Bioinformatics
Exact Transcriptome Reconstruction from Short Sequence Reads
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Modeling the Marginal Distribution of Gene Expression with Mixture Models
FGCN '08 Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking - Volume 03
Statistical inferences for isoform expression in RNA-Seq
Bioinformatics
Bioinformatics
Bioinformatics
Bubbles: alternative splicing events of arbitrary dimension in splicing graphs
RECOMB'08 Proceedings of the 12th 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
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
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
An integer programming approach to novel transcript reconstruction from paired-end RNA-Seq reads
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Due to alternative splicing events in eukaryotic species, the identification of mRNA isoforms (or splicing variants) is a difficult problem Traditional experimental methods for this purpose are time consuming and cost ineffective The emerging RNA-Seq technology provides a possible effective method to address this problem Although the advantages of RNA-Seq over traditional methods in transcriptome analysis have been confirmed by many studies, the inference of isoforms from millions of short sequence reads (e.g., Illumina/Solexa reads) has remained computationally challenging In this work, we propose a method to calculate the expression levels of isoforms and infer isoforms from short RNA-Seq reads using exon-intron boundary, transcription start site (TSS) and poly-A site (PAS) information We first formulate the relationship among exons, isoforms, and single-end reads as a convex quadratic program, and then use an efficient algorithm (called IsoInfer) to search for isoforms IsoInfer can calculate the expression levels of isoforms accurately if all the isoforms are known and infer novel isoforms from scratch Our experimental tests on known mouse isoforms with both simulated expression levels and reads demonstrate that IsoInfer is able to calculate the expression levels of isoforms with an accuracy comparable to the state-of-the-art statistical method and a 60 times faster speed Moreover, our tests on both simulated and real reads show that it achieves a good precision and sensitivity in inferring isoforms when given accurate exon-intron boundary, TSS and PAS information, especially for isoforms whose expression levels are significantly high.