Identification of context-specific gene regulatory networks with GEMULA--Gene Expression Modeling Using LAsso

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Motivation: Gene regulatory networks, in which edges between nodes describe interactions between transcriptional regulators and their target genes, determine the coordinated spatiotemporal expression of genes. Especially in higher organisms, context-specific combinatorial regulation by transcription factors (TFs) is believed to determine cellular states and fates. TF-target gene interactions can be studied using high-throughput techniques such as ChIP-chip or ChIP-Seq. These experiments are time and cost intensive, and further limited by, for instance, availability of high affinity TF antibodies. Hence, there is a practical need for methods that can predict TF-TF and TF-target gene interactions in silico, i.e. from gene expression and DNA sequence data alone. We propose GEMULA, a novel approach based on linear models to predict TF-gene expression associations and TF-TF interactions from experimental data. GEMULA is based on linear models, fast and considers a wide range of biologically plausible models that describe gene expression data as a function of predicted TF binding to gene promoters. Results: We show that models inferred with GEMULA are able to explain roughly 70% of the observed variation in gene expression in the yeast heat shock response. The functional relevance of the inferred TF-TF interactions in these models are validated by different sources of independent experimental evidence. We also have applied GEMULA to an in vitro model of neuronal outgrowth. Our findings confirm existing knowledge on gene regulatory interactions underlying neuronal outgrowth, but importantly also generate new insights into the temporal dynamics of this gene regulatory network that can now be addressed experimentally. © The Author 2011. Published by Oxford University Press. All rights reserved.
Original languageEnglish
Pages (from-to)214-221
Number of pages8
JournalBioinformatics
Volume28
Issue number2
DOIs
Publication statusPublished - 2012

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Lasso
Transcription factors
Gene Regulatory Networks
Gene Regulatory Network
Transcription Factor
Gene expression
Gene Expression
Transcription Factors
Genes
Modeling
Gene
Interaction
Chip
Target
Context
alachlor
Linear Models
Linear Model
Heat-Shock Response
Predict

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title = "Identification of context-specific gene regulatory networks with GEMULA--Gene Expression Modeling Using LAsso",
abstract = "Motivation: Gene regulatory networks, in which edges between nodes describe interactions between transcriptional regulators and their target genes, determine the coordinated spatiotemporal expression of genes. Especially in higher organisms, context-specific combinatorial regulation by transcription factors (TFs) is believed to determine cellular states and fates. TF-target gene interactions can be studied using high-throughput techniques such as ChIP-chip or ChIP-Seq. These experiments are time and cost intensive, and further limited by, for instance, availability of high affinity TF antibodies. Hence, there is a practical need for methods that can predict TF-TF and TF-target gene interactions in silico, i.e. from gene expression and DNA sequence data alone. We propose GEMULA, a novel approach based on linear models to predict TF-gene expression associations and TF-TF interactions from experimental data. GEMULA is based on linear models, fast and considers a wide range of biologically plausible models that describe gene expression data as a function of predicted TF binding to gene promoters. Results: We show that models inferred with GEMULA are able to explain roughly 70{\%} of the observed variation in gene expression in the yeast heat shock response. The functional relevance of the inferred TF-TF interactions in these models are validated by different sources of independent experimental evidence. We also have applied GEMULA to an in vitro model of neuronal outgrowth. Our findings confirm existing knowledge on gene regulatory interactions underlying neuronal outgrowth, but importantly also generate new insights into the temporal dynamics of this gene regulatory network that can now be addressed experimentally. {\circledC} The Author 2011. Published by Oxford University Press. All rights reserved.",
author = "G. Geeven and {van Kesteren}, R.E. and A.B. Smit and {de Gunst}, M.C.M.",
year = "2012",
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Identification of context-specific gene regulatory networks with GEMULA--Gene Expression Modeling Using LAsso. / Geeven, G.; van Kesteren, R.E.; Smit, A.B.; de Gunst, M.C.M.

In: Bioinformatics, Vol. 28, No. 2, 2012, p. 214-221.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Identification of context-specific gene regulatory networks with GEMULA--Gene Expression Modeling Using LAsso

AU - Geeven, G.

AU - van Kesteren, R.E.

AU - Smit, A.B.

AU - de Gunst, M.C.M.

PY - 2012

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AB - Motivation: Gene regulatory networks, in which edges between nodes describe interactions between transcriptional regulators and their target genes, determine the coordinated spatiotemporal expression of genes. Especially in higher organisms, context-specific combinatorial regulation by transcription factors (TFs) is believed to determine cellular states and fates. TF-target gene interactions can be studied using high-throughput techniques such as ChIP-chip or ChIP-Seq. These experiments are time and cost intensive, and further limited by, for instance, availability of high affinity TF antibodies. Hence, there is a practical need for methods that can predict TF-TF and TF-target gene interactions in silico, i.e. from gene expression and DNA sequence data alone. We propose GEMULA, a novel approach based on linear models to predict TF-gene expression associations and TF-TF interactions from experimental data. GEMULA is based on linear models, fast and considers a wide range of biologically plausible models that describe gene expression data as a function of predicted TF binding to gene promoters. Results: We show that models inferred with GEMULA are able to explain roughly 70% of the observed variation in gene expression in the yeast heat shock response. The functional relevance of the inferred TF-TF interactions in these models are validated by different sources of independent experimental evidence. We also have applied GEMULA to an in vitro model of neuronal outgrowth. Our findings confirm existing knowledge on gene regulatory interactions underlying neuronal outgrowth, but importantly also generate new insights into the temporal dynamics of this gene regulatory network that can now be addressed experimentally. © The Author 2011. Published by Oxford University Press. All rights reserved.

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