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Nucleic Acids Res. 2019 Mar 1. pii: gkz139. doi: 10.1093/nar/gkz139. [Epub ahead of print]

Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets.

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School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.
School of Computer Science and Engineering, Kyungsung University, Busan 48434, Republic of Korea.
Department of Functional Genomics, University of Science and Technology (UST), Daejeon 34141, Republic of Korea.
Genome Editing Research Center, Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea.
Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.


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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