{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:04Z","timestamp":1772138044925,"version":"3.50.1"},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:00:00Z","timestamp":1617321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National institutes of health","doi-asserted-by":"publisher","award":["MH113715"],"award-info":[{"award-number":["MH113715"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014370","name":"Simons Foundation Autism Research Initiative","doi-asserted-by":"publisher","award":["606768"],"award-info":[{"award-number":["606768"]}],"id":[{"id":"10.13039\/100014370","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81730036"],"award-info":[{"award-number":["81730036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81525007"],"award-info":[{"award-number":["81525007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>As sequencing technologies and analysis pipelines evolve, de novo mutation (DNM) calling tools must be adapted. Therefore, a flexible approach is needed that can accurately identify DNMs from genome or exome sequences from a variety of datasets and variant calling pipelines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we describe SynthDNM, a random-forest based classifier that can be readily adapted to new sequencing or variant-calling pipelines by applying a flexible approach to constructing simulated training examples from real data. The optimized SynthDNM classifiers predict de novo SNPs and indels with robust accuracy across multiple methods of variant calling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availabilityand implementation<\/jats:title>\n                    <jats:p>SynthDNM is freely available on Github (https:\/\/github.com\/james-guevara\/synthdnm).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab225","type":"journal-article","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T15:29:42Z","timestamp":1617290982000},"page":"3640-3641","source":"Crossref","is-referenced-by-count":3,"title":["Customized\n                    <i>de novo<\/i>\n                    mutation detection for any variant calling pipeline: SynthDNM"],"prefix":"10.1093","volume":"37","author":[{"given":"Aojie","family":"Lian","sequence":"first","affiliation":[{"name":"Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University , Changsha, Hunan, 410008 China"},{"name":"Department of Psychiatry, University of California San Diego , La Jolla, CA, 92093 USA"}]},{"given":"James","family":"Guevara","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, University of California San Diego , La Jolla, CA, 92093 USA"}]},{"given":"Kun","family":"Xia","sequence":"additional","affiliation":[{"name":"Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University , Changsha, Hunan, 410008 China"}]},{"given":"Jonathan","family":"Sebat","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, University of California San Diego , La Jolla, CA, 92093 USA"}]}],"member":"286","published-online":{"date-parts":[[2021,4,2]]},"reference":[{"key":"2023051608573343500_btab225-B1","doi-asserted-by":"crossref","first-page":"eaat6576","DOI":"10.1126\/science.aat6576","article-title":"Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder","volume":"362","author":"An","year":"2018","journal-title":"Science"},{"key":"2023051608573343500_btab225-B2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/s41525-019-0093-8","article-title":"Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes","volume":"4","author":"Feliciano","year":"2019","journal-title":"NPJ Genomic Med"},{"key":"2023051608573343500_btab225-B3","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1038\/nature13908","article-title":"The contribution of de novo coding mutations to autism spectrum disorder","volume":"515","author":"Iossifov","year":"2014","journal-title":"Nature"},{"key":"2023051608573343500_btab225-B4","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1038\/nature11396","article-title":"Rate of de novo mutations and the importance of father\u2019s age to disease risk","volume":"488","author":"Kong","year":"2012","journal-title":"Nature"},{"key":"2023051608573343500_btab225-B5","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1038\/nbt.4235","article-title":"A universal SNP and small-indel variant caller using deep neural networks","volume":"36","author":"Poplin","year":"2018","journal-title":"Nat. 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Genet"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab225\/37466692\/btab225.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/20\/3640\/50338471\/btab225.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/20\/3640\/50338471\/btab225.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T04:58:09Z","timestamp":1684213089000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/20\/3640\/6209072"}},"subtitle":[],"editor":[{"given":"Inanc","family":"Birol","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,4,2]]},"references-count":8,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2021,10,25]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab225","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.02.10.427198","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,10,15]]},"published":{"date-parts":[[2021,4,2]]}}}