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High-resolution functional annotation of human transcriptome: predicting isoform functions by a novel multiple

Wenyuan Li¹, Shuli Kang, Chun-Chi Liu

¹Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA, Institute of Genomics and Bioinformatics, National Chung Hsing University, Taiwan 40227, Republic of China, National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China and Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA.

Nucleic Acids Research

|December 27, 2013

Summary

This study systematically predicts functions for individual human transcript isoforms, revealing isoform-specific roles and their link to tissue expression diversity. It provides high-resolution functional annotation for the human transcriptome.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Alternative transcript processing generates functional diversity, but individual isoform functions remain largely unknown.
Proteins, not genes, are the primary carriers of biological function.
Existing functional annotations are primarily at the gene level, hindering isoform-specific studies.

Purpose of the Study:

To systematically predict functions for individual human transcript isoforms.
To enable high-resolution functional annotation of the human transcriptome.
To address the challenge of limited training data for isoform function prediction.

Main Methods:

Integration of multiple human RNA-sequencing datasets.
Modeling gene-isoform relationships as multiple instance data.
Development of a novel label propagation method for function prediction.

Main Results:

Achieved an average area under the receiver operating characteristic curve of 0.67.
Assigned functions to 15,572 human isoforms.
Observed varying sensitivities of functions to alternative splicing and a positive correlation between isoform function diversity and tissue expression diversity.

Conclusions:

The study provides the first systematic prediction of human isoform functions.
Isoform function prediction is feasible and offers high-resolution transcriptome annotation.
Functional diversity of isoforms is linked to their expression patterns, with implications for understanding gene regulation, as exemplified by TP53 isoforms.