A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

Lourdes Peña-Castillo, Murat Tasan, Chad L. Myers, Hyunju Lee, Trupti Joshi, Chao Zhang, Yuanfang Guan, Michele Leone, Andrea Pagnani, Wan Kyu Kim, Chase Krumpelman, Weidong Tian, Guillaume Obozinski, Yanjun Qi, Sara Mostafavi, Guan Ning Lin, Gabriel F. Berriz, Francis D. Gibbons, Gert Lanckriet, Jian QiuCharles Grant, Zafer Barutcuoglu, David P. Hill, David Warde-Farley, Chris Grouios, Debajyoti Ray, Judith A. Blake, Minghua Deng, Michael I. Jordan, William S. Noble, Quaid Morris, Judith Klein-Seetharaman, Ziv Bar-Joseph, Ting Chen, Fengzhu Sun, Olga G. Troyanskaya, Edward M. Marcotte, Dong Xu, Timothy R. Hughes, Frederick P. Roth

Research output: Contribution to journalArticlepeer-review

201 Scopus citations


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.

Original languageEnglish
Article numberS2
JournalGenome Biology
Issue numberSUPPL. 1
StatePublished - 27 Jun 2008

Bibliographical note

Funding Information:
Team A (GO, GL, JQ, CG, MJ, and WSN) was supported by NIH award R33 HG003070. Team B (HL, MD, TC, and FS) was supported by NIH/NSF joint mathematical biology initiative DMS-0241102 and NIH P50 HG 002790; HL is supported by the systems biology infrastructure establishment grant provided by Gwangju Institute of Science and Technology in 2008; MD is supported by the National Natural Science Foundation of China (No. 30570425), the National Key Basic Research Project of China (No. 2003CB715903), and Microsoft Research Asia (MSRA). Team C (SM, DW-F, CG, DR, QM) was supported by an NSERC operating grant to QM as well as a Genome Canada grant administered by the Ontario Genomics Institute. Team D (YG, CLM, ZB, and OGT) was partially supported by NIH grant R01 GM071966 and NSF grant IIS-0513552 to OGT and NIGMS Center of Excellence grant P50 GM071508. Team E (WKK, CK, and EMM) was supported by grants from the NIH, NSF, Packard and Welch Foundations. Team F (TJ, CZ, GNL, and DX) was supported by USDA/CSREES-2004-25604-14708 and NSF/ITR-IIS-0407204. Team G (MT, WT, FDG, GFB, and FPR) was supported by NIH grants (HG003224, HG0017115, HL81341, HG004233 and HG004098), by the Keck Foundation, and by NSF TeraGrid resources. Team H (YQ, JK, and ZB) was supported in part by National Science Foundation NSF grants EIA0225656, EIA0225636, CAREER CC044917 and National Institutes of Health NIH grant LM07994-01. Team I (ML and AP) warmly thanks A Vazquez for his support. DPH and JAB were supported by HG002273. LP-C and TRH were supported by a CIHR grant and thank O Meruvia for helping with the design of figures.


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