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Updated: Jun 28, 2025

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

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Published on: January 2, 2018

CaTCH: Calculating transcript complexity of human genes.

Koushiki Basu¹, Anubha Dey¹, Manjari Kiran¹

¹Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana 500 046, India.

|April 19, 2024

Summary

This summary is machine-generated.

Alternative splicing in human genes generates proteome diversity. Transcript complexity (TC), measured by transcripts per exon, is influenced by features like exon length, coding potential, and splice site characteristics.

Keywords:

Alternative splicing CaTCH Linear Regression Random Forest Transcript complexit

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Alternative splicing significantly expands the human proteome, with ~95% of multi-exon genes undergoing this process.
Transcript complexity (TC) quantifies splicing efficiency, reflecting the number of transcripts per exon.
The discrepancy between theoretical and actual transcript numbers necessitates exploring regulatory features.

Purpose of the Study:

To identify key features influencing Transcript Complexity (TC) in human genes.
To develop a predictive model for TC using identified determinant features.
To explore features that restrict the full potential of alternative splicing.

Main Methods:

Whole transcriptome sequencing data and GENCODE annotations were utilized.
Features contributing to TC were extracted from various databases.
Linear regression and random forest models were employed to identify determinant features and predict TC.

Main Results:

Exon length was identified as the primary determinant of TC, positively affecting it.
Coding potential, chromatin signatures, and 5' splice site dinucleotides negatively impact TC.
A linear regression model (CaTCH) was developed to calculate human gene TC based on these features.

Conclusions:

Transcript complexity is a key metric for inferring gene splicing efficiency.
Exon length, coding potential, chromatin signatures, and splice site features are crucial regulators of TC.
The CaTCH model provides a method to quantify TC, aiding in understanding alternative splicing regulation.