Data mining of mRNA-Seq and small RNA-Seq data to find microRNA targets
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Estimation of alternative splicing isoform frequencies from RNA-Seq data
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Isolasso: a lasso regression approach to RNA-seq based transcriptome assembly
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
T-IDBA: a de novo iterative de bruijn graph assembler for transcriptome
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Inference of isoforms from short sequence reads
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
A robust method for transcript quantification with RNA-seq data
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Detecting various types of differential splicing events using RNA-Seq data
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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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.