Open AccessBiclustering analysis of transcriptome big data identifies condition-specific microRNA targets
Author(s) -
Sora Yoon,
Nguyen Cao Truong Hai,
Woobeen Jo,
Jinhwan Kim,
Sang-Mun Chi,
Jiyoung Park,
SeonYoung Kim,
Dougu Nam
Publication year - 2019
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkz139
Subject(s) - biology , microrna , transcriptome , biclustering , computational biology , messenger rna , gene , bioinformatics , genetics , gene expression , cluster analysis , cure data clustering algorithm , correlation clustering , machine learning , computer science
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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