{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T17:33:07Z","timestamp":1783963987598,"version":"3.55.0"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2019,8,2]],"date-time":"2019-08-02T00:00:00Z","timestamp":1564704000000},"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":["U41 HG009293"],"award-info":[{"award-number":["U41 HG009293"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["T32 HG00040"],"award-info":[{"award-number":["T32 HG00040"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Michigan Undergraduate Research Opportunity Program"},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Genome-wide association studies have revealed that 88% of disease-associated single-nucleotide polymorphisms (SNPs) reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>SEMpl estimates transcription factor-binding affinity by observing differences in chromatin immunoprecipitation followed by deep sequencing signal intensity for SNPs within functional transcription factor-binding sites (TFBSs) genome-wide. By cataloging the effects of every possible mutation within the TFBS motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>SEMpl is available from https:\/\/github.com\/Boyle-Lab\/SEM_CPP.<\/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\/btz612","type":"journal-article","created":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T15:11:49Z","timestamp":1564672309000},"page":"364-372","source":"Crossref","is-referenced-by-count":58,"title":["Predicting the effects of SNPs on transcription factor binding affinity"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4288-4567","authenticated-orcid":false,"given":"Sierra S","family":"Nishizaki","sequence":"first","affiliation":[{"name":"Department of Human Genetics, University of Michigan , Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalie","family":"Ng","sequence":"additional","affiliation":[{"name":"Department of Human Genetics, Stanford University , Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengcheng","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics , University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert S","family":"Porter","sequence":"additional","affiliation":[{"name":"Department of Human Genetics, University of Michigan , Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cody","family":"Morterud","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics , University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colten","family":"Williams","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics , University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Courtney","family":"Asman","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics , University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica A","family":"Switzenberg","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics , University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan P","family":"Boyle","sequence":"additional","affiliation":[{"name":"Department of Human Genetics, University of Michigan , Ann Arbor, MI 48109, USA"},{"name":"Department of Computational Medicine and Bioinformatics , University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,8,2]]},"reference":[{"key":"2023013112075810900_btz612-B1","doi-asserted-by":"crossref","first-page":"2434","DOI":"10.1021\/bi101955f","article-title":"Equilibrium unfolding studies of monellin: the double-chain variant appears to be more stable than the single-chain variant","volume":"50","author":"Aghera","year":"2011","journal-title":"Biochemistry"},{"key":"2023013112075810900_btz612-B2","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat. 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