LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data

G. Geeven, H.D. MacGillavry, R. Eggers, M.M. Sassen, J. Verhaagen, A.B. Smit, M.C.M. de Gunst, R.E. van Kesteren

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

All cellular processes are regulated by condition-specific and time-dependent interactions between transcription factors and their target genes. While in simple organisms, e.g. bacteria and yeast, a large amount of experimental data is available to support functional transcription regulatory interactions, in mammalian systems reconstruction of gene regulatory networks still heavily depends on the accurate prediction of transcription factor binding sites. Here, we present a new method, log-linear modeling of 3D contingency tables (LLM3D), to predict functional transcription factor binding sites. LLM3D combines gene expression data, gene ontology annotation and computationally predicted transcription factor binding sites in a single statistical analysis, and offers a methodological improvement over existing enrichment-based methods. We show that LLM3D successfully identifies novel transcriptional regulators of the yeast metabolic cycle, and correctly predicts key regulators of mouse embryonic stem cell self-renewal more accurately than existing enrichment-based methods. Moreover, in a clinically relevant in vivo injury model of mammalian neurons, LLM3D identified peroxisome proliferator-activated receptor γ (PPARγ) as a neuron-intrinsic transcriptional regulator of regenerative axon growth. In conclusion, LLM3D provides a significant improvement over existing methods in predicting functional transcription regulatory interactions in the absence of experimental transcription factor binding data. © 2011 The Author(s).
Original languageEnglish
Pages (from-to)5313-5327
JournalNucleic Acids Research
Volume39
Issue number13
DOIs
Publication statusPublished - 2011

Fingerprint

Regulator Genes
Transcription Factors
Genome
Binding Sites
Yeasts
Molecular Sequence Annotation
Neurons
Gene Ontology
Peroxisome Proliferator-Activated Receptors
Gene Regulatory Networks
Axons
Bacteria
Gene Expression
Wounds and Injuries
Growth
Genes

Cite this

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title = "LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data",
abstract = "All cellular processes are regulated by condition-specific and time-dependent interactions between transcription factors and their target genes. While in simple organisms, e.g. bacteria and yeast, a large amount of experimental data is available to support functional transcription regulatory interactions, in mammalian systems reconstruction of gene regulatory networks still heavily depends on the accurate prediction of transcription factor binding sites. Here, we present a new method, log-linear modeling of 3D contingency tables (LLM3D), to predict functional transcription factor binding sites. LLM3D combines gene expression data, gene ontology annotation and computationally predicted transcription factor binding sites in a single statistical analysis, and offers a methodological improvement over existing enrichment-based methods. We show that LLM3D successfully identifies novel transcriptional regulators of the yeast metabolic cycle, and correctly predicts key regulators of mouse embryonic stem cell self-renewal more accurately than existing enrichment-based methods. Moreover, in a clinically relevant in vivo injury model of mammalian neurons, LLM3D identified peroxisome proliferator-activated receptor γ (PPARγ) as a neuron-intrinsic transcriptional regulator of regenerative axon growth. In conclusion, LLM3D provides a significant improvement over existing methods in predicting functional transcription regulatory interactions in the absence of experimental transcription factor binding data. {\circledC} 2011 The Author(s).",
author = "G. Geeven and H.D. MacGillavry and R. Eggers and M.M. Sassen and J. Verhaagen and A.B. Smit and {de Gunst}, M.C.M. and {van Kesteren}, R.E.",
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LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data. / Geeven, G.; MacGillavry, H.D.; Eggers, R.; Sassen, M.M.; Verhaagen, J.; Smit, A.B.; de Gunst, M.C.M.; van Kesteren, R.E.

In: Nucleic Acids Research, Vol. 39, No. 13, 2011, p. 5313-5327.

Research output: Contribution to JournalArticleAcademicpeer-review

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AU - Geeven, G.

AU - MacGillavry, H.D.

AU - Eggers, R.

AU - Sassen, M.M.

AU - Verhaagen, J.

AU - Smit, A.B.

AU - de Gunst, M.C.M.

AU - van Kesteren, R.E.

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AB - All cellular processes are regulated by condition-specific and time-dependent interactions between transcription factors and their target genes. While in simple organisms, e.g. bacteria and yeast, a large amount of experimental data is available to support functional transcription regulatory interactions, in mammalian systems reconstruction of gene regulatory networks still heavily depends on the accurate prediction of transcription factor binding sites. Here, we present a new method, log-linear modeling of 3D contingency tables (LLM3D), to predict functional transcription factor binding sites. LLM3D combines gene expression data, gene ontology annotation and computationally predicted transcription factor binding sites in a single statistical analysis, and offers a methodological improvement over existing enrichment-based methods. We show that LLM3D successfully identifies novel transcriptional regulators of the yeast metabolic cycle, and correctly predicts key regulators of mouse embryonic stem cell self-renewal more accurately than existing enrichment-based methods. Moreover, in a clinically relevant in vivo injury model of mammalian neurons, LLM3D identified peroxisome proliferator-activated receptor γ (PPARγ) as a neuron-intrinsic transcriptional regulator of regenerative axon growth. In conclusion, LLM3D provides a significant improvement over existing methods in predicting functional transcription regulatory interactions in the absence of experimental transcription factor binding data. © 2011 The Author(s).

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