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GOThresher: a program to remove annotation biases from protein function annotation datasets

Parnal Joshi, Sagnik Banerjee, Xiao Hu, Pranav M. Khade, View ORCID ProfileIddo Friedberg
doi: https://doi.org/10.1101/2022.11.30.506803
Parnal Joshi
1Bioinformatics and Computational Biology Program
2Department of Veterinary Microbiology and Preventive Medicine
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Sagnik Banerjee
1Bioinformatics and Computational Biology Program
4Department of Statistics, Iowa State University, Ames IA USA
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Xiao Hu
2Department of Veterinary Microbiology and Preventive Medicine
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Pranav M. Khade
1Bioinformatics and Computational Biology Program
3Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology
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Iddo Friedberg
1Bioinformatics and Computational Biology Program
2Department of Veterinary Microbiology and Preventive Medicine
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  • ORCID record for Iddo Friedberg
  • For correspondence: idoerg@iastate.edu
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Abstract

Motivation Advances in sequencing technologies have led to a surge in genomic data, although the functions of many gene products coded by these genes remain unknown. While in-depth, targeted experiments that determine the functions of these gene products are crucial and routinely performed, they fail to keep up with the inflow of novel genomic data. In an attempt to address this gap, high-throughput experiments are being conducted in which a large number of genes are investigated in a single study. The annotations generated as a result of these experiments are generally biased towards a small subset of less informative Gene Ontology (GO) terms. Identifying and removing biases from protein function annotation databases is important since biases impact our understanding of protein function by providing a poor picture of the annotation landscape. Additionally, as machine learning methods for predicting protein function are becoming increasingly prevalent, it is essential that they are trained on unbiased datasets. Therefore, it is not only crucial to be aware of biases, but also to judiciously remove them from annotation datasets.

Results We introduce GOThresher, a Python tool that identifies and removes biases in function annotations from protein function annotation databases.

Implementation and Availability GOThresher is written in Python and released via PyPI https://pypi.org/project/gothresher/ and on the Bioconda Anaconda channel https://anaconda.org/bioconda/gothresher. The source code is hosted on GitHub https://github.com/FriedbergLab/GOThresher and distributed under the GPL 3.0 license.

Contact {idoerg{at}iastate.edu | parnal{at}iastate.edu}

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/FriedbergLab/GOThresher

  • https://anaconda.org/bioconda/gothresher

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 02, 2022.
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GOThresher: a program to remove annotation biases from protein function annotation datasets
Parnal Joshi, Sagnik Banerjee, Xiao Hu, Pranav M. Khade, Iddo Friedberg
bioRxiv 2022.11.30.506803; doi: https://doi.org/10.1101/2022.11.30.506803
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GOThresher: a program to remove annotation biases from protein function annotation datasets
Parnal Joshi, Sagnik Banerjee, Xiao Hu, Pranav M. Khade, Iddo Friedberg
bioRxiv 2022.11.30.506803; doi: https://doi.org/10.1101/2022.11.30.506803

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