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

• Published in: Genome biology Vol. 9 Suppl 1; no. Suppl 1; p. S2
• Main Authors: Peña-Castillo, Lourdes, Tasan, Murat, Myers, Chad L, Lee, Hyunju, Joshi, Trupti, Zhang, Chao, Guan, Yuanfang, Leone, Michele, Pagnani, Andrea, Kim, Wan Kyu, Krumpelman, Chase, Tian, Weidong, Obozinski, Guillaume, Qi, Yanjun, Mostafavi, Sara, Lin, Guan Ning, Berriz, Gabriel F, Gibbons, Francis D, Lanckriet, Gert, Qiu, Jian, Grant, Charles, Barutcuoglu, Zafer, Hill, David P, Warde-Farley, David, Grouios, Chris, Ray, Debajyoti, Blake, Judith A, Deng, Minghua, Jordan, Michael I, Noble, William S, Morris, Quaid, Klein-Seetharaman, Judith, Bar-Joseph, Ziv, Chen, Ting, Sun, Fengzhu, Troyanskaya, Olga G, Marcotte, Edward M, Xu, Dong, Hughes, Timothy R, Roth, Frederick P
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01-01-2008; BioMed Central
• Subjects: Algorithms; Animals; Mice - genetics; Mice - metabolism; Mus musculus; Proteins - genetics; Proteins - metabolism
• ISSN: 1465-6906; 1474-760X; 1465-6914
• DOI: 10.1186/gb-2008-9-s1-s2

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.
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%.
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.