{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:34:01Z","timestamp":1761863641118},"reference-count":7,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2017,2,22]],"date-time":"2017-02-22T00:00:00Z","timestamp":1487721600000},"content-version":"vor","delay-in-days":63,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Biomedical Research Centre"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,4,15]]},"abstract":"Abstract<\/jats:title>Summary<\/jats:title>Detecting significant associations between genetic variants and disease may prove particularly challenging when the variants are rare in the population and\/or act together with other variants to cause the disease. We have developed a statistical framework named Mutation Enrichment Gene set Analysis of Variants (MEGA-V) that specifically detects the enrichments of genetic alterations within a process in a cohort of interest. By focusing on the mutations of several genes contributing to the same function rather than on those affecting a single gene, MEGA-V increases the power to detect statistically significant associations.<\/jats:p><\/jats:sec>Availability and Implementation<\/jats:title>MEGA-V is available at https:\/\/github.com\/ciccalab\/MEGA<\/jats:p><\/jats:sec>Supplementary information<\/jats:title>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btw809","type":"journal-article","created":{"date-parts":[[2016,12,19]],"date-time":"2016-12-19T12:05:49Z","timestamp":1482149149000},"page":"1248-1249","source":"Crossref","is-referenced-by-count":6,"title":["MEGA-V: detection of variant gene sets in patient cohorts"],"prefix":"10.1093","volume":"33","author":[{"given":"Gennaro","family":"Gambardella","sequence":"first","affiliation":[{"name":"Division of Cancer Studies, King\u2019s College London, London, UK"}]},{"given":"Matteo","family":"Cereda","sequence":"additional","affiliation":[{"name":"Division of Cancer Studies, King\u2019s College London, London, UK"}]},{"given":"Lorena","family":"Benedetti","sequence":"additional","affiliation":[{"name":"Division of Cancer Studies, King\u2019s College London, London, UK"}]},{"given":"Francesca D","family":"Ciccarelli","sequence":"additional","affiliation":[{"name":"Division of Cancer Studies, King\u2019s College London, London, UK"}]}],"member":"286","published-online":{"date-parts":[[2016,12,21]]},"reference":[{"key":"2023020205025821500_btw809-B1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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