{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T01:57:05Z","timestamp":1772243825087,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>Somatic single nucleotide variants (SNVs) are genomic events with increasing implications in cancer treatment. The clinical standard for SNVs detection is whole genome\/exome sequencing (WGS\/WES) in matched tumor-normal samples. Yet, this is a very costly approach both economically and biologically and very often only tumor samples are sequenced. On the other hand, RNA sequencing (RNA-Seq) is the most popular technology to study gene expression, and has also the potential for a cost-effective identification of SNVs as an alternative to tumor-only WES. Here we present a method for the identification of SNVs in tumor-only RNA-Seq data putting a special focus on a small panel of clinically relevant SNVs. For evaluation purposeswe analyzed matched tumor-normal WEStumor-only RNA-Seq data from 14 cancer patients. We compared SNVs detected in i) RNA-Seq by our method, ii) WES tumor-only by Mutect2 and iii) WES matched tumor-normal by Mutect2. We did a detailed evaluation for a reduced panel of clinically relevant SNVs and reliably identified in RNA-Seq data a subset of mutations for which we had pathological annotation. Hence, RNA-Seq rises as a cost-effective option to detect in parallel gene expression as well as a small panel of clinically relevant SNVs in research.<\/jats:p>","DOI":"10.3233\/978-1-61499-896-9-217","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:25:19Z","timestamp":1740108319000},"source":"Crossref","is-referenced-by-count":0,"title":["Using RNA-Seq Data for the Detection of a Panel of Clinically Relevant Mutations"],"prefix":"10.3233","author":[{"family":"Wolff Alexander","sequence":"additional","affiliation":[]},{"family":"Perera-Bel J&uacute;lia","sequence":"additional","affiliation":[]},{"family":"Schildhaus Hans-Ulrich","sequence":"additional","affiliation":[]},{"family":"Homayounfar Kia","sequence":"additional","affiliation":[]},{"family":"Schatlo Bawarjan","sequence":"additional","affiliation":[]},{"family":"Bleckmann Annalen","sequence":"additional","affiliation":[]},{"family":"Bei&szlig;barth Tim","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences: A Learning Healthcare System"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:43:37Z","timestamp":1740109417000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-895-2&spage=217&doi=10.3233\/978-1-61499-896-9-217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-896-9-217","relation":{"is-cited-by":[{"id-type":"doi","id":"10.3389\/fonc.2021.717616","asserted-by":"object"}]},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}