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

Yoon, Susan A.; Nguyen, Hai Duong; Jo, Woobeen; Kim, Jinhwan; Chi, Sang-Mun; Park, Jiyoung; Kim, Seon‐Young; Nam, Dougu

doi:10.1093/nar/gkz139

Cited by 15 publications

(16 citation statements)

References 58 publications

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“…It is known that weak repression by miRNAs can nevertheless have a substantial effect on cell phenotype ( Flynt & Lai, 2008 ). Recently, data have been obtained supporting an FC threshold as low as 1.3 for functional miRNA targets ( Yoon et al, 2019 ). This threshold can apply to the miRNA-induced mRNA up-regulation.…”

Section: Discussionmentioning

confidence: 99%

Kalirin-RAC controls nucleokinetic migration in ADRN-type neuroblastoma

et al. 2021

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The migrational propensity of neuroblastoma is affected by cell identity, but the mechanisms behind the divergence remain unknown. Using RNAi and time-lapse imaging, we show that ADRN-type NB cells exhibit RAC1- and kalirin-dependent nucleokinetic (NUC) migration that relies on several integral components of neuronal migration. Inhibition of NUC migration by RAC1 and kalirin-GEF1 inhibitors occurs without hampering cell proliferation and ADRN identity. Using three clinically relevant expression dichotomies, we reveal that most of up-regulated mRNAs in RAC1- and kalirin–GEF1–suppressed ADRN-type NB cells are associated with low-risk characteristics. The computational analysis shows that, in a context of overall gene set poverty, the upregulomes in RAC1- and kalirin–GEF1–suppressed ADRN-type cells are a batch of AU-rich element–containing mRNAs, which suggests a link between NUC migration and mRNA stability. Gene set enrichment analysis–based search for vulnerabilities reveals prospective weak points in RAC1- and kalirin–GEF1–suppressed ADRN-type NB cells, including activities of H3K27- and DNA methyltransferases. Altogether, these data support the introduction of NUC inhibitors into cancer treatment research.

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Section: Discussionmentioning

confidence: 99%

Kalirin-RAC controls nucleokinetic migration in ADRN-type neuroblastoma

et al. 2021

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“…Different from other biological networks (i.e., protein-protein interaction network), the miRNA–target regulatory network is a bipartite network. Consequently, the generated miRNA–target modules are actually bicliques where every miRNA of the miRNA set is connected to each target gene of the target gene set ( Yoon et al, 2019 ). In this work, we utilize the R package biclique ( Zhang et al, 2014 ) to enumerate all bicliques from the identified miRNA–target bipartite network.…”

Section: Methodsmentioning

confidence: 99%

ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder

et al. 2020

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Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders characterized by genetic and environmental risk factors. The pathogenesis of ASD has a strong genetic basis, consisting of rare de novo or inherited variants among a variety of multiple molecules. Previous studies have shown that microRNAs (miRNAs) are involved in neurogenesis and brain development and are closely associated with the pathogenesis of ASD. However, the regulatory mechanisms of miRNAs in ASD are largely unclear. In this work, we present a stepwise method, ASDmiR, for the identification of underlying pathogenic genes, networks, and modules associated with ASD. First, we conduct a comparison study on 12 miRNA target prediction methods by using the matched miRNA, lncRNA, and mRNA expression data in ASD. In terms of the number of experimentally confirmed miRNA–target interactions predicted by each method, we choose the best method for identifying miRNA–target regulatory network. Based on the miRNA–target interaction network identified by the best method, we further infer miRNA–target regulatory bicliques or modules. In addition, by integrating high-confidence miRNA–target interactions and gene expression data, we identify three types of networks, including lncRNA–lncRNA, lncRNA–mRNA, and mRNA–mRNA related miRNA sponge interaction networks. To reveal the community of miRNA sponges, we further infer miRNA sponge modules from the identified miRNA sponge interaction network. Functional analysis results show that the identified hub genes, as well as miRNA-associated networks and modules, are closely linked with ASD. ASDmiR is freely available at https://github.com/chenchenxiong/ASDmiR .

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“…Biclustering has been successfully applied to hundreds of problems in the biological and biomedical domain and has supported detection of functional annotations (e.g., gene regulatory pathways), as well as biological interactions (e.g., transcriptional networks), the discovery of drugs and biomarkers, identification of subtypes of diseases, and analysis of responses to treatments [6]. A biclustering method has also helped to identify novel human microRNA regulatory modules [2]. Biclustering techniques have been also been successfully used in graph analysis, text mining, recommendation systems, marketing, economy (e.g., market segmentation), analysis of sports data, and multiple other domains [1].…”

Section: What Is Biclustering?mentioning

confidence: 99%

Scalable biclustering — the future of big data exploration?

2019

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Biclustering is a technique of discovering local similarities within data. For many years the complexity of the methods and parallelization issues limited its application to big data problems. With the development of novel scalable methods, biclustering has finally started to close this gap. In this paper we discuss the caveats of biclustering and present its current challenges and guidelines for practitioners. We also try to explain why biclustering may soon become one of the standards for big data analytics.

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

Cited by 15 publications

References 58 publications

Kalirin-RAC controls nucleokinetic migration in ADRN-type neuroblastoma

Kalirin-RAC controls nucleokinetic migration in ADRN-type neuroblastoma

ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder

Scalable biclustering — the future of big data exploration?

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