Dataset Information

Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.

ABSTRACT: Alternative splicing (AS) is generally regulated by trans-splicing factors that specifically bind to cis-elements in pre-mRNAs. The human genome encodes ∼1,500 RNA binding proteins (RBPs) that potentially regulate AS, yet their functions remain largely unknown. To explore their potential activities, we fused the putative functional domains of RBPs to a sequence-specific RNA-binding domain and systemically analyzed how these engineered factors affect splicing. We discovered that ∼80% of low-complexity domains in endogenous RBPs displayed distinct context-dependent activities in regulating splicing, indicating that AS is under more extensive regulation than previously expected. We developed a machine learning approach to classify and predict the activities of RBPs based on their sequence compositions and further validated this model using endogenous RBPs and synthetic polypeptides. These results represent a systematic inspection, modeling, prediction, and validation of how RBP sequences affect their activities in controlling splicing, paving the way for de novo engineering of artificial splicing factors.

SUBMITTER: Mao M

PROVIDER: S-EPMC9390836 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.

Mao Miaowei M Hu Yue Y Yang Yun Y Qian Yajie Y Wei Huanhuan H Fan Wei W Yang Yi Y Li Xiaoling X Wang Zefeng Z

Cell systems 20181107 5

Alternative splicing (AS) is generally regulated by trans-splicing factors that specifically bind to cis-elements in pre-mRNAs. The human genome encodes ∼1,500 RNA binding proteins (RBPs) that potentially regulate AS, yet their functions remain largely unknown. To explore their potential activities, we fused the putative functional domains of RBPs to a sequence-specific RNA-binding domain and systemically analyzed how these engineered factors affect splicing. We discovered that ∼80% of low-compl ...[more]

PMID: 30414922

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Dataset Information

Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.

Publications

Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.

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