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

Bioinformatics approaches to predict target genes from transcription factor binding data

Methods ◽  
2017 ◽  
Vol 131 ◽  
pp. 111-119 ◽  
Author(s):  
Alexandra Essebier ◽  
Marnie Lamprecht ◽  
Michael Piper ◽  
Mikael Bodén
Keyword(s):  
Transcription Factor ◽  
Target Genes ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Binding Data

scholarly journals FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data

2017 ◽  
Author(s):  
Daniel Quang ◽  
Xiaohui Xie
Keyword(s):  
Neural Network ◽  
Transcription Factor ◽  
Deep Learning ◽  
Cell Types ◽  
Transcription Factor Binding ◽  
Cell Type ◽  
Neural Network Models ◽  
Factor Binding ◽  
Binding Data ◽  
Nucleotide Resolution

AbstractDue to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all TF/cell type pairs is not experimentally feasible, owing to constraints in time and resources. To address this issue, we developed a convolutional-recurrent neural network model, called FactorNet, to computationally impute the missing binding data. FactorNet trains on binding data from reference cell types to make accurate predictions on testing cell types by leveraging a variety of features, including genomic sequences, genome annotations, gene expression, and single-nucleotide resolution sequential signals, such as DNase I cleavage. To the best of our knowledge, this is the first deep learning method to study the rules governing TF binding at such a fine resolution. With FactorNet, a researcher can perform a single sequencing assay, such as DNase-seq, on a cell type and computationally impute dozens of TF binding profiles. This is an integral step for reconstructing the complex networks underlying gene regulation. While neural networks can be computationally expensive to train, we introduce several novel strategies to significantly reduce the overhead. By visualizing the neural network models, we can interpret how the model predicts binding which in turn reveals additional insights into regulatory grammar. We also investigate the variables that affect cross-cell type predictive performance to explain why the model performs better on some TF/cell types than others, and offer insights to improve upon this field. Our method ranked among the top four teams in the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge.

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