Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets
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- Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets
- Yoon, Sora; Nguyen, HCT; Jo, Woobeen; Kim, Jinhwan; Chi, Sang-Mun; Park, Jiyoung; Kim, Seon-Young; Nam, Dougu
- Issue Date
- Oxford University Press
- NUCLEIC ACIDS RESEARCH, v.47, no.9, pp.e53
- We present a novel approach to identify human microRNA (miRNA) regulatory modules (mRNA targets and relevant cell conditions) by biclustering a large collection of mRNA fold-change data for sequence-specific targets. Bicluster targets were assessed using validated messenger RNA (mRNA) targets and exhibited on an average 17.0% (median 19.4%) improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.4%) by incorporating functional networks of targets. We analyzed cancer-specific biclusters and found that the PI3K/Akt signaling pathway is strongly enriched with targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Indeed, five independent prognostic miRNAs were identified, and repression of bicluster targets and pathway activity by miR-29 was experimentally validated. In total, 29 898 biclusters for 459 human miRNAs were collected in the BiMIR database where biclusters are searchable for miRNAs, tissues, diseases, keywords and target genes.
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