Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Pena-Castillo L

PROVIDER: S-EPMC2447536 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Publications

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

Peña-Castillo Lourdes L Tasan Murat M Myers Chad L CL Lee Hyunju H Joshi Trupti T Zhang Chao C Guan Yuanfang Y Leone Michele M Pagnani Andrea A Kim Wan Kyu WK Krumpelman Chase C Tian Weidong W Obozinski Guillaume G Qi Yanjun Y Mostafavi Sara S Lin Guan Ning GN Berriz Gabriel F GF Gibbons Francis D FD Lanckriet Gert G Qiu Jian J Grant Charles C Barutcuoglu Zafer Z Hill David P DP Warde-Farley David D Grouios Chris C Ray Debajyoti D Blake Judith A JA Deng Minghua M Jordan Michael I MI Noble William S WS Morris Quaid Q Klein-Seetharaman Judith J Bar-Joseph Ziv Z Chen Ting T Sun Fengzhu F Troyanskaya Olga G OG Marcotte Edward M EM Xu Dong D Hughes Timothy R TR Roth Frederick P FP

Genome biology 20080627

<h4>Background</h4>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.<h4>Results</h4>In this ...[more]

PMID: 18613946

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Project description:BACKGROUND:Identification of protein-protein interactions is an important first step to understand living systems. High-throughput experimental approaches have accumulated large amount of information on protein-protein interactions in human and other model organisms. Such interaction information has been successfully transferred to other species, in which the experimental data are limited. However, the annotation transfer method could yield false positive interologs due to the lack of conservation of interactions when applied to phylogenetically distant organisms. RESULTS:To address this issue, we used phylogenetic profile method to filter false positives in interologs based on the notion that evolutionary conserved interactions show similar patterns of occurrence along the genomes. The approach was applied to Mus musculus, in which the experimentally identified interactions are limited. We first inferred the protein-protein interactions in Mus musculus by using two approaches: i) identifying mouse orthologs of interacting proteins (interologs) based on the experimental protein-protein interaction data from other organisms; and ii) analyzing frequency of mouse ortholog co-occurrence in predicted operons of bacteria. We then filtered possible false-positives in the predicted interactions using the phylogenetic profiles. We found that this filtering method significantly increased the frequency of interacting protein-pairs coexpressed in the same cells/tissues in gene expression omnibus (GEO) database as well as the frequency of interacting protein-pairs shared the similar Gene Ontology (GO) terms for biological processes and cellular localizations. The data supports the notion that phylogenetic profile helps to reduce the number of false positives in interologs. CONCLUSION:We have developed protein-protein interaction database in mouse, which contains 41109 interologs. We have also developed a web interface to facilitate the use of database http://lgsun.grc.nia.nih.gov/mppi/.

Dataset Information

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

Background

Results

Conclusion

Publications

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

Similar Datasets

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