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Comprehensive characterization of cistrome and epicistrome involved in fruit ripening [green fruit]

ABSTRACT: The biggest advantage of DAP-seq is that it’s capable of measuring the influence of DNA methylation on TF binding, without disturbing of other epigenetic markers. Here, by utilizing DNA affinity purification sequencing, we generated two binding sets which were incubated with DNA libraries that representing methylomes in different developmental stage. And we made the most of our data to predict DNA methylation impact on TF binding in two dimensions: prediction by comparing genome-wide binding signal in motif-matched regions with or without methylation in single stage, and prediction by comparing binding signal variation in motif-matched regions with or without methylation variation.

ORGANISM(S): Solanum lycopersicum

PROVIDER: GSE172249 | GEO | 2023/06/30

REPOSITORIES: GEO

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Comprehensive characterization of cistrome and epicistrome involved in fruit ripening [red fruit]

Project description:The biggest advantage of DAP-seq is that it’s capable of measuring the influence of DNA methylation on TF binding, without disturbing of other epigenetic markers. Here, by utilizing DNA affinity purification sequencing, we generated two binding sets which were incubated with DNA libraries that representing methylomes in different developmental stage. And we made the most of our data to predict DNA methylation impact on TF binding in two dimensions: prediction by comparing genome-wide binding signal in motif-matched regions with or without methylation in single stage, and prediction by comparing binding signal variation in motif-matched regions with or without methylation variation.

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Base-resolution methylation patterns accurately predict transcription factor bindings in vivo

Project description:Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo. However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF-DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF-DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods. Examine Oct4 genome-wide binding in mouse embryonic stem cells (E14)

2015-03-02 | E-GEOD-65093 | biostudies-arrayexpress

Base-resolution methylation patterns accurately predict transcription factor bindings in vivo

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Project description:Transcription factor (TF) binding specificities (motifs) are essential to the analysis of noncoding DNA and gene regulation. Accurate prediction of TF sequence specificities is critical, because the hundreds of sequenced eukaryotic genomes encompass hundreds of thousands of TFs, and assaying each is currently infeasible. There is ongoing controversy regarding the efficacy of motif prediction methods, as well as the degree of motif diversification among related species. Here, we describe Similarity Regression (SR), a significantly improved method for predicting motifs. We have updated and expanded the Cis-BP database using SR, and validate its predictive capacity with new data from diverse eukaryotic TFs. SR inherently quantifies TF motif evolution, and we show that previous claims of near-complete conservation of motifs between human and Drosophila are grossly inflated, with nearly half the motifs in each species absent from the other. We conclude that diversification in DNA binding motifs is pervasive, and present a new tool and updated resource to study TF diversity and gene regulation across eukaryotes.

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DNA binding factors shape the mouse methylome at distal regulatory regions [strand-specific-RNA-seq]

Project description:To gain insights into the interplay between DNA methylation and gene regulation we generated a basepair resolution reference map of the mouse methylome in stem cells and neurons. High genome coverage allowed for a novel quantitative analysis of local methylation states, which identified Low Methylated Regions (LMR) with an average methylation of 30%. These regions are evolutionary conserved, reside outside of CpG islands and distal to promoters. They represent regulatory regions evidenced by their DNaseI hypersensitivity and chromatin marks of enhancer elements. LMRs are occupied by transcription factors (TF) and their reduced methylation requires TF binding while introduction of TF binding sites creates LMRs de novo. This dependency on TF activity is further evident when comparing the methylomes of embryonic stem cells and derived neuronal cells. LMRs present in both cell types are occupied by broadly expressed factors, while LMRs present at only one state are occupied by cell-type specific TFs. Methylome data can thus enhance the prediction of occupied TF binding sites and identification of active regulatory regions genome-wide. Our study provides reference methylomes for the mouse at two cell states, identifies a novel and highly dynamic feature of the epigenome that defines distal regulatory elements and shows that transcription factor binding dynamically shapes mammalian methylomes. Strand specific expression profiling by high throughput sequencing.

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