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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>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>62758</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>17</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>16</volume>
    <type>article</type>
    <publisherName>Springer Science and Business Media LLC</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events</title>
    <abstract language="eng">Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. Augmenting these libraries with simulated mass spectra can provide the necessary references to resolve unmatched spectra, but generating high-quality data is difficult. In this study, we present FIORA, an open-source graph neural network designed to simulate tandem mass spectra. Our main contribution lies in utilizing the molecular neighborhood of bonds to learn breaking patterns and derive fragment ion probabilities. FIORA not only surpasses state-of-the-art fragmentation algorithms, ICEBERG and CFM-ID, in prediction quality, but also facilitates the prediction of additional features, such as retention time and collision cross section. Utilizing GPU acceleration, FIORA enables rapid validation of putative compound annotations and large-scale expansion of spectral reference libraries with high-quality predictions.</abstract>
    <parentTitle language="eng">Nature Communications</parentTitle>
    <identifier type="issn">2041-1723</identifier>
    <identifier type="doi">10.1038/s41467-025-57422-4</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-627585</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
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    <author>Francesco Friedrich Russo</author>
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    <title language="eng">Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Briefings in Bioinformatics</parentTitle>
    <identifier type="doi">10.1093/bib/bbac542</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-570363</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">23.02.2023</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>D. Beslic</author>
    <author>Georg Tscheuschner</author>
    <author>B. Y. Renard</author>
    <author>Michael G. Weller</author>
    <author>Thilo Muth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>De novo peptide sequencing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bioinformatics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Benchmarking study</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Monoclonal antibody</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sequence coverage</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Light chains</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Heavy chains</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>IgG</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Immunoglobulins</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Error correction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sequencing algorithm</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Preprocessing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Missing fragmentation sites</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning-based tools</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">1 Analytische Chemie; Referenzmaterialien</collection>
    <collection role="institutes" number="">1.5 Proteinanalytik</collection>
    <collection role="themenfelder" number="">Material</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/57036/Beslic-2023-BriefingsBioinformatics.pdf</file>
    <file>https://opus4.kobv.de/opus4-bam/files/57036/Beslic-2023-BriefingsBioinformatics Supplement.pdf</file>
  </doc>
</export-example>
