{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T17:16:33Z","timestamp":1783358193441,"version":"3.54.6"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003977","name":"Israel Science Foundation","doi-asserted-by":"publisher","award":["1124\/25"],"award-info":[{"award-number":["1124\/25"]}],"id":[{"id":"10.13039\/501100003977","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001698","name":"Israel Cancer Research Fund","doi-asserted-by":"publisher","award":["23\u2013204-RCDA"],"award-info":[{"award-number":["23\u2013204-RCDA"]}],"id":[{"id":"10.13039\/100001698","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Eric and Wendy Schmidt Fund for Strategic Innovation"},{"name":"Ruth and Bruce Rappaport Technion Integrated Cancer Center"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Formalin-fixed paraffin-embedded (FFPE) tissues are widely used in clinical and research settings, yet their use for detecting somatic mutations from RNA sequencing (RNA-seq) is hindered by artefactual mutations introduced by cytosine deamination and strand-specific damage. Existing FFPE noise-filtering tools are tailored to DNA sequencing (DNA-seq) and rely on strand bias, rendering them unsuitable for RNA-seq. Here, we present FFixR, a machine learning\u2013based framework that filters FFPE-induced artefacts from RNA-seq data without requiring matched-normal samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Trained on FFPE melanoma samples with matched DNA, FFixR leverages allele-specific read counts, variant features, and mutational signature probabilities. FFixR removed up to 98% of artefactual mutations while maintaining \u223c92% recall of true variants. SHAP analysis revealed key feature interactions guiding model decisions. When applied to independent cohorts, FFixR restored the correlation between RNA- and DNA-derived tumor mutational burden (R2 = 0.881) and recovered biologically meaningful mutational signatures. FFixR enables accurate somatic variant calling from FFPE RNA-seq data, expanding the utility of archival samples for research and clinical applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>FFixR tool is freely available on the web at https:\/\/github.com\/yizhak-lab-ccg\/FFixR and https:\/\/doi.org\/10.6084\/m9.figshare.31998315. The repository also includes a readme file describing the inputs, outputs and the entire pipeline. The results presented here were produced using v1.0.0.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag405","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T11:39:06Z","timestamp":1782214746000},"source":"Crossref","is-referenced-by-count":0,"title":["FFixR: a machine learning framework for accurate somatic mutation calling from FFPE RNA-seq data in cancer"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1252-3106","authenticated-orcid":false,"given":"Or","family":"Livne","sequence":"first","affiliation":[{"name":"Department of Cell Biology and Cancer Science, The Ruth and Bruce Rappaport Faculty of Medicine, Technion\u2014Israel Institute of Technology , Haifa, 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