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Updated: Jan 28, 2026

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
Published on: June 15, 2016
Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets
Sora Yoon1, Hai C T Nguyen1, Woobeen Jo1
1School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.
Abstract:
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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