Study
Prediction and quantification of splice events from RNA-seq data
Study ID | Alternative Stable ID | Type |
---|---|---|
EGAS00001001026 | Other |
Study Description
Analysis of splice variants from short read RNA-seq data remains a challenging problem. Here we present a novel method for the genome-guided prediction and quantification of splice events from RNA-seq data, which enables the analysis of unannotated and complex splice events. Splice junctions and exons are predicted from reads mapped to a reference genome and are assembled into a genome-wide splice graph. Splice events are identified recursively from the graph and are quantified locally based on reads extending across the start or end of each splice variant. We assess prediction accuracy based on simulated and real RNA-seq data, and illustrate how different read aligners (GSNAP, HISAT2, STAR, TopHat2) affect prediction results. We validate our approach for quantification based on simulated data, and compare local estimates of relative splice variant usage with those from other methods (MISO, Cufflinks) based on simulated and real RNA-seq data. In a proof-of-concept study of splice variants in 16 normal human tissues (Illumina Body Map 2.0) we identify 249 internal exons that belong ... (Show More)
Study Datasets 2 datasets.
Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data
Dataset ID | Description | Technology | Samples |
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EGAD00001001057 |
RNA-seq from normal human tissues (2 x 75 bp)
|
Illumina HiSeq 2000 | 3 |
EGAD00001001922 |
RNA-seq from normal human tissues (2 x 250 bp)
|
Illumina HiSeq 2000 | 14 |
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