期刊
NATURE METHODS
卷 16, 期 4, 页码 307-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41592-019-0351-9
关键词
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资金
- National Institutes of Health [R01GM088342, R01GM117624, U01HG007912, U01CA233074]
- UCLA Dissertation Year Fellowship
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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