Dataset Information

An expectation-maximization framework for comprehensive prediction of isoform-specific functions.

ABSTRACT:

Motivation

Advances in RNA sequencing technologies have achieved an unprecedented accuracy in the quantification of mRNA isoforms, but our knowledge of isoform-specific functions has lagged behind. There is a need to understand the functional consequences of differential splicing, which could be supported by the generation of accurate and comprehensive isoform-specific gene ontology annotations.

Results

We present isoform interpretation, a method that uses expectation-maximization to infer isoform-specific functions based on the relationship between sequence and functional isoform similarity. We predicted isoform-specific functional annotations for 85 617 isoforms of 17 900 protein-coding human genes spanning a range of 17 430 distinct gene ontology terms. Comparison with a gold-standard corpus of manually annotated human isoform functions showed that isoform interpretation significantly outperforms state-of-the-art competing methods. We provide experimental evidence that functionally related isoforms predicted by isoform interpretation show a higher degree of domain sharing and expression correlation than functionally related genes. We also show that isoform sequence similarity correlates better with inferred isoform function than with gene-level function.

Availability and implementation

Source code, documentation, and resource files are freely available under a GNU3 license at https://github.com/TheJacksonLaboratory/isopretEM and https://zenodo.org/record/7594321.

SUBMITTER: Karlebach G

PROVIDER: S-EPMC10079350 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

An expectation-maximization framework for comprehensive prediction of isoform-specific functions.

Karlebach Guy G Carmody Leigh L Sundaramurthi Jagadish Chandrabose JC Casiraghi Elena E Hansen Peter P Reese Justin J Mungall Christopher J CJ Valentini Giorgio G Robinson Peter N PN

Bioinformatics (Oxford, England) 20230401 4

<h4>Motivation</h4>Advances in RNA sequencing technologies have achieved an unprecedented accuracy in the quantification of mRNA isoforms, but our knowledge of isoform-specific functions has lagged behind. There is a need to understand the functional consequences of differential splicing, which could be supported by the generation of accurate and comprehensive isoform-specific gene ontology annotations.<h4>Results</h4>We present isoform interpretation, a method that uses expectation-maximization ...[more]

PMID: 36929917

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Dataset Information

An expectation-maximization framework for comprehensive prediction of isoform-specific functions.

Motivation

Results

Availability and implementation

Publications

An expectation-maximization framework for comprehensive prediction of isoform-specific functions.

Similar Datasets

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