Reverse‐Engineering Transcriptional Modules from Gene Expression Data

Michoel, Tom; Smet, Riet De; Joshi, Anagha; Marchal, Kathleen; Peer, Yves Van de

doi:10.1111/j.1749-6632.2008.03943.x

Cited by 5 publications

(5 citation statements)

References 30 publications

(57 reference statements)

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“…This approach is highly analogous to [52] who generate bootstrap replicates of a module and locate the TF that correlates the strongest across the replicates. Our version of this approach performs well, presumably because it amplifies the signal to noise ratio (as it is not reliant on a significant connection to any given gene).…”

Section: Discussionmentioning

confidence: 99%

Inferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networks

et al. 2009

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BackgroundDespite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches.Methodology/Principal FindingsHere we report a simple algorithm that asks “which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?” It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1), glycolysis (HLF), mitochondrial transcription (TFB2M), adipogenesis (PIAS1), neuronal development (TLX3), immune function (IRF1) and vasculogenesis (SOX17), within a skeletal muscle context. However, none of the canonical pro-myogenic transcription factors (MYOD1, MYOG, MYF5, MYF6 and MEF2C) were linked to muscle structural gene expression modules. Co-expression values were computed using developing bovine muscle from 60 days post conception (early foetal) to 30 months post natal (adulthood) for two breeds of cattle, in addition to a nutritional comparison with a third breed. A number of transcriptional landscapes were constructed and integrated into an always correlated landscape. One notable feature was a ‘metabolic axis’ formed from glycolysis genes at one end, nuclear-encoded mitochondrial protein genes at the other, and centrally tethered by mitochondrially-encoded mitochondrial protein genes.Conclusions/SignificanceThe new module-to-regulator algorithm complements our recently described Regulatory Impact Factor analysis. Together with a simple examination of a co-expression module's contents, these three gene expression approaches are starting to illuminate the in vivo transcriptional regulation of skeletal muscle development.

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Section: Discussionmentioning

confidence: 99%

Inferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networks

et al. 2009

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“…A large body of gene-expression data is publicly available( Barrett et al 2011 ; Rustici et al 2013 ) and has enabled computational prediction of gene modules( Kim et al 2001 ; Segal et al 2003a ; Ihmels et al 2004 ; Michoel et al 2009 ; Engreitz et al 2010 ). We refer to our method for going from a raw compendium of gene expression data to an optimized set of gene modules and a list of genes that belong to each module as DEXICA, for D eep EX traction I ndependent C omponent A nalysis (described below).…”

Section: Resultsmentioning

confidence: 99%

Application of Transcriptional Gene Modules to Analysis of Caenorhabditis elegans’ Gene Expression Data

Cary

Podshivalova

Kenyon

2020

G3 Genes|Genomes|Genetics

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Identification of co-expressed sets of genes (gene modules) is used widely for grouping functionally related genes during transcriptomic data analysis. An organism-wide atlas of high-quality gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method based on independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organism Caenorhabditis elegans. These modules represent responses to changes in the environment (e.g. starvation, exposure to xenobiotics), genes regulated by transcriptions factors (e.g. ATFS-1, DAF-16), genes specific to tissues (e.g. neurons, muscle), genes that change during development, and other complex transcriptional responses to genetic, environmental and temporal perturbations. Interrogation of these modules reveals processes that are activated in long-lived mutants in cases where traditional analyses of differentially expressed genes fail to do so. Additionally, we show that modules can inform the strength of the association between a gene and an annotation (e.g. GO term). Analysis of "module-weighted annotations" improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. We provide an online interactive resource at http://genemodules.org/ in which users can find detailed information on each module, check genes for module-weighted annotations, and use both of these to analyze their own transcription data or gene sets of interest.

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“…A large body of gene expression data is publicly available 17,18 and has enabled computational prediction of gene modules 10,12,[19][20][21] . We refer to our method for going from a raw compendium of gene expression data to an optimized set of gene modules and a list of genes that belong to each module as DEXICA, for Deep EXtraction Independent Component Analysis (described below).…”

Section: Development Of Dexica and Extraction Of Gene Modulesmentioning

confidence: 99%

A resource for analyzing C. elegans’ gene expression data using transcriptional gene modules and module-weighted annotations

Cary

Podshivalova

Kenyon

2019

Preprint

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Identification of gene co-expression patterns (gene modules) is widely used for grouping functionally-related genes during transcriptomic data analysis. An organism-wide atlas of high quality fundamental gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method of independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organism C. elegans. Interrogation of these modules reveals processes that are activated in longlived mutants in cases where traditional analyses of differentially-expressed genes fail to do so. Using this resource, users can easily identify active modules in their gene expression data and access detailed descriptions of each module. Additionally, we show that modules can inform the strength of the association between a gene and an annotation (e.g. GO term). Analysis of "module-weighted annotations" improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. Interactive access to the resource is provided at http://genemodules.org/.

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Reverse‐Engineering Transcriptional Modules from Gene Expression Data

Cited by 5 publications

References 30 publications

Inferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networks

Inferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networks

Application of Transcriptional Gene Modules to Analysis of Caenorhabditis elegans’ Gene Expression Data

A resource for analyzing C. elegans’ gene expression data using transcriptional gene modules and module-weighted annotations

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