Epigenetic priors for identifying active transcription factor binding sites

Cuéllar-Partida, Gabriel; Buske, Fabian A.; McLeay, Robert C.; Whitington, Tom; Noble, William Stafford; Bailey, Timothy L.

doi:10.1093/bioinformatics/btr614

Cited by 105 publications

(125 citation statements)

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“…Using machine learning methods, 43 these predictions can be used to identify TFs acting as key regulators [4,46,47]. 44 In addition to open-chromatin data, also Histone Modification (HM) ChIP-seq data 45 was used for the prediction of TF binding [4,5,13,24,51,67]. In these studies, HMs were 46 used either exclusively or along with open-chromatin data.…”

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confidence: 99%

“…It was shown that using 47 DNaseI-seq data alone can lead to highly accurate TF binding predictions [13,51], 48 therefore we mainly focus on open-chromatin data in this article. 49 There are two general classes of methods to predict TF binding: site-centric 50 methods [13,32,42,51,57,69], and segmentation-based methods [3,7,24,25,27,48,50,58]. 51 Site-centric methods require the identification of putative TF binding sites (TFBS) 52 using TF binding motifs represented with position weight matrices (pwms).…”

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confidence: 99%

“…There are various ways of incorporating epigenetic data in the to the closest TSS are considered using a hierarchical mixture model [51]. Another 58 approach is taken in [13]. Here, an epigenetic prior is computed using the DNaseI-seq 59 signal that is combined with a simple motif score.…”

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confidence: 99%

“…Thereby, we can assess the influence of the 181 affinity-based binding prediction on gene expression learning. In addition, we run Fimo 182 using a DNase prior [13], to compare TEPIC against a state of the art site-centric 183 approach. This comparison is also motivated by the fact that this method has been 184 used in a previous study on gene expression learning with TF binding predictions [46].…”

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confidence: 99%

“…This comparison is also motivated by the fact that this method has been 184 used in a previous study on gene expression learning with TF binding predictions [46]. 185 Transcription factor scores are computed in 3000bp and 50000bp windows around the The copyright holder for this preprint (which was not .calculated as described above for cases [1] and [2] using the log-ratio scores introduced 188 in [13]. Note that we thus do not log-transform the Fimo TF scores in the elastic net 189 model, comparable to [46].…”

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Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

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The binding and contribution of transcription factors (TF) to cell specific gene Using machine learning, we find low affinity binding sites to improve our ability to 10 explain gene expression variability compared to the standard presence/absence 11 classification of binding sites. Further, we show that both footprints and peaks capture 12 essential TF binding events and lead to a good prediction performance. In our nearly reach the quality of several TF ChIP-seq datasets. Finally, these scores correctly 15

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Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

Schmidt

Gasparoni

et al. 2016

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Systems biology approaches to toll‐like receptor signaling

Vandenbon

Teraguchi

Akira

et al. 2012

WIREs Mechanisms of Disease

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Toll-like receptor (TLR) signaling pathways constitute an evolutionarily conserved host defense system that protects against a broad range of infectious agents. Modeling of TLR signaling has been carried out at several levels. Structural models of TLRs and their adaptors, which utilize a small number of structural domains to recognize a diverse range of pathogens, provide a starting point for understanding how pathogens are recognized and signaling events initiated. Various experimental and computational techniques have been used to construct models of downstream signal transduction networks from the measurements of gene expression and chromatin structure under resting and perturbed conditions along with predicted regulatory sequence motifs. Although a complete and accurate mathematical model of all TLR signaling pathways has yet to be derived, many important modules have been identified and investigated, enhancing our understanding of innate immune responses. Extensions of these models based on emerging experimental techniques are discussed. © 2012 Wiley Periodicals, Inc.

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Piecing together cis‐regulatory networks: insights from epigenomics studies in plants

Huang

Ecker

2017

WIREs Mechanisms of Disease

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5-Methylcytosine, a chemical modification of DNA, is a covalent modification found in the genomes of both plants and animals. Epigenetic inheritance of phenotypes mediated by DNA methylation is well established in plants. Most of the known mechanisms of establishing, maintaining and modifying DNA methylation have been worked out in the reference plant Arabidopsis thaliana. Major functions of DNA methylation in plants include regulation of gene expression and silencing of transposable elements (TEs) and repetitive sequences, both of which have parallels in mammalian biology, involve interaction with the transcriptional machinery, and may have profound effects on the regulatory networks in the cell. Methylome and transcriptome dynamics have been investigated in development and environmental responses in Arabidopsis and agriculturally and ecologically important plants, revealing the interdependent relationship among genomic context, methylation patterns, and expression of TE and protein coding genes. Analyses of methylome variation among plant natural populations and species have begun to quantify the extent of genetic control of methylome variation vs. true epimutation, and model the evolutionary forces driving methylome evolution in both short and long time scales. The ability of DNA methylation to positively or negatively modulate binding affinity of transcription factors (TFs) provides a natural link from genome sequence and methylation changes to transcription. Technologies that allow systematic determination of methylation sensitivities of TFs, in native genomic and methylation context without confounding factors such as histone modifications, will provide baseline datasets for building cell-type- and individual-specific regulatory networks that underlie the establishment and inheritance of complex traits. This article is categorized under: Laboratory Methods and Technologies > Genetic/Genomic Methods Biological Mechanisms > Regulatory Biology.

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Epigenetic priors for identifying active transcription factor binding sites

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 105 publications

References 28 publications

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

Systems biology approaches to toll‐like receptor signaling

Piecing together cis‐regulatory networks: insights from epigenomics studies in plants

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