{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T19:44:33Z","timestamp":1761767073235,"version":"3.41.2"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T00:00:00Z","timestamp":1644969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:p>Several fusion genes are directly involved in the initiation and progression of cancers. Numerous bioinformatics tools have been developed to detect fusion events, but they are mainly based on RNA-seq data. The whole-exome sequencing (WES) represents a powerful technology that is widely used for disease-related DNA variant detection. In this study, we build a novel analysis pipeline called Fuseq-WES to detect fusion genes at DNA level based on the WES data. The same method applies also for targeted panel sequencing data. We assess the method to real datasets of acute myeloid leukemia (AML) and prostate cancer patients. The result shows that two of the main AML fusion genes discovered in RNA-seq data, PML-RARA and CBFB-MYH11, are detected in the WES data in 36 and 63% of the available samples, respectively. For the targeted deep-sequencing of prostate cancer patients, detection of the TMPRSS2-ERG fusion, which is the most frequent chimeric alteration in prostate cancer, is 91% concordant with a manually curated procedure based on four other methods. In summary, the overall results indicate that it is challenging to detect fusion genes in WES data with a standard coverage of \u223c 15\u201330x, where fusion candidates discovered in the RNA-seq data are often not detected in the WES data and vice versa. A subsampling study of the prostate data suggests that a coverage of at least 75x is necessary to achieve high accuracy.<\/jats:p>","DOI":"10.3389\/fgene.2022.820493","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T07:05:16Z","timestamp":1644995116000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Fusion Gene Detection Using Whole-Exome Sequencing Data in Cancer Patients"],"prefix":"10.3389","volume":"13","author":[{"given":"Wenjiang","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarath","family":"Murugan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johan","family":"Lindberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Venkatesh","family":"Chellappa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yudi","family":"Pawitan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trung Nghia","family":"Vu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2022,2,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"67","DOI":"10.4137\/CIN.S13779","article-title":"Review of Current Methods, Applications, and Data Management for the Bioinformatics Analysis of Whole Exome Sequencing","volume":"13","author":"Bao","year":"2014","journal-title":"Cancer Inform."},{"key":"B2","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1093\/bioinformatics\/bts731","article-title":"Ngsutils: a Software Suite for Analyzing and Manipulating Next-Generation Sequencing Datasets","volume":"29","author":"Breese","year":"2013","journal-title":"Bioinformatics"},{"key":"B3","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.cell.2013.09.034","article-title":"The Somatic Genomic Landscape of Glioblastoma","volume":"155","author":"Brennan","year":"2013","journal-title":"Cell"},{"key":"B4","doi-asserted-by":"publisher","first-page":"521","DOI":"10.3390\/diagnostics10080521","article-title":"Next Generation Sequencing for Gene Fusion Analysis in Lung Cancer: a Literature Review","volume":"10","author":"Bruno","year":"2020","journal-title":"Diagnostics"},{"key":"B5","doi-asserted-by":"publisher","first-page":"2050","DOI":"10.1101\/gr.222109.117","article-title":"Gridss: sensitive and specific genomic rearrangement detection using positional de bruijn graph assembly","volume":"27","author":"Cameron","year":"2017","journal-title":"Genome Res."},{"key":"B6","doi-asserted-by":"publisher","first-page":"340620","DOI":"10.1155\/2013\/340620","article-title":"State-of-the-art Fusion-Finder Algorithms Sensitivity and Specificity","volume":"2013","author":"Carrara","year":"2013","journal-title":"Biomed. 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