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PreprintCited by 192Biomedical Text Mining and OntologiesBioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

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Gene annotation bias impedes biomedical research

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Haynes, Winston A., Tomczak, Aurelie, Khatri, Purvesh·2017·Scientific Reports
DOI 10.1101/133108PMC PMC5778030

Preprint — published as 10.1038/s41598-018-19333-x · Paper page

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Abstract

1 We found tremendous inequality across gene and protein annotation resources. We observe that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate for researchers to reduce these biases by pursuing data-driven hypotheses.

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Research works printed but not linked: 33

  1. 2012, Ten years of pathway analysis: current approaches and outstanding challenges, PLoS computational biology, 8, e1002375, 10.1371/journal.pcbi.1002375 · DOI
  2. 10.1038/75556 · DOI · Paper page
  3. 10.1093/nar/gkt1102 · DOI · Paper page
  4. 10.1093/nar/gku935 · DOI
  5. 10.1093/nar/gkj067 · DOI · Paper page
  6. 10.1093/nar/28.1.235 · DOI · Paper page
  7. 10.1038/nrg2363 · DOI
  8. Battaglia, M. & Atkinson, M. A. The streetlight effect in type 1 diabetes. Diabetes 64, 1081–90 (2015). URL http://www.ncbi.nlm.nih.gov/pubmed/25805758 www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4375074. · DOI
  9. Bulgheresi, S. Bacterial cell biology outside the streetlight. Environmental Microbiology 18, 2305–2318 (2016). URL http://doi.wiley.com/10.1111/1462-2920.13406. · DOI
  10. Lam, M. P. Y. et al. Data-Driven Approach To Determine Popular Proteins for Targeted Proteomics Translation of Six Organ Systems. Journal of proteome research Web (2016). URL http://www.ncbi.nlm.nih.gov/pubmed/27356587.
  11. 10.1371/journal.pmed.0020124 · DOI · Paper page
  12. Ioannidis, J. P. A. Why Most Discovered True Associations Are Inflated. Epidemiology 19, 640–648 (2008). URL http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage\&an=00001648-200809000-00002. · DOI · Paper page
  13. Macleod, M. R. et al. Biomedical research: increasing value, reducing waste (2014). · DOI · Paper page
  14. 10.1038/505612a · DOI · Paper page
  15. 10.1084/jem.20122709 · DOI
  16. Haynes, W. A. et al. Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility. bioRxiv Web (2016). URL http://biorxiv.org/content/early/2016/08/25/071514. · DOI
  17. Mazur, P. K. et al. SMYD3 links lysine methylation of MAP3K2 to Ras-driven cancer. Nature advance on (2014). URL www.nature.com/articles/nature13320. · DOI
  18. 10.1038/nm.3952 · DOI
  19. Chen, R. et al. A meta-analysis of lung cancer gene expression identifies PTK7 as a survival gene in lung adenocarcinoma. Cancer Research 74, 2892–2902 (2014). URL http://www.ncbi.nlm.nih.gov/pubmed/24654231. · DOI
  20. 10.1126/scitranslmed.aaa5993 · DOI
  21. Andres-Terre, M. et al. Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses. Immunity 43, 1199–1211 (2015). URL http://www.cell.com/article/S1074761315004550/fulltext. · DOI
  22. 2016, Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis, The Lancet Respiratory Medicine, 4, 213, 10.1016/S2213-2600(16)00048-5 · DOI
  23. 10.1126/scitranslmed.aaf7165 · DOI · Paper page
  24. Lofgren, S. et al. Integrated, multicohort analysis of systemic sclerosis identifies robust transcriptional signature of disease severity. JCI Insight 1 (2016). URL https://insight.jci.org/articles/view/89073. · DOI
  25. 10.1038/nrd3439-c1 · DOI · Paper page
  26. 10.1038/483531a · DOI · Paper page
  27. 10.1093/nar/gkt1229 · DOI · Paper page
  28. 10.1093/bioinformatics/btp618 · DOI
  29. 10.1038/ejhg.2011.91 · DOI
  30. 2014, Integrated multi-cohort transcriptional meta-analysis of neurodegenerative diseases, Acta neuropathologica communications, 2, 93, 10.1186/s40478-014-0093-y · DOI
  31. 10.1093/nar/gkw1099 · DOI · Paper page
  32. 10.1093/nar/gkg091 · DOI
  33. 10.1093/nar/30.1.207 · DOI · Paper page

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Metadata

Authors
Haynes, W. A., Tomczak, A., & Khatri, P.
Affiliations
Stanford Medicine (US)
Corresponding author
Khatri, Purvesh
ORCID
Venue
Scientific Reports
Publisher
openRxiv
Published
2017-05-02
Volume
8
Issue
1
Pages
1362-1362
ISSN
2045-2322 (ISSN-L)
DOI
10.1101/133108
PMC
PMC5778030
OpenAlex
W2908494225
Cited by
192
References
0
Citations by year
2026: 19 · 2025: 26 · 2024: 19 · 2023: 29 · 2022: 27 · 2021: 22 · 2020: 17 · 2019: 15 · 2018: 15 · 2017: 3
FWCI
6.10
Type
journal article
Language
en
Open access
Licence
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
  • Health Sciences › Medicine › Health Informatics › 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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