scholarly journals Genomic analysis identifies a transcription-factor binding motif regulating expression of the alpha C protein in group B Streptococcus

2009 ◽  
Vol 46 (6) ◽  
pp. 315-320 ◽  
Author(s):  
David C. Klinzing ◽  
Lawrence C. Madoff ◽  
Karen M. Puopolo
Keyword(s):  
Transcription Factor ◽  
Group B Streptococcus ◽  
Genomic Analysis ◽  
Transcription Factor Binding ◽  
Binding Motif ◽  
Transcription Factor Binding Motif ◽  
C Protein ◽  
Factor Binding ◽  
Group B

scholarly journals Rational gRNA Design Based on Transcription Factor Binding Data

2021 ◽  
Author(s):  
David Bergenholm ◽  
Yasaman Dabirian ◽  
Raphael Ferreira ◽  
Verena Siewers ◽  
Florian David ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Genome Engineering ◽  
Transcription Factor Binding ◽  
Central Carbon Metabolism ◽  
Binding Motif ◽  
Transcription Factor Binding Motif ◽  
Factor Binding ◽  
Transcription Factor Motif ◽  
Binding Data ◽  
Genes Encoding

Abstract The CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas9 system has become a standard tool in many genome engineering endeavors. The endonuclease-deficient version of Cas9 (dCas9) is also a powerful programmable tool for gene regulation. In this study, we made use of Saccharomyces cerevisiae transcription factor binding data to obtain a better understanding of the interplay between transcription factor binding and binding of dCas9 fused to an activator domain, VPR. More specifically, we targeted dCas9-VPR towards binding sites of Gcr1-Gcr2 and Tye7 present in several promoters of genes encoding enzymes engaged in the central carbon metabolism. From our data, we observed an upregulation of gene expression when dCas9-VPR was targeted next to a transcription factor binding motif, whereas downregulation or no change was observed when dCas9 was bound on a transcription factor motif. This suggests a steric competition between dCas9 and the specific transcription factor. Integrating transcription factor binding data, therefore, proved to be useful for designing gRNAs for CRISPRi/a applications.

scholarly journals RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections

2017 ◽  
Vol 45 (13) ◽  
pp. e119-e119 ◽  
Author(s):  
Jaime Abraham Castro-Mondragon ◽  
Sébastien Jaeger ◽  
Denis Thieffry ◽  
Morgane Thomas-Chollier ◽  
Jacques van Helden
Keyword(s):  
Transcription Factor ◽  
Transcription Factor Binding ◽  
Binding Motif ◽  
Transcription Factor Binding Motif ◽  
Factor Binding ◽  
Redundancy Reduction

scholarly journals Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Giovanna Ambrosini ◽  
Ilya Vorontsov ◽  
Dmitry Penzar ◽  
Romain Groux ◽  
Oriol Fornes ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factor Binding ◽  
Binding Motif ◽  
Transcription Factor Binding Motif ◽  
Factor Binding

scholarly journals Genome-wide use of high- and low-affinity Tbrain transcription factor binding sites during echinoderm development

2017 ◽  
Vol 114 (23) ◽  
pp. 5854-5861 ◽  
Author(s):  
Gregory A. Cary ◽  
Alys M. Cheatle Jarvela ◽  
Rene D. Francolini ◽  
Veronica F. Hinman
Keyword(s):  
Transcription Factor ◽  
Sea Urchins ◽  
Transcription Factor Binding ◽  
Model Systems ◽  
Binding Motif ◽  
Regulatory Sequences ◽  
Binding Motifs ◽  
Chip Sequencing ◽  
Factor Binding ◽  
Genome Wide

Sea stars and sea urchins are model systems for interrogating the types of deep evolutionary changes that have restructured developmental gene regulatory networks (GRNs). Although cis-regulatory DNA evolution is likely the predominant mechanism of change, it was recently shown that Tbrain, a Tbox transcription factor protein, has evolved a changed preference for a low-affinity, secondary binding motif. The primary, high-affinity motif is conserved. To date, however, no genome-wide comparisons have been performed to provide an unbiased assessment of the evolution of GRNs between these taxa, and no study has attempted to determine the interplay between transcription factor binding motif evolution and GRN topology. The study here measures genome-wide binding of Tbrain orthologs by using ChIP-sequencing and associates these orthologs with putative target genes to assess global function. Targets of both factors are enriched for other regulatory genes, although nonoverlapping sets of functional enrichments in the two datasets suggest a much diverged function. The number of low-affinity binding motifs is significantly depressed in sea urchins compared with sea star, but both motif types are associated with genes from a range of functional categories. Only a small fraction (∼10%) of genes are predicted to be orthologous targets. Collectively, these data indicate that Tbr has evolved significantly different developmental roles in these echinoderms and that the targets and the binding motifs in associated cis-regulatory sequences are dispersed throughout the hierarchy of the GRN, rather than being biased toward terminal process or discrete functional blocks, which suggests extensive evolutionary tinkering.

scholarly journals Local DNA shape is a general principle of transcription factor binding specificity in Arabidopsis thaliana

2020 ◽  
Author(s):  
Janik Sielemann ◽  
Donat Wulf ◽  
Romy Schmidt ◽  
Andrea Bräutigam
Keyword(s):  
Arabidopsis Thaliana ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Affinity Purification ◽  
Transcription Factor Binding ◽  
Binding Motif ◽  
Binding Prediction ◽  
Factor Binding ◽  
Binding Behavior ◽  
A Genome

A genome encodes two types of information, the “what can be made” and the “when and where”. The “what” are mostly proteins which perform the majority of functions within living organisms and the “when and where” is the regulatory information that encodes when and where proteins are made. Currently, it is possible to efficiently predict the majority of the protein content of a genome but nearly impossible to predict the transcriptional regulation. This regulation is based upon the interaction between transcription factors and genomic sequences at the site of binding motifs1,2,3. Information contained within the motif is necessary to predict transcription factor binding, however, it is not sufficient4. Peaks detected in amplified DNA affinity purification sequencing (ampDAP-seq) and the motifs derived from them only partially overlap in the genome3 indicating that the sequence holds information beyond the binding motif. Here we show a random forest machine learning approach which incorporates the 3D-shape improved the area under the precision recall curve for binding prediction for all 216 tested Arabidopsis thaliana transcription factors. The method resolved differential binding of transcription factor family members which share the same binding motif. The models correctly predicted the binding behavior of novel, not-in-genome motif sequences. Understanding transcription factor binding as a combination of motif sequence and motif shape brings us closer to predicting gene expression from promoter sequence.

scholarly journals GRID-seq assisted prediction of transcription factor binding motifs

2018 ◽  
Author(s):  
Werner Pieter Veldsman
Keyword(s):  
Transcription Factor ◽  
Deep Learning ◽  
Rna Sequencing ◽  
Transcription Factor Binding ◽  
Binding Motif ◽  
Functional Protein ◽  
Binding Motifs ◽  
Factor Binding ◽  
Dnase Hypersensitivity ◽  
Transcription Factor Binding Motifs

AbstractExperimental validation of computationally predicted transcription factor binding motifs is desirable. Increased RNA levels in the vicinity of predicted protein-chromosomal binding motifs intuitively suggest regulatory activity. With this intuition in mind, the approach presented here juxtaposes publicly available experimentally derived GRID-seq data with binding motif predictions computationally determined by deep learning models. The aim is to demonstrate the feasibility of using RNA-sequencing data to improve binding motif prediction accuracy. Publicly available GRID-seq scores and computed DeepBind scores could be aggregated by chromosomal region and anomalies within the aggregated data could be detected using mahalanobis distance analysis. A mantel’s test of matrices containing pairwise hamming distances showed significant differences between 1) randomly ranked sequences, 2) sequences ranked by non-GRID-seq assisted scores, and 3) sequences ranked by GRID-seq assisted scores. Plots of mahalanobis ranked binding motifs revealed an inversely proportional relationship between GRID-seq scores and DeepBind scores. Data points with high DeepBind scores but low GRID-seq scores had no DNAse hypersensitivity clusters annotated to their respective sequences. However, DNase hypersensitivity was observed for high scoring DeepBind motifs with moderate GRID-seq scores. Binding motifs of interest were recognized by their deviance from the inversely proportional tendency, and the underlying context sequences of these predicted motifs were on occasion associated with DNAse hypersensitivity unlike the most highly ranked motif scores when DeepBind was used in isolation. This article presents a novel combinatory approach to predict functional protein-chromosomal binding motifs. The two underlying methods are based on recent developments in the fields of RNA sequencing and deep learning, respectively. They are shown to be suited for synergistic use, which broadens the scope of their respective applications.

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