A critical assessment of Mus musculusgene function prediction using integrated genomic evidence
Open accessAbstract
Background: Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated. Results: In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%. Conclusion: We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.
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Even gold-standard expression datasets perform relatively poorly under GBA in benchmarking exercises.
“Even 'gold-standard' expression datasets perform relatively poorly under GBA when assessed in benchmarking exercises (e.g. the Mousefunc competition for genome-wide gene function prediction- Pena-Castillo et al., 2008).”
From Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015) · cites this paper as Pena-Castillo et al., 2008
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- Authors
- Peña-Castillo, L., Tasan, M., Myers, C. L., Lee, H., Joshi, T., Zhang, C., Guan, Y., Leone, M., Pagnani, A., Kim, W. K., Krumpelman, C., Tian, W., Obozinski, G., Qi, Y., Mostafavi, S., Lin, G. N., Berriz, G. F., Gibbons, F. D., Lanckriet, G., Qiu, J., Grant, C., Barutcuoglu, Z., Hill, D. P., Warde-Farley, D., Grouios, C., Ray, D., Blake, J. A., Deng, M., Jordan, M. I., Noble, W. S., Morris, Q., Klein-Seetharaman, J., Bar-Joseph, Z., Chen, T., Sun, F., Troyanskaya, O. G., Marcotte, E. M., Xu, D., Hughes, T. R., & Roth, F. P.
- Affiliations
- Harvard University (US)
- Princeton University (US)
- Gwangju Institute of Science and Technology (KR)
- University of Missouri (US)
- Institute for Scientific Interchange (IT)
- The University of Texas at Austin (US)
- University of California, Berkeley (US)
- Carnegie Mellon University (US)
- University of Toronto (CA)
- University of California San Diego (US)
- University of Washington (US)
- Jackson Laboratory (US)
- Gatsby Computational Neuroscience Unit (GB)
- Peking University (CN)
- University of Southern California (US)
- Corresponding author
- Hughes, Timothy R
- Roth, Frederick P
- ORCID
- Myers, Chad L
- Lee, Hyunju
- Joshi, Trupti
- Zhang, Chao
- Guan, Yuanfang
- Leone, Michele
- Pagnani, Andrea
- Tian, Weidong
- Obozinski, Guillaume
- Qi, Yanjun
- Mostafavi, Sara
- Lin, Guan Ning
- Gibbons, Francis D
- Qiu, Jian
- Hill, David P
- Blake, Judith A
- Deng, Minghua
- Jordan, Michael I
- Noble, William S
- Morris, Quaid
- Chen, Ting
- Sun, Fengzhu
- Troyanskaya, Olga G
- Marcotte, Edward M
- Xu, Dong
- Hughes, Timothy R
- Roth, Frederick P
- Venue
- Genome Biology
- Published
- 2008-06-27
- Volume
- 9
- Issue
- S1
- Pages
- S2-S2
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC2447536
- OpenAlex
- W2147461734
- Cited by
- 248
- References
- 47
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 1 · 2022: 3 · 2021: 9 · 2020: 10 · 2019: 8 · 2018: 12 · 2017: 8 · 2016: 8 · 2015: 21 · 2014: 19 · 2013: 28 · 2012: 24
- FWCI
- 12.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- TSpace (University of Toronto) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- PORTO@Iris (submittedVersion)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion)
- PORTO Publications Open Repository TOrino (Politecnico di Torino) (submittedVersion)
- Funders
- National Science Foundation (0225656, 0513552, CC044917, 0407204, 0225636, 0241102, P50 GM071508, CAREER, CAREER CC044917)
- U.S. Department of Agriculture
- W. M. Keck Foundation
- Microsoft Research
- Genome Canada
- Ontario Genomics
- Ontario Genomics Institute
- National Natural Science Foundation of China (30570425, CAREER)
- Gwangju Institute of Science and Technology
- National Institutes of Health (CAREER CC044917, 2p41hg002273-07, EIA0225636, NIH P50, HG002273, GM071508)
- Cooperative State Research, Education, and Extension Service
- Microsoft Research Asia
- Canadian Institutes of Health Research (HG004233)
- Natural Sciences and Engineering Research Council of Canada
- National Heart, Lung, and Blood Institute (HL81341, U01HL081341)
- National Human Genome Research Institute (U41HG002273, HG004233, HG004098, R33HG003070, HG 002273, HG0017115, F32HG004098, R01HG003224, HG003224, P50HG002790, P50HG004233, R01HG002273, P41-HG002273)
- National Institute of General Medical Sciences (GM071508, R01GM071966, GM071966, P50GM071508)
- U.S. National Library of Medicine (LM07994-01, R01LM007994)
- Topics
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- gene function prediction, Mus musculus, mouse genes, Gene Ontology, functional genomic data, classifier comparison, uncharacterized genes
- MeSH terms
- Algorithms, Animals, Proteins, Mice
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