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Nat Methods. 2019 Apr;16(4):307-310. doi: 10.1038/s41592-019-0351-9. Epub 2019 Mar 25.

Deep-learning augmented RNA-seq analysis of transcript splicing.

Author information

1
Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA, USA.
2
Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
3
Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
4
Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA, USA.
5
Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
6
Department of Statistics, University of California, Los Angeles, Los Angeles, CA, USA.
7
Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA, USA. XINGYI@email.chop.edu.
8
Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA. XINGYI@email.chop.edu.
9
Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA. XINGYI@email.chop.edu.
10
Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA. XINGYI@email.chop.edu.

Abstract

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.

PMID:
30923373
DOI:
10.1038/s41592-019-0351-9
[Indexed for MEDLINE]

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