{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T12:16:36Z","timestamp":1752668196959},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Many human genes express multiple transcript isoforms through alternative splicing, which greatly increases diversity of protein function. Although RNA sequencing (RNA-Seq) technologies have been widely used in measuring amounts of transcribed mRNA, accurate estimation of transcript isoform abundances from RNA-Seq data is challenging because reads often map to more than one transcript isoforms or paralogs whose sequences are similar to each other.<\/jats:p>\n               <jats:p>Results: We propose a statistical method to estimate transcript isoform abundances from RNA-Seq data. Our method can handle gapped alignments of reads against reference sequences so that it allows insertion or deletion errors within reads. The proposed method optimizes the number of transcript isoforms by variational Bayesian inference through an iterative procedure, and its convergence is guaranteed under a stopping criterion. On simulated datasets, our method outperformed the comparable quantification methods in inferring transcript isoform abundances, and at the same time its rate of convergence was faster than that of the expectation maximization algorithm. We also applied our method to RNA-Seq data of human cell line samples, and showed that our prediction result was more consistent among technical replicates than those of other methods.<\/jats:p>\n               <jats:p>Availability: An implementation of our method is available at http:\/\/github.com\/nariai\/tigar<\/jats:p>\n               <jats:p>Contact: \u00a0nariai@megabank.tohoku.ac.jp<\/jats:p>\n               <jats:p>Supplementary Information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt381","type":"journal-article","created":{"date-parts":[[2013,7,3]],"date-time":"2013-07-03T04:27:49Z","timestamp":1372825669000},"page":"2292-2299","source":"Crossref","is-referenced-by-count":34,"title":["TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference"],"prefix":"10.1093","volume":"29","author":[{"given":"Naoki","family":"Nariai","sequence":"first","affiliation":[{"name":"1 Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan and 2Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa, Ishikawa, 920-1192, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Osamu","family":"Hirose","sequence":"additional","affiliation":[{"name":"1 Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan and 2Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa, Ishikawa, 920-1192, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaname","family":"Kojima","sequence":"additional","affiliation":[{"name":"1 Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan and 2Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa, Ishikawa, 920-1192, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masao","family":"Nagasaki","sequence":"additional","affiliation":[{"name":"1 Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan and 2Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa, Ishikawa, 920-1192, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2013,7,2]]},"reference":[{"key":"2023070311325093200_btt381-B1","first-page":"21","article-title":"Inferring parameters and structure of latent variable models by variational bayes","volume-title":"Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence","author":"Attias","year":"1999"},{"key":"2023070311325093200_btt381-B2","first-page":"7","article-title":"The variational Bayesian EM algorithm for incomplete data: with application to scoring graphical model structures","author":"Beal","year":"2003","journal-title":"Bayesian Stat."},{"key":"2023070311325093200_btt381-B3","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1214\/06-BA126","article-title":"Variational Bayesian learning of directed graphical models with hidden variables","volume":"1","author":"Beal","year":"2006","journal-title":"Bayesian Anal."},{"key":"2023070311325093200_btt381-B4","volume-title":"Pattern Recognition and Machine Learning","author":"Biship","year":"2006"},{"key":"2023070311325093200_btt381-B5","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1093\/nar\/gkp1137","article-title":"The Sanger FASTQ file format for sequences with quality scores, and the Solexa\/Illumina FASTQ variants","volume":"38","author":"Cock","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"2023070311325093200_btt381-B6","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/BF00994110","article-title":"A Bayesian method for the induction of probabilistic networks from data","volume":"9","author":"Cooper","year":"1992","journal-title":"Mach. 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