Gene annotation bias impedes biomedical research
Open accessPublished version of a preprint
Abstract
We found tremendous inequality across gene and protein annotation resources. We observed that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate that researchers reduce these biases by pursuing data-driven hypotheses.
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Cites
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288 · see citing works
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505 · see citing works
The ASA Statement on <i>p</i> -Values: Context, Process, and Purpose
Wasserstein, R. L., & Lazar, N. A. (2016) · The American Statistician · doi:10.1080/00031305.2016.1154108 · Cited by 6,306 · see citing works
Raise standards for preclinical cancer research
Begley, C. G., & Ellis, L. M. (2012) · Nature · doi:10.1038/483531a · Cited by 2,979 · see citing works
Believe it or not: how much can we rely on published data on potential drug targets?
Prinz, F., Schlange, T., & Asadullah, K. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3439-c1 · Cited by 1,888 · see citing works
“Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks
Gillis, J., & Pavlidis, P. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002444 · Cited by 214 · see citing works
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Large-scale investigation of the reasons why potentially important genes are ignored
Stoeger, T., Gerlach, M., Morimoto, R. I., & Nunes Amaral, L. A. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2006643 · Cited by 261 · see citing works
Identifying biases and their potential solutions in human microbiome studies
Nearing, J. T., Comeau, A. M., & Langille, M. G. I. (2021) · Microbiome · doi:10.1186/s40168-021-01059-0 · Cited by 240 · see citing works
Byrne, J. A., & Christopher, J. (2020) · FEBS Letters · doi:10.1002/1873-3468.13747 · Cited by 133 · see citing works
Functional unknomics: Systematic screening of conserved genes of unknown function
Rocha, J. J., Jayaram, S. A., Stevens, T. J., Muschalik, N., Shah, R. D., Emran, S., Robles, C., Freeman, M., & Munro, S. (2023) · PLOS Biology · doi:10.1371/journal.pbio.3002222 · Cited by 70 · see citing works
Albaradei, S., Napolitano, F., Thafar, M. A., Gojobori, T., Essack, M., & Gao, X. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.08.006 · Cited by 61 · see citing works
Byrne, J. A., Park, Y., Richardson, R. A. K., Pathmendra, P., Sun, M., & Stoeger, T. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1139 · Cited by 46 · see citing works
COVID-19 research risks ignoring important host genes due to pre-established research patterns
Stoeger, T., & Nunes Amaral, L. A. (2020) · eLife · doi:10.7554/elife.61981 · Cited by 30 · see citing works
Byrne, J. A., Grima, N., Capes-Davis, A., & Labbé, C. (2019) · Biomarker Insights · doi:10.1177/1177271919829162 · Cited by 34 · see citing works
Broad functional profiling of fission yeast proteins using phenomics and machine learning
Rodríguez-López, M., Bordin, N., Lees, J., Scholes, H., Hassan, S., Saintain, Q., Kamrad, S., Orengo, C., & Bähler, J. (2023) · eLife · doi:10.7554/elife.88229.3 · Cited by 18 · see citing works
Phan, A., Joshi, P., Kadelka, C., & Friedberg, I. (2025) · Database · doi:10.1093/database/baaf036 · Cited by 2 · see citing works
- Authors
- Haynes, W. A., Tomczak, A., & Khatri, P.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Khatri, Purvesh
- Venue
- Scientific Reports
- Published
- 2018-01-22
- Volume
- 8
- Issue
- 1
- Pages
- 1362-1362
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5778030
- OpenAlex
- W2908494225
- Cited by
- 192
- References
- 6
- Citations by year
- 2026: 19 · 2025: 26 · 2024: 19 · 2023: 29 · 2022: 26 · 2021: 22 · 2020: 17 · 2019: 15 · 2018: 16 · 2017: 3
- FWCI
- 6.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- AMIA
- Funders
- National Science Foundation ((DGE-114747, 114747)
- Bill and Melinda Gates Foundation (U19AI057229)
- National Institutes of Health (5u19ai109662-05, DGE-114747, 1U19‐AI‐109662)
- National Heart, Lung, and Blood Institute (R01HL122887)
- National Institute of Allergy and Infectious Diseases (1 U19AI109662, U19-AI109662, U54I117925, U19AI057229)
- U.S. National Library of Medicine (T15LM007033)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- protein annotation, research bias, data-driven hypothesis generation
- MeSH terms
- Humans, Bias, Computational Biology, Biomedical Research, Molecular Sequence Annotation
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