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dc.titleIdentifying co-regulating microrna groups
dc.contributor.authorAn, J.
dc.contributor.authorChoi, K.P.
dc.contributor.authorWells, C.A.
dc.contributor.authorChen, Y.-P.P.
dc.identifier.citationAn, J.,Choi, K.P.,Wells, C.A.,Chen, Y.-P.P. (2010-02). Identifying co-regulating microrna groups. Journal of Bioinformatics and Computational Biology 8 (1) : 99-115. ScholarBank@NUS Repository. <a href="" target="_blank"></a>
dc.description.abstractBackground: Current miRNA target prediction tools have the common problem that their false positive rate is high. This renders identification of co-regulating groups of miRNAs and target genes unreliable. In this study, we describe a procedure to identify highly probable co-regulating miRNAs and the corresponding co-regulated gene groups. Our procedure involves a sequence of statistical tests: (1) identify genes that are highly probable miRNA targets; (2) determine for each such gene, the minimum number of miRNAs that co-regulate it with high probability; (3) find, for each such gene, the combination of the determined minimum size of miRNAs that co-regulate it with the lowest p-value; and (4) discover for each such combination of miRNAs, the group of genes that are co-regulated by these miRNAs with the lowest p-value computed based on GO term annotations of the genes. Results: Our method identifies 4, 3 and 2-term miRNA groups that co-regulate gene groups of size at least 3 in human. Our result suggests some interesting hypothesis on the functional role of several miRNAs through a "guilt by association" reasoning. For example, miR-130, miR-19 and miR-101 are known neurodegenerative diseases associated miRNAs. Our 3-term miRNA table shows that miR-130/19/101 form a co-regulating group of rank 22 (p-value =1.16 × 10-2). Since miR-144 is co-regulating with miR-130, miR-19 and miR-101 of rank 4 (p-value = 1.16 × 10-2) in our 4-term miRNA table, this suggests hsa-miR-144 may be neurodegenerative diseases related miRNA. Conclusions: This work identifies highly probable co-regulating miRNAs, which are refined from the prediction by computational tools using (1) signal-to-noise ratio to get high accurate regulating miRNAs for every gene, and (2) Gene Ontology to obtain functional related co-regulating miRNA groups. Our result has partly been supported by biological experiments. Based on prediction by TargetScanS, we found highly probable target gene groups in the Supplementary Information. This result might help biologists to find small set of miRNAs for genes of interest rather than huge amount of miRNA set. Supplementary Information: . © 2010 Imperial College Press.
dc.subjectTarget gene
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.sourcetitleJournal of Bioinformatics and Computational Biology
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