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Related Experiment Video

Updated: May 30, 2025

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

A Narrow Range of Transcript-error Rates Across the Tree of Life.

Weiyi Li¹, Stephan Baehr², Michelle Marasco³

¹Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA, 94305.

Biorxiv : the Preprint Server for Biology

|January 27, 2025

Summary

Cellular transcript error rates are surprisingly consistent across diverse species, suggesting natural selection may limit variations. Mechanisms likely exist to remove harmful errors, though a predictive model explains only a fraction of observed differences.

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Last Updated: May 30, 2025

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Molecular Biology
Evolutionary Biology
Genetics

Background:

Genomic information expression is prone to errors, with transcript errors far exceeding DNA mutation rates.
The cellular burden of these errors and their evolutionary constraints across species are not fully understood.

Purpose of the Study:

To investigate genome-wide transcript error rates across the Tree of Life.
To determine the extent to which natural selection constrains transcript error rates and identify lineage-specific divergence.
To develop a model explaining the evolutionary forces shaping transcript error rates.

Main Methods:

Utilized a modified rolling-circle sequencing method for genome-wide analysis.
Analyzed transcript error rates across diverse species.
Developed a cell biology and population genetics model incorporating cell volume, proteome size, error exposure, and effective population size.

Main Results:

Transcript error rates show a remarkably narrow range across diverse species.
Errors are generally randomly distributed, with no strong evidence for local control linked to gene expression levels.
Missense and some nonsense transcript errors are underrepresented compared to random expectations, indicating error-purging mechanisms.

Conclusions:

Transcript error rates are a highly conserved trait across the Tree of Life.
Natural selection likely plays a role in constraining these error rates and purging deleterious errors.
Current models explain limited variation, highlighting the need for further theoretical development in understanding the evolution of transcript fidelity.