scholarly journals An improved ChEC-seq method accurately maps the genome-wide binding of transcription coactivators and sequence-specific transcription factors

2021 ◽  
Author(s):  
Rafal Donczew ◽  
Amélia Lalou ◽  
Didier Devys ◽  
Laszlo Tora ◽  
Steven Hahn
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Sequence ◽  
Dna Interactions ◽  
Experimental Conditions ◽  
Genome Wide ◽  
Protein Dna Interactions ◽  
Cleavage Step ◽  
Computational Analyses ◽  
Active Genes

AbstractMittal and colleagues have raised questions about mapping transcription factor locations on DNA using the MNase-based ChEC-seq method (Mittal et al., 2021). Partly due to this concern, we modified the experimental conditions of the MNase cleavage step and subsequent computational analyses, resulting in more stringent conditions for mapping protein-DNA interactions (Donczew et al., 2020). The revised method (dx.doi.org/10.17504/protocols.io.bizgkf3w) answers questions raised by Mittal et al. and, without changing earlier conclusions, identified widespread promoter binding of the transcription coactivators TFIID and SAGA at active genes. The revised method is also suitable for accurately mapping the genome-wide locations of DNA sequence-specific transcription factors.

scholarly journals ChIP-seq meta-analysis yields high quality training sets for enhancer classification

2018 ◽  
Author(s):  
Hana Imrichova ◽  
Stein Aerts
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Meta Analysis ◽  
Rank Aggregation ◽  
Training Data ◽  
Data Sets ◽  
High Quality ◽  
Experimental Conditions ◽  
Genome Wide ◽  
Training Sets

AbstractGenome-wide prediction of enhancers depends on high-quality positive and negative training sets. The use of ChIP-seq peaks as positive training data can be problematic due to high degrees of indirectly bound regions, and often poor overlap between experimental conditions.Here we explore meta-analysis of ChIP-seq data to generate high-quality training data for enhancer modeling. Our method is based on rank aggregation and identifies a core set of directly bound regions per transcription factor, exploiting between five and twenty ChIP-seq data sets per factor. We applied this method to six different transcription factors, namely TP53, REST, SOX2, GRHL2, HIF1A and PPARG. Sequence analysis and modeling of recurrently bound enhancers yielded distinct enhancer features for the different factors, whereby binding sites of REST and TP53 are strongly determined by their motif; binding of GRHL2 and SOX2 is determined by nucleosome positioning; and binding of PPARG and HIF1A depends on other transcription factors. In conclusion, meta-analysis of ChIP-seq peaks, and centering on motifs, allowed discovering new properties of transcription factor binding.

scholarly journals TFutils: Data structures for transcription factor bioinformatics

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 152
Author(s):  
Benjamin J. Stubbs ◽  
Shweta Gopaulakrishnan ◽  
Kimberly Glass ◽  
Nathalie Pochet ◽  
Celine Everaert ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Data Structures ◽  
Binding Sites ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Bioconductor Package ◽  
Genome Wide ◽  
Study Results ◽  
Integrative Analyses

DNA transcription is intrinsically complex. Bioinformatic work with transcription factors (TFs) is complicated by a multiplicity of data resources and annotations. The Bioconductor package TFutils includes data structures and functions to enhance the precision and utility of integrative analyses that have components involving TFs. TFutils provides catalogs of human TFs from three reference sources (CISBP, HOCOMOCO, and GO), a catalog of TF targets derived from MSigDb, and multiple approaches to enumerating TF binding sites. Aspects of integration of TF binding patterns and genome-wide association study results are explored in examples.

scholarly journals High similarity among ChEC-seq datasets

2021 ◽  
Author(s):  
Chitvan Mittal ◽  
Matthew J. Rossi ◽  
B. Franklin Pugh
Keyword(s):  
Rna Polymerase Ii ◽  
Transcriptional Regulators ◽  
Micrococcal Nuclease ◽  
High Similarity ◽  
Dna Interactions ◽  
Promoter Regions ◽  
Genome Wide ◽  
A Genome ◽  
Protein Dna Interactions ◽  
Negative Controls

AbstractChEC-seq is a method used to identify protein-DNA interactions across a genome. It involves fusing micrococcal nuclease (MNase) to a protein of interest. In principle, specific genome-wide interactions of the fusion protein with chromatin result in local DNA cleavages that can be mapped by DNA sequencing. ChEC-seq has been used to draw conclusions about broad gene-specificities of certain protein-DNA interactions. In particular, the transcriptional regulators SAGA, TFIID, and Mediator are reported to generally occupy the promoter/UAS of genes transcribed by RNA polymerase II in yeast. Here we compare published yeast ChEC-seq data performed with a variety of protein fusions across essentially all genes, and find high similarities with negative controls. We conclude that ChEC-seq patterning for SAGA, TFIID, and Mediator differ little from background at most promoter regions, and thus cannot be used to draw conclusions about broad gene specificity of these factors.

scholarly journals Altered Transcription Factor Binding and Gene Bivalency in Islets of Intrauterine Growth Retarded Rats

Cells ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1435
Author(s):  
Yu-Chin Lien ◽  
Paul Zhiping Wang ◽  
Xueqing Maggie Lu ◽  
Rebecca A. Simmons
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Transcription Factor Binding ◽  
Binding Motifs ◽  
Factor Binding ◽  
Intrauterine Growth ◽  
Gene Dysregulation ◽  
Genome Wide

Intrauterine growth retardation (IUGR), which induces epigenetic modifications and permanent changes in gene expression, has been associated with the development of type 2 diabetes. Using a rat model of IUGR, we performed ChIP-Seq to identify and map genome-wide histone modifications and gene dysregulation in islets from 2- and 10-week rats. IUGR induced significant changes in the enrichment of H3K4me3, H3K27me3, and H3K27Ac marks in both 2-wk and 10-wk islets, which were correlated with expression changes of multiple genes critical for islet function in IUGR islets. ChIP-Seq analysis showed that IUGR-induced histone mark changes were enriched at critical transcription factor binding motifs, such as C/EBPs, Ets1, Bcl6, Thrb, Ebf1, Sox9, and Mitf. These transcription factors were also identified as top upstream regulators in our previously published transcriptome study. In addition, our ChIP-seq data revealed more than 1000 potential bivalent genes as identified by enrichment of both H3K4me3 and H3K27me3. The poised state of many potential bivalent genes was altered by IUGR, particularly Acod1, Fgf21, Serpina11, Cdh16, Lrrc27, and Lrrc66, key islet genes. Collectively, our findings suggest alterations of histone modification in key transcription factors and genes that may contribute to long-term gene dysregulation and an abnormal islet phenotype in IUGR rats.

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