Computational analysis of tissue-specific gene networks: application to murine retinal functional studies
| Autor: | Jianfei Hu, Jiang Qian, Jun Wan, Laszlo Hackler, Donald J. Zack |
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| Rok vydání: | 2010 |
| Předmět: |
Statistics and Probability
Gene regulatory network Computational biology Biology Biochemistry Retina Mice Bayes' theorem Retinal Diseases Databases Genetic Gene expression Animals Gene Regulatory Networks Databases Protein Molecular Biology Gene Genetics Internet Bayes Theorem Original Papers Major gene Computer Science Applications Bayesian statistics Computational Mathematics ROC Curve Computational Theory and Mathematics sense organs Software Function (biology) Network analysis |
| Zdroj: | Bioinformatics. 26:2289-2297 |
| ISSN: | 1367-4811 1367-4803 |
| DOI: | 10.1093/bioinformatics/btq408 |
| Popis: | Motivation: The vertebrate retina is a complex neuronal tissue, and its development, normal functioning and response to injury and disease is subject to a variety of genetic factors. To understand better the regulatory and functional relationships between the genes expressed within the retina, we constructed an interactive gene network of the mouse retina by applying a Bayesian statistics approach to information derived from a variety of gene expression, protein–protein interaction and gene ontology annotation databases. Results: The network contains 673 retina-related genes. Most of them are obtained through manual literature-based curation, while the others are the genes preferentially expressed in the retina. These retina-related genes are linked by 3403 potential functional associations in the network. The prediction on the gene functional association using the Bayesian approach outperforms predictions using only one source of information. The network includes five major gene clusters, each enriched in different biological activities. There are several applications to this network. First, we identified ∼50 hub genes that are predicted to play particularly important roles in the function of the retina. Some of them are not yet well studied. Second, we can predict novel gene functions using ‘guilt by association’ method. Third, we also predicted novel retinal disease-associated genes based on the network analysis. Availability: To provide easy access to the retinal network, we constructed an interactive web tool, named MoReNet, which is available at http://bioinfo.wilmer.jhu.edu/morenet/ Contact: jiang.qian@jhmi.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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