MicroArray/Sequencing Quality Control (MAQC/SEQC)

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href="/science-research/bioinformatics-tools">Bioinformatics Tools</a> </li> <li > <a class="current-link">MicroArray/Sequencing Quality Control (MAQC/SEQC)</a> </li> </ol> <ol class="lcds-breadcrumb visible-sm visible-xs"> <li> <a href="/science-research/bioinformatics-tools" title="Bioinformatics Tools"> Bioinformatics Tools </a> </li> </ol> </section> <main> <article id="main-content" class="article main-content container-fluid" role="article"> <header class="row content-header" role="heading" aria-level="1"> <section id="block-entityviewcontent-14" data-block-plugin-id="entity_view:node" class="block block-ctools block-entity-viewnode clearfix"> <div class="col-sm-12 col-md-8 col-md-offset-2"> <h1 class="content-title text-center">MicroArray/Sequencing Quality Control (MAQC/SEQC)</h1> <h2 class="article-subtitle text-center">About MAQC/SEQC, links to journal articles featuring MAQC/SEQC, information about RNA samples, and contact information for questions or suggestions</h2> <div class="lcds-toolbar lcds-toolbar--social"> <a href="#subscribe" class="btn btn-sm btn-default header-button-subscribe">Subscribe to Email Updates</a> <ul class="lcds-share lcds-share--default"> <li class="lcds-share__item"> <a href="https://www.facebook.com/sharer/sharer.php?u=https://www.fda.gov%2Fscience-research%2Fbioinformatics-tools%2Fmicroarraysequencing-quality-control-maqcseqc" class="lcds-share__btn lcds-share--default__btn-facebook js-share" id="fb-share" target="_blank"><span class="fa icon-facebook" aria-hidden="true"></span>Share</a> </li> <li class="lcds-share__item"> <a href="https://x.com/intent/tweet/?text=MicroArray%2FSequencing%20Quality%20Control%20%28MAQC%2FSEQC%29&url=https://www.fda.gov%2Fscience-research%2Fbioinformatics-tools%2Fmicroarraysequencing-quality-control-maqcseqc" target="_blank" class="lcds-share__btn lcds-share--default__btn-x-twitter js-share" id="twitter-share"> <svg class="icon-svg-inline" aria-hidden="true"> <use 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href="javascript:window.print();" title="Print this page" class="lcds-share__btn lcds-share--default__btn-print"><span class="fa icon-print" aria-hidden="true"></span>Print</a> </li> </ul> <div class="form-group"> </div> </div> </div> </section> </header> <div class="col-md-8 col-md-push-2" role="main"> <h4><a href="#Overview">Overview</a><br> <a href="#MAQC_IV">MAQC-IV (also known as SEQC2)</a><br> <a href="#MAQC-IIIalsoknownasSEQC">MAQC-III (also known as SEQC)</a><br> <a href="#MAQC-II">MAQC-II</a><br> <a href="#MAQC-I">MAQC-I</a><br> <a href="#Contact_Information">Contact Information</a></h4> <h4><div class=" panel-group" id="accordion_810475" role="tablist" aria-multiselectable="true"></div></h4> <h4> <div title="Journal Special Issues or Manuscript Collections Featuring MAQC/SECQ" open="no" class=" panel panel-default fda-accordion-panel"> <div class="panel-heading" role="tab" id="heading_67466972c4f84"> <h2 class="panel-title"> <a role="button" class="collapsed" data-toggle="collapse" data-parent href="#67466972c4f84" aria-controls="67466972c4f84">Journal Special Issues or Manuscript Collections Featuring MAQC/SECQ</a> </h2> </div> <div id="67466972c4f84" class="panel-collapse collapse collapsed" role="tabpanel" aria-labelledby="heading_67466972c4f84"> <div class="panel-body"> </div></div></div></h4> <p><a href="https://www.nature.com/collections/fjhdjcdefg"><em>Nature Biotechnology</em> — SEQC2 Article Collection</a> (2020-2022)</p> <p><a href="https://www.biomedcentral.com/collections/seqc2-article-collection"><em>Genome Biology</em> — SEQC2 Article Collection</a> (2021-2022)</p> <p><a href="http://www.nature.com/nbt/collections/seqc/index.html"><em>Nature Biotechnology </em>— 2014 Special Issue</a></p> <p><a href="http://www.nature.com/nbt/journal/v28/n8/index.html" target="_blank"><em>Nature Biotechnology</em> — August 2010 Issue</a></p> <p><a href="http://www.nature.com/tpj/journal/v10/n4/index.html"><em>Pharmacogenomics Journal</em> — August 2010 Issue</a></p> <p><a href="http://www.nature.com/nbt/focus/maqc/index.html"><em>Nature Biotechnology</em> — September 2006</a></p> <h4> </h4> <h4></h4> <hr> <h2><a id="Overview" name="Overview"></a>Overview</h2> <p>Microarrays and next-generation sequencing represent core technologies in pharmacogenomics and toxicogenomics; however, before these technologies can successfully and reliably be used in clinical practice and regulatory decision-making, standards and quality measures need to be developed. The MAQC project is helping improve the microarray and next-generation sequencing technologies and foster their proper applications in discovery, development and review of FDA regulated products. Everyone is invited to participate in the MAQC project.</p> <p>It is the FDA-led community wide consortium effort to address issues relating to the application of constantly evolving high-throughput genomics technologies to either assess safety and efficacy of FDA regulated products or their safe and effective use in clinical applications as in vitro diagnostic devices. The MAQC consortium completed four projects between 2005-2022 (namely MAQC I and II, and SECQ1 and 2), resulting in ~60 publications and one-quarter of which were published in <em>Nature Biotechnology</em>. Furthermore, two papers of these papers were among the most cited in Nature Biotechnology in the last 20 years.</p> <h2><a id="MAQC_IV" name="MAQC_IV"></a>MAQC-IV (also known as SEQC2)</h2> <ul> <li><a href="https://www.nature.com/collections/fjhdjcdefg"><strong>SEQC2 Article Collection in <em>Nature Biotechnology</em>: 2021-2022</strong></a></li> <li><a href="https://www.biomedcentral.com/collections/seqc2-article-collection"><strong>SEQC2 Article Collection in <em>Genome Biology</em>: 2020-2022</strong></a></li> </ul> <p>This is the fourth project of MAQC, named Sequencing Quality Control Phase 2 (SEQC2). Launched in 2016, SEQC2 aimed to develop quality-control metrics and benchmarking bioinformatics approaches for analysis of DNA-sequencing data to achieve best practice and standard analysis protocols for applying next-generation sequencing (NGS) methodologies in regulatory settings and precision medicine. Specifically, SEQC2 focused on 1) assessing reproducibility of DNA-sequencing data in germline mutation analysis, cancer screening and diagnosis, and epigenetic markers for personalized medicine; 2) benchmarking bioinformatics approaches for clinical use of DNA-sequencing data; and 3) evaluating the application of these technologies in liquid biopsy, single-cell sequencing, and for formalin-fixed paraffin-embedded samples. </p> <p>The SEQC2 effort—by a coalition of ~380 participants and ~190 organizations—was successfully completed in 2022 with over 20 manuscripts. This included a brief editorial and commentary in <em>Nature Biotechnology</em> and an editorial published in <em>Genome Biology</em> that provided a comprehensive overview of SEQC2. Manuscripts can be organized into three groups based on the level of research-to-application translation potential according to the current practice:</p> <ol> <li>Oncopanel sequencing to advance precision oncology, which dominates the current application of DNA-sequencing technologies in clinical settings; FDA has already cleared or authorized some oncopanels and liquid biopsy assays for cancer diagnosis. </li> <li>Benchmarking whole genomics sequencing for somatic and germline mutation analysis. </li> <li>Challenges and applications such as single-cell RNA-seq, epigenomics, difficult genomic regions, copy number variation, and structural variants. </li> </ol> <h2><a id="MAQC-IIIalsoknownasSEQC" name="MAQC-IIIalsoknownasSEQC"></a>MAQC-III (also known as SEQC)</h2> <ul> <li><a href="http://www.nature.com/nbt/collections/seqc/index.html" target="_blank"><strong>Special Issue of <em>Nature Collections </em>in <em>Nature Biotechnology: </em>2014</strong></a></li> </ul> <p>The third phase of the MAQC project (MAQC-III), also called Sequencing Quality Control (SEQC), aimed to assess the technical performance of NGS platforms by generating benchmark datasets with reference samples and evaluating advantages and limitations of various bioinformatics strategies in RNA and DNA analyses.</p> <p>This was an FDA-led community-wide consortium consisting of 180 researchers from 73 organizations across 12 countries.  The project aimed to:</p> <ul> <li>examine the latest tools for measuring gene activity (RNA-Seq)</li> <li>establish best practices for reproducibility across different technologies and laboratories</li> <li>evaluate the utility of these technologies in clinical and safety assessments.</li> </ul> <p>Specifically, three RNA-seq platforms (Illumina HiSeq, Life Technologies SOLiD, and Roche 454) were tested at multiple sites for reproducibility, accuracy, and information content. The project also extensively compared RNA-seq to microarray technology and evaluated the transferability of predictive models and signature genes between microarray and RNA-Seq data. The impact of various bioinformatics approaches on the downstream biological interpretations of RNA-seq results was also comprehensively examined and the utility of RNA-seq in clinical application and safety evaluation was assessed. The project was completed by the end of 2014 and generated many manuscripts (<a href="/science-research/microarray-quality-control-maqc/maqc-publications" target>visit MAQC Publications under 2014 for a list</a>). Most of the publications are available in a <a href="http://www.nature.com/nbt/collections/seqc/index.html" target="_blank"><em>Nature Collections </em>special issue</a>.</p> <h2><a id="MAQC-II" name="MAQC-II"></a>MAQC-II</h2> <ul> <li><a href="http://www.nature.com/nbt/journal/v28/n8/index.html" target="_blank"><strong><em>Nature Biotechnology</em> — August 2010 Issue</strong></a></li> <li><a href="http://www.nature.com/tpj/journal/v10/n4/index.html" target="_blank"><strong><em>Pharmacogenomics Journal</em> — August 2010</strong></a></li> </ul> <p>The second phase of the MAQC project (MAQC-II) aimed to 1) assess the capabilities and limitations of various data analysis methods in developing and validating microarray-based predictive models and 2) reach consensus on the “best practices” for development and validation of predictive models based on microarray gene expression and genotyping data for personalized medicine.</p> <p>Thirty-six teams developed classifiers for 13 endpoints—some easy, some difficult to predict—from six relatively large training data sets. These analyses collectively produced >18,000 models that were challenged by independent and blinded validation sets generated for MAQC-II. The cross-validated performance estimates for models developed under good practices are predictive of the blinded validation performance. The achievable prediction performance is largely determined by the intrinsic predictability of the endpoint, and simple data analysis methods often perform as well as more complicated approaches. Multiple models of comparable performance can be developed for a given endpoint and the stability of gene lists correlates with endpoint predictability. Importantly, similar conclusions were reached when >12,000 new models were generated by swapping the original training and validation sets. </p> <h2><a id="MAQC-I" name="MAQC-I"></a>MAQC-I</h2> <ul> <li><a href="http://www.nature.com/nbt/focus/maqc/index.html" target="_blank"><strong><em>Nature Biotechnology <font color="#0c2c58">—</font> </em>September 2006 Issue</strong></a></li> </ul> <p>The first phase of the MAQC project (MAQC-I) aimed to: </p> <ul> <li>provide quality control (QC) tools to the microarray community to avoid procedural failures </li> <li>develop guidelines for microarray data analysis by providing the public with large reference datasets along with readily accessible reference RNA samples</li> <li>establish QC metrics and thresholds for objectively assessing the performance achievable by various microarray platforms </li> <li>evaluate the advantages and disadvantages of various data analysis methods</li> </ul> <p>MAQC-I involved six FDA Centers, major providers of microarray platforms and RNA samples, EPA, NIST, academic laboratories, and other stakeholders. Two human reference RNA samples were selected, and differential gene expression levels between the two samples were calibrated with microarrays and other technologies (e.g., QRT-PCR). The resulting microarray datasets were used for assessing the precision and cross-platform/laboratory comparability of microarrays, and the QRT-PCR datasets enabled evaluation of the nature and magnitude of any systematic biases that may exist between microarrays and QRT-PCR. The availability of the well-characterized RNA samples combined with the resulting microarray and QRT-PCR datasets, which were made readily accessible to the scientific community, allow individual laboratories to more easily identify and correct procedural failures. </p> <h2>RNA Samples</h2> <p>The availability of the calibrated RNA samples combined with the resulting microarray and QRT-PCR datasets, which will be made readily accessible to the microarray community, will allow individual laboratories to more easily identify and correct procedural failures. </p> <h2><a name="Contact_Information"></a>Contact Information</h2> <p>Please address any other questions and suggestions to <a href="mailto:Weida.Tong@fda.hhs.gov?subject=MAQC">Weida Tong, Ph.D.,</a> or <a href="mailto:NCTRBioinformaticsSupport@fda.hhs.gov?subject=MAQC">NCTR Bioinformatics Support</a>.<br>  </p> <hr> <h2>Resources For You</h2> <ul> <li><a data-entity-substitution="canonical" data-entity-type="node" 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