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APIR: a universal FDR-control framework for boosting peptide identification power by aggregating multiple proteomics database search algorithms

Chen, Yiling; Woyshner, Kyla; Mcdermott, MeiLu; Wang, Leo; Li, Jingyi


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  <identifier identifierType="DOI">10.5281/zenodo.5202768</identifier>
  <creators>
    <creator>
      <creatorName>Chen, Yiling</creatorName>
      <givenName>Yiling</givenName>
      <familyName>Chen</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7259-2787</nameIdentifier>
      <affiliation>Department of Statistics, University of California, Los Angeles, CA 90095</affiliation>
    </creator>
    <creator>
      <creatorName>Woyshner, Kyla</creatorName>
      <givenName>Kyla</givenName>
      <familyName>Woyshner</familyName>
      <affiliation>Department of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte CA 91010</affiliation>
    </creator>
    <creator>
      <creatorName>Mcdermott, MeiLu</creatorName>
      <givenName>MeiLu</givenName>
      <familyName>Mcdermott</familyName>
      <affiliation>Department of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte CA 91010</affiliation>
    </creator>
    <creator>
      <creatorName>Wang, Leo</creatorName>
      <givenName>Leo</givenName>
      <familyName>Wang</familyName>
      <affiliation>Departments of Pediatrics and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte CA 91010</affiliation>
    </creator>
    <creator>
      <creatorName>Li, Jingyi</creatorName>
      <givenName>Jingyi</givenName>
      <familyName>Li</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9288-5648</nameIdentifier>
      <affiliation>Department of Statistics, University of California, Los Angeles, CA 90095</affiliation>
    </creator>
  </creators>
  <titles>
    <title>APIR: a universal FDR-control framework for boosting peptide identification power by aggregating multiple proteomics database search algorithms</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-09-08</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5202768</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5202767</relatedIdentifier>
  </relatedIdentifiers>
  <version>Version 1</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The data folder includes all the processed database search results&amp;nbsp;as well as the combining results from ConsensusID and Scaffold on the complex proteomics standard dataset generated from &lt;em&gt;Pyrococcus furiosus&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;The Rcode folder contains all the&amp;nbsp;R code files for running APIR, analyzing results, and generate figures.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
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