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Not every gene is special: Modelling scale controls the false discovery rate when analysing high-throughput sequencing data.

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Title: Not every gene is special: Modelling scale controls the false discovery rate when analysing high-throughput sequencing data.
Authors: Dos Santos SJ; Department of Biochemistry, Western University, London, Ontario, Canada., Murariu AC; Department of Biochemistry, Western University, London, Ontario, Canada., Silverman JD; Program in Bioinformatics and Genomics, Pennsylvania State University, University Park, Pennsylvania, United States of America.; College of Information Science and Technology, Pennsylvania State University, State College, Pennsylvania, United States of America.; Department of Statistics, Pennsylvania State University, State College, Pennsylvania, United States of America.; Department of Medicine, Pennsylvania State University, Hershey, Pennsylvania, United States of America.; Institute for Computational and Data Science, Pennsylvania State University, Hershey, Pennsylvania, United States of America., Gloor GB; Department of Biochemistry, Western University, London, Ontario, Canada.
Source: PLoS computational biology [PLoS Comput Biol] 2026 Sep 08; Vol. 22 (9), pp. e1014728. Date of Electronic Publication: 2026 Sep 08 (Print Publication: 2026).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, N.I.H., Extramural
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Dos+Santos+SJ%22">Dos Santos SJ</searchLink>; Department of Biochemistry, Western University, London, Ontario, Canada.<br /><searchLink fieldCode="AU" term="%22Murariu+AC%22">Murariu AC</searchLink>; Department of Biochemistry, Western University, London, Ontario, Canada.<br /><searchLink fieldCode="AU" term="%22Silverman+JD%22">Silverman JD</searchLink>; Program in Bioinformatics and Genomics, Pennsylvania State University, University Park, Pennsylvania, United States of America.; College of Information Science and Technology, Pennsylvania State University, State College, Pennsylvania, United States of America.; Department of Statistics, Pennsylvania State University, State College, Pennsylvania, United States of America.; Department of Medicine, Pennsylvania State University, Hershey, Pennsylvania, United States of America.; Institute for Computational and Data Science, Pennsylvania State University, Hershey, Pennsylvania, United States of America.<br /><searchLink fieldCode="AU" term="%22Gloor+GB%22">Gloor GB</searchLink>; Department of Biochemistry, Western University, London, Ontario, Canada.
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  Data: <searchLink fieldCode="JN" term="%22101238922%22">PLoS computational biology</searchLink> [PLoS Comput Biol] 2026 Sep 08; Vol. 22 (9), pp. e1014728. <i>Date of Electronic Publication: </i>2026 Sep 08 (<i>Print Publication: </i>2026).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Public+Library+of+Science%22">Public Library of Science </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101238922 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>1553-7358 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%221553734X%22">1553734X </searchLink><i>NLM ISO Abbreviation: </i>PLoS Comput Biol <i>Subsets: </i>MEDLINE
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