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MetaMirClust: Discovery of miRNA cluster patterns using a data-mining approach
Authors:Chan Wen-Ching  Ho Meng-Ru  Li Sung-Chou  Tsai Kuo-Wang  Lai Chun-Hung  Hsu Chun-Nan  Lin Wen-Chang
Affiliation:Institute of Biomedical Informatics, National Yang-Ming University, Academia Sinica, Taipei, Taiwan, ROC; Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan, ROC; Institute of Information Sciences, Academia Sinica, Taipei, Taiwan, ROC.
Abstract:Recent genome-wide surveys on ncRNA have revealed that a substantial fraction of miRNA genes is likely to form clusters. However, the evolutionary and biological function implications of clustered miRNAs are still elusive. After identifying clustered miRNA genes under different maximum inter-miRNA distances (MIDs), this study intended to reveal evolution conservation patterns among these clustered miRNA genes in metazoan species using a computation algorithm. As examples, a total of 15-35% of known and predicted miRNA genes in nine selected species constitute clusters under the MIDs ranging from 1kb to 50kb. Intriguingly, 33 out of 37 metazoan miRNA clusters in 56 metazoan genomes are co-conserved with their up/down-stream adjacent protein-coding genes. Meanwhile, a co-expression pattern of miR-1 and miR-133a in the mir-133-1 cluster has been experimentally demonstrated. Therefore, the MetaMirClust database provides a useful bioinformatic resource for biologists to facilitate the advanced interrogations on the composition of miRNA clusters and their evolution patterns.
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