{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T05:42:40Z","timestamp":1782884560630,"version":"3.54.5"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Charles A. King Trust Postdoctoral Research Fellowship Program"},{"name":"Sara Elisabeth O\u2019Brien Trust"},{"DOI":"10.13039\/100002486","name":"Bank of America","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100002486","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005416","name":"Norwegian Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Oslo through the Centre for Molecular Medicine Norway"},{"DOI":"10.13039\/100000054","name":"US National Cancer Institute","doi-asserted-by":"publisher","award":["R35CA220523"],"award-info":[{"award-number":["R35CA220523"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K25HL133599"],"award-info":[{"award-number":["K25HL133599"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA\u2013target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https:\/\/kuijjer.shinyapps.io\/puma_gtex\/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>PUMA is available in C++, MATLAB and Python on GitHub (https:\/\/github.com\/kuijjerlab and https:\/\/netzoo.github.io\/).<\/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\/btaa571","type":"journal-article","created":{"date-parts":[[2020,6,10]],"date-time":"2020-06-10T07:31:50Z","timestamp":1591774310000},"page":"4765-4773","source":"Crossref","is-referenced-by-count":29,"title":["PUMA: PANDA Using MicroRNA Associations"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6280-3130","authenticated-orcid":false,"given":"Marieke L","family":"Kuijjer","sequence":"first","affiliation":[{"name":"Centre for Molecular Medicine Norway, University of Oslo , Oslo 0318, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maud","family":"Fagny","sequence":"additional","affiliation":[{"name":"UMR7206 Eco-Anthropologie, Mus\u00e9um National d\u2019Histoire Naturelle, Centre National de la Recherche Scientifique, Universit\u00e9 de Paris , Paris 75016, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Marin","sequence":"additional","affiliation":[{"name":"Centre for Computing in Science Education, Department of Physics, University of Oslo , Oslo 0316, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Quackenbush","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA 02115, USA"},{"name":"Department of Data Science, Dana-Farber Cancer Institute , Boston, MA 02215, USA"},{"name":"Channing Division of Network Medicine, Harvard Medical School , Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kimberly","family":"Glass","sequence":"additional","affiliation":[{"name":"Channing Division of Network Medicine, Harvard Medical School , Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,6,17]]},"reference":[{"key":"2023062213573657200_btaa571-B1","doi-asserted-by":"crossref","first-page":"e05005","DOI":"10.7554\/eLife.05005","article-title":"Predicting effective microRNA target sites in mammalian mRNAs","volume":"4","author":"Agarwal","year":"2015","journal-title":"eLife"},{"key":"2023062213573657200_btaa571-B2","doi-asserted-by":"crossref","first-page":"3353","DOI":"10.1093\/nar\/gkz097","article-title":"An estimate of the total number of true human miRNAs","volume":"47","author":"Alles","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023062213573657200_btaa571-B3","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.cell.2009.01.002","article-title":"MicroRNAs: target recognition and regulatory functions","volume":"136","author":"Bartel","year":"2009","journal-title":"Cell"},{"key":"2023062213573657200_btaa571-B4","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.cell.2018.03.006","article-title":"Metazoan microRNAs","volume":"173","author":"Bartel","year":"2018","journal-title":"Cell"},{"key":"2023062213573657200_btaa571-B5","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s13059-017-1162-x","article-title":"RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells","volume":"18","author":"Bosia","year":"2017","journal-title":"Genome Biol"},{"key":"2023062213573657200_btaa571-B6","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/1471-2164-13-44","article-title":"miRdSNP: a database of disease-associated SNPs and microRNA target sites on 3\u2019UTRs of human genes","volume":"13","author":"Bruno","year":"2012","journal-title":"BMC Genomics"},{"key":"2023062213573657200_btaa571-B7","doi-asserted-by":"crossref","first-page":"066111","DOI":"10.1103\/PhysRevE.70.066111","article-title":"Finding community structure in very large networks","volume":"70","author":"Clauset","year":"2004","journal-title":"Phys. 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