Statistical inferences for isoform expression in RNA-Seq Export

@article{citeulike:4115742, abstract = {Summary: The development of RNA sequencing (RNA-Seq) makes it possible for us to measure transcription at an unprecedented precision and throughput. However, challenges remain in understanding the source and distribution of the reads, modeling the transcript abundance and developing efficient computational methods. In this article, we develop a method to deal with the isoform expression estimation problem. The count of reads falling into a locus on the genome annotated with multiple isoforms is modeled as a Poisson variable. The expression of each individual isoform is estimated by solving a convex optimization problem and statistical inferences about the parameters are obtained from the posterior distribution by importance sampling. Our results show that isoform expression inference in RNA-Seq is possible by employing appropriate statistical methods. Contact: whwong@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btp113}, author = {Jiang, Hui and Wong, Wing H.}, citeulike-article-id = {4115742}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btp113}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/content/25/8/1026.abstract}, citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/25/8/1026.full.pdf}, citeulike-linkout-3 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/25/8/1026}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/19244387}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=19244387}, day = {15}, doi = {10.1093/bioinformatics/btp113}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {alt-splicing, bioinformatics, computational-model, isoform, mrna, rna-seq, splicing}, month = {April}, number = {8}, pages = {1026--1032}, posted-at = {2009-03-26 09:27:38}, priority = {2}, title = {Statistical inferences for isoform expression in RNA-Seq}, url = {http://dx.doi.org/10.1093/bioinformatics/btp113}, volume = {25}, year = {2009} }

TY - JOUR ID - citeulike:4115742 L3 - citeulike-article-id:4115742 N2 - Summary: The development of RNA sequencing (RNA-Seq) makes it possible for us to measure transcription at an unprecedented precision and throughput. However, challenges remain in understanding the source and distribution of the reads, modeling the transcript abundance and developing efficient computational methods. In this article, we develop a method to deal with the isoform expression estimation problem. The count of reads falling into a locus on the genome annotated with multiple isoforms is modeled as a Poisson variable. The expression of each individual isoform is estimated by solving a convex optimization problem and statistical inferences about the parameters are obtained from the posterior distribution by importance sampling. Our results show that isoform expression inference in RNA-Seq is possible by employing appropriate statistical methods. Contact: whwong@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btp113 IS - 8 JF - Bioinformatics SN - 1460-2059 EP - 1032 TI - Statistical inferences for isoform expression in RNA-Seq VL - 25 SP - 1026 KW - alt-splicing KW - bioinformatics KW - computational-model KW - isoform KW - mrna KW - rna-seq KW - splicing AU - Jiang, Hui AU - Wong, Wing PY - 2009/04/15/ UR - http://dx.doi.org/10.1093/bioinformatics/btp113 ER -