This article is part of the supplement: Quantitative inference of gene function from diverse large-scale datasets
Research
A critical assessment of Mus musculus gene function prediction using integrated genomic evidence
1 Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S3E1, Canada
2 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
3 Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
4 Department of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, 500-712 Republic of Korea
5 Digital Biology Laboratory, Computer Science Department and Christopher S Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
6 ISI Foundation, Torino, 10133, Italy
7 Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA
8 Department of Electrical and Computer Engineering, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA
9 Department of Statistics, UC Berkeley, Berkeley, CA 94720-3860, USA
10 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
11 Department of Computer Science, University of Toronto, Toronto, ON M5S3G4, Canada
12 Department of Electrical and Computer Engineering, UC San Diego, La Jolla, CA 92093-0407, USA
13 Department of Genome Sciences, University of Washington, Seattle, WA 98195-5065, USA
14 Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
15 Bioinformatics and Computational Biology, The Jackson Laboratory, Bar Harbor, ME 04609, USA
16 Gatsby Computational Neuroscience Unit, London, WC1N 3AR, UK
17 School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing 100871, PRC
18 Department of Electrical Engineering and Computer Science, and Department of Statistics, UC Berkeley, Berkeley, CA 94720-1776, USA
19 Department of Genome Sciences, and Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
20 Banting and Best Department of Medical Research, University of Toronto, Toronto, ON M5S 3E1, Canada
21 Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
22 Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
23 Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
Genome Biology 2008, 9(Suppl 1):S2 doi:10.1186/gb-2008-9-s1-s2
Published: 27 June 2008Abstract
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.
