<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>8449</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>5854</issue>
    <volume>12</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A proteomics sample metadata representation for multiomics integration and big data analysis</title>
    <abstract language="eng">The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.</abstract>
    <parentTitle language="eng">Nature Communications</parentTitle>
    <identifier type="doi">https://doi.org/10.1038/s41467-021-26111-3</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2021-09-16</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Chengxin Dai</author>
    <submitter>Julianus Pfeuffer</submitter>
    <author>Anja Füllgrabe</author>
    <author>Julianus Pfeuffer</author>
    <author>Elizaveta M. Solovyeva</author>
    <author>Jingwen Deng</author>
    <author>Pablo Moreno</author>
    <author>Selvakumar Kamatchinathan</author>
    <author>Deepti Jaiswal Kundu</author>
    <author>Nancy George</author>
    <author>Silvie Fexovy</author>
    <author>Björn Grüning</author>
    <author>Melanie Christine Föll</author>
    <author>Johannes Griss</author>
    <author>Marc Vaudel</author>
    <author>Enrique Audain</author>
    <author>Marie Locard-Paulet</author>
    <author>Michael Turewicz</author>
    <author>Martin Eisenacher</author>
    <author>Julian Uszkoreit</author>
    <author>Tim Van Den Bossche</author>
    <author>Veit Schwämmle</author>
    <author>Henry Webel</author>
    <author>Stefan Schulze</author>
    <author>David Bouyssié</author>
    <author>Savita Jayaram</author>
    <author>Vinay Kumar Duggineni</author>
    <author>Patroklos Samaras</author>
    <author>Mathias Wilhelm</author>
    <author>Meena Choi</author>
    <author>Mingxun Wang</author>
    <author>Oliver Kohlbacher</author>
    <author>Alvis Brazma</author>
    <author>Irene Papatheodorou</author>
    <author>Nuno Bandeira</author>
    <author>Eric W. Deutsch</author>
    <author>Juan Antonio Vizcaíno</author>
    <author>Mingze Bai</author>
    <author>Timo Sachsenberg</author>
    <author>Lev I. Levitsky</author>
    <author>Yasset Perez-Riverol</author>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>9095</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2114</pageFirst>
    <pageLast>2123</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>22</volume>
    <type>article</type>
    <publisherName>American Chemical Society</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-05-23</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">LFQ-Based Peptide and Protein Intensity Differential Expression Analysis</title>
    <abstract language="eng">Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.</abstract>
    <parentTitle language="eng">J. Proteome Res.</parentTitle>
    <identifier type="doi">10.1021/acs.jproteome.2c00812</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Mingze Bai</author>
    <submitter>Julianus Pfeuffer</submitter>
    <author>Jingwen Deng</author>
    <author>Chengxin Dai</author>
    <author>Julianus Pfeuffer</author>
    <author>Timo Sachsenberg</author>
    <author>Yasset Perez-Riverol</author>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
</export-example>
