Deep-learning augmented RNA-seq analysis of transcript splicing

doi:10.1038/s41592-019-0351-9

Deep-learning augmented RNA-seq analysis of transcript splicing

2019-03-25

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DOI：10.1038/s41592-019-0351-9

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A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS (https://github.com/Xinglab/DARTS), a computational framework that integrates deep-learning-based predictions with empirical RNA-seq evidence to infer differential alternative splicing between biological samples. DARTS leverages public RNA-seq big data to provide a knowledge base of splicing regulation via deep learning, thereby helping researchers better characterize alternative splicing using RNA-seq datasets even with modest coverage.

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Deep-learning augmented RNA-seq analysis of transcript splicing

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