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Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

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Published on: September 13, 2018

Gene name errors: Lessons not learned.

Mandhri Abeysooriya¹, Megan Soria¹, Mary Sravya Kasu¹

¹Deakin University, School of Life and Environmental Sciences, Geelong, Australia.

Plos Computational Biology

|July 30, 2021

Summary

Gene name errors in supplementary files persist despite prior warnings, with spreadsheets frequently misinterpreting gene symbols as dates or numbers. This highlights the ongoing challenge for computational biology research.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene name conversion errors in supplementary files have been a long-standing issue for computational biologists.
A 2016 report highlighted the prevalence of these errors, prompting an investigation into potential improvements.

Purpose of the Study:

To assess whether gene name errors in supplementary files decreased after the 2016 report.
To quantify the current extent of gene name conversion errors in scientific literature.

Main Methods:

A scan of supplementary files from PubMed Central published between 2014 and 2020 was conducted.
Improved scanning software was developed and utilized to identify gene name errors in supplementary Excel gene lists.

Main Results:

Gene name errors continued to accumulate unabated in the post-2016 period.
The developed scanning software identified gene name errors in 30.9% of articles with supplementary Excel gene lists, a higher rate than previously estimated.
Errors included conversion to dates, floating-point numbers, and internal date formats (five-digit numbers).

Conclusions:

Spreadsheets are unsuitable for handling large genomic datasets due to persistent gene name conversion errors.
The findings underscore the need for robust data handling practices in computational biology to prevent data integrity issues.