20 Nisan 2021 Veri seti Açık Erişim

Batuhan KISAKOL; Şahin SARIHAN; Mehmet Arif ERGÜN; Mehmet BAYSAN

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/114556</identifier>
  <creators>
    <creator>
      <creatorName>Batuhan KISAKOL</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8732-5216</nameIdentifier>
      <affiliation>Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland</affiliation>
    </creator>
    <creator>
      <creatorName>Şahin SARIHAN</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2597-1001</nameIdentifier>
      <affiliation>Computer Engineering Department, Faculty of Engineering, Marmara University, İstanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Mehmet Arif ERGÜN</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9399-1530</nameIdentifier>
      <affiliation>Computer Engineering Department, Faculty of Computer and Informatics Engineering, İstanbul Technical University, İstanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Mehmet BAYSAN</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7359-2965</nameIdentifier>
      <affiliation>Computer Engineering Department, Faculty of Computer and Informatics Engineering, İstanbul Technical University, İstanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Detailed Evaluation Of Cancer Sequencing Pipelines In Different Microenvironments And Heterogeneity Levels</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Clinical bioinformatics</subject>
    <subject>next generation sequencing</subject>
    <subject>cancer</subject>
    <subject>mapping algorithms</subject>
    <subject>variant discovery algorithms</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-04-20</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/114556</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.3906/biy-2008-8-sup</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The importance of next generation sequencing (NGS) rises in cancer research as accessing this key technology becomes easier for researchers. The sequence data created by NGS technologies must be processed by various bioinformatics algorithms within a pipeline in order to convert raw data to meaningful information. Mapping and variant calling are the two main steps of these analysis pipelines, and many algorithms are available for these steps. Therefore, detailed benchmarking of these algorithms in different scenarios is crucial for the efficient utilization of sequencing technologies. In this study, we compared the performance of twelve pipelines (three mapping and four variant discovery algorithms) with recommended settings to capture single nucleotide variants. We observed significant discrepancy in variant calls among tested pipelines for different heterogeneity levels in real and simulated samples with overall high specificity and low sensitivity. Additional to the individual evaluation of pipelines, we also constructed and tested the performance of pipeline combinations. In these analyses, we observed that certain pipelines complement each other much better than others and display superior performance than individual pipelines. This suggests that adhering to a single pipeline is not optimal for cancer sequencing analysis and sample heterogeneity should be considered in algorithm optimization.&lt;/p&gt;</description>
  </descriptions>
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