Dong Wang1, Juan Wang, Ming Lu, Fei Song, Qinghua Cui. 1. Department of Biomedical Informatics, Peking University Health Science Center, 38 Xueyuan Road, Beijing 100191, China.
Abstract
MOTIVATION: It is popular to explore meaningful molecular targets and infer new functions of genes through gene functional similarity measuring and gene functional network construction. However, little work is available in this field for microRNA (miRNA) genes due to limited miRNA functional annotations. With the rapid accumulation of miRNAs, it is increasingly needed to uncover their functional relationships in a systems level. RESULTS: It is known that genes with similar functions are often associated with similar diseases, and the relationship of different diseases can be represented by a structure of directed acyclic graph (DAG). This is also true for miRNA genes. Therefore, it is feasible to infer miRNA functional similarity by measuring the similarity of their associated disease DAG. Based on the above observations and the rapidly accumulated human miRNA-disease association data, we presented a method to infer the pairwise functional similarity and functional network for human miRNAs based on the structures of their disease relationships. Comparisons showed that the calculated miRNA functional similarity is well associated with prior knowledge of miRNA functional relationship. More importantly, this method can also be used to predict novel miRNA biomarkers and to infer novel potential functions or associated diseases for miRNAs. In addition, this method can be easily extended to other species when sufficient miRNA-associated disease data are available for specific species. AVAILABILITY: The online tool is available at http://cmbi.bjmu.edu.cn/misim CONTACT: cuiqinghua@hsc.pku.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: It is popular to explore meaningful molecular targets and infer new functions of genes through gene functional similarity measuring and gene functional network construction. However, little work is available in this field for microRNA (miRNA) genes due to limited miRNA functional annotations. With the rapid accumulation of miRNAs, it is increasingly needed to uncover their functional relationships in a systems level. RESULTS: It is known that genes with similar functions are often associated with similar diseases, and the relationship of different diseases can be represented by a structure of directed acyclic graph (DAG). This is also true for miRNA genes. Therefore, it is feasible to infer miRNA functional similarity by measuring the similarity of their associated disease DAG. Based on the above observations and the rapidly accumulated human miRNA-disease association data, we presented a method to infer the pairwise functional similarity and functional network for human miRNAs based on the structures of their disease relationships. Comparisons showed that the calculated miRNA functional similarity is well associated with prior knowledge of miRNA functional relationship. More importantly, this method can also be used to predict novel miRNA biomarkers and to infer novel potential functions or associated diseases for miRNAs. In addition, this method can be easily extended to other species when sufficient miRNA-associated disease data are available for specific species. AVAILABILITY: The online tool is available at http://cmbi.bjmu.edu.cn/misim CONTACT: cuiqinghua@hsc.pku.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Victoria N Parikh; Richard C Jin; Sabrina Rabello; Natali Gulbahce; Kevin White; Andrew Hale; Katherine A Cottrill; Rahamthulla S Shaik; Aaron B Waxman; Ying-Yi Zhang; Bradley A Maron; Jochen C Hartner; Yuko Fujiwara; Stuart H Orkin; Kathleen J Haley; Albert-László Barabási; Joseph Loscalzo; Stephen Y Chan Journal: Circulation Date: 2012-02-27 Impact factor: 29.690