn">2522-5839</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">05.05.2022</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>T. Altenburg</author>
    <author>S. Giese</author>
    <author>S. Wang</author>
    <author>Thilo Muth</author>
    <author>B.Y. Renard</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Peptide identification</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/54758/Altenburg_et_al-2022-Nature_Machine_Intelligence.pdf</file>
  </doc>
</export-example>
