{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:01Z","timestamp":1740185101747,"version":"3.37.3"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019217","name":"Institut de Valorisation des Donn\u00e9es","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100019217","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,15]]},"abstract":"Abstract<\/jats:title>\n \n Motivation<\/jats:title>\n Messenger RNA subcellular localization mechanisms play a crucial role in post-transcriptional gene regulation. This trafficking is mediated by trans-acting RNA-binding proteins interacting with cis-regulatory elements called zipcodes. While new sequencing-based technologies allow the high-throughput identification of RNAs localized to specific subcellular compartments, the precise mechanisms at play, and their dependency on specific sequence elements, remain poorly understood.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n We introduce RNATracker, a novel deep neural network built to predict, from their sequence alone, the distributions of mRNA transcripts over a predefined set of subcellular compartments. RNATracker integrates several state-of-the-art deep learning techniques (e.g. CNN, LSTM and attention layers) and can make use of both sequence and secondary structure information. We report on a variety of evaluations showing RNATracker\u2019s strong predictive power, which is significantly superior to a variety of baseline predictors. Despite its complexity, several aspects of the model can be isolated to yield valuable, testable mechanistic hypotheses, and to locate candidate zipcode sequences within transcripts.<\/jats:p>\n <\/jats:sec>\n \n Availability and implementation<\/jats:title>\n Code and data can be accessed at https:\/\/www.github.com\/HarveyYan\/RNATracker.<\/jats:p>\n <\/jats:sec>\n \n Supplementary information<\/jats:title>\n Supplementary data are available at Bioinformatics online.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz337","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T19:21:53Z","timestamp":1557429713000},"page":"i333-i342","source":"Crossref","is-referenced-by-count":63,"title":["Prediction of mRNA subcellular localization using deep recurrent neural networks"],"prefix":"10.1093","volume":"35","author":[{"given":"Zichao","family":"Yan","sequence":"first","affiliation":[{"name":"School of Computer Science, McGill University, Montreal, QC, Canada"}]},{"given":"Eric","family":"L\u00e9cuyer","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, University of Montreal, Montreal, QC, Canada"},{"name":"Institut de Recherches Clinique de Montr\u00e9al (IRCM), Montreal, QC, Canada"},{"name":"Division of Experimental Medicine, McGill University, Montreal, QC, Canada"}]},{"given":"Mathieu","family":"Blanchette","sequence":"additional","affiliation":[{"name":"School of Computer Science, McGill University, Montreal, QC, Canada"}]}],"member":"286","published-online":{"date-parts":[[2019,7,5]]},"reference":[{"key":"2023062712370650500_btz337-B1","doi-asserted-by":"crossref","first-page":"D635","DOI":"10.1093\/nar\/gkw1104","article-title":"Ensembl 2017","volume":"45","author":"Aken","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023062712370650500_btz337-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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