Transcription of a large set of nuclear-encoded genes underlies biogenesis of mitochondria, regulated by a complex network of transcription factors and co-regulators. A remarkable heterogeneity can be detected in the expression of these genes in different cell types and tissues, and the recent availability of large gene expression compendiums allows the quantification of specific mitochondrial biogenesis patterns. We have developed a method to effectively perform this task. Massively correlated biclustering (MCbiclust) is a novel bioinformatics method that has been successfully applied to identify co-regulation patterns in large genesets, underlying essential cellular functions and determining cell types. The method has been recently evaluated and made available as a package in Bioconductor for R. One of the potential applications of the method is to compare expression of nuclear-encoded mitochondrial genes or larger sets of metabolism-related genes between different cell types or cellular metabolic states. Here we describe the essential steps to use MCbiclust as a tool to investigate co-regulation of mitochondrial genes and metabolic pathways.

Biclustering analysis of co-regulation patterns in nuclear-encoded mitochondrial genes and metabolic pathways

Szabadkai G.
2019

Abstract

Transcription of a large set of nuclear-encoded genes underlies biogenesis of mitochondria, regulated by a complex network of transcription factors and co-regulators. A remarkable heterogeneity can be detected in the expression of these genes in different cell types and tissues, and the recent availability of large gene expression compendiums allows the quantification of specific mitochondrial biogenesis patterns. We have developed a method to effectively perform this task. Massively correlated biclustering (MCbiclust) is a novel bioinformatics method that has been successfully applied to identify co-regulation patterns in large genesets, underlying essential cellular functions and determining cell types. The method has been recently evaluated and made available as a package in Bioconductor for R. One of the potential applications of the method is to compare expression of nuclear-encoded mitochondrial genes or larger sets of metabolism-related genes between different cell types or cellular metabolic states. Here we describe the essential steps to use MCbiclust as a tool to investigate co-regulation of mitochondrial genes and metabolic pathways.
2019
Cancer Metabolism
978-1-4939-9026-9
978-1-4939-9027-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3350057
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