scholarly journals CiiiDER: a new tool for predicting and analysing transcription factor binding sites

2019 ◽  
Author(s):  
Linden J. Gearing ◽  
Helen E. Cumming ◽  
Ross Chapman ◽  
Alexander M. Finkel ◽  
Isaac B. Woodhouse ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Transcription Factor Binding Sites ◽  
Computational Prediction ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Java Programming ◽  
Computer Operating System

AbstractThe availability of large amounts of high-throughput genomic, transcriptomic and epigenomic data has provided opportunity to understand regulation of the cellular transcriptome with an unprecedented level of detail. As a result, research has advanced from identifying gene expression patterns associated with particular conditions to elucidating signalling pathways that regulate expression. There are over 1,000 transcription factors (TFs) in vertebrates that play a role in this regulation. Determining which of these are likely to be controlling a set of genes can be assisted by computational prediction, utilising experimentally verified binding site motifs.Here we present CiiiDER, an integrated computational toolkit for transcription factor binding analysis, written in the Java programming language, to make it independent of computer operating system. It is operated through an intuitive graphical user interface with interactive, high-quality visual outputs, making it accessible to all researchers. CiiiDER predicts transcription factor binding sites (TFBSs) across regulatory regions of interest, such as promoters and enhancers derived from any species. It can perform an enrichment analysis to identify TFs that are significantly over- or under-represented in comparison to a bespoke background set and thereby elucidate pathways regulating sets of genes of pathophysiological importance.CiiiDER is available from www.ciiider.org.

Prioritize Transcription Factor Binding Sites for Multiple Co-Expressed Gene Sets Based on Lasso Multinomial Regression Models

2016 ◽  
pp. 280-315 ◽  
Author(s):  
Hong Hu ◽  
Yang Dai
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Regression Models ◽  
Expression Profiles ◽  
Transcription Factor Binding Sites ◽  
Computational Prediction ◽  
Transcription Factor Binding ◽  
Multinomial Regression ◽  
Factor Binding

Computational prediction of cis-regulatory elements for a set of co-expressed genes based on sequence analysis provides an overwhelming volume of potential transcription factor binding sites. It presents a challenge to prioritize a set of functional transcription factors and their binding sites on the regulatory regions of the target genes that are relevant to the gene expression study. A novel approach based on the use of lasso multinomial regression models is proposed to address this problem. We examine the ability of the lasso models using a time-course microarray data obtained from a comprehensive study of gene expression profiles in skin and mucosal in mouse over all stages of wound healing.

Prioritize Transcription Factor Binding Sites for Multiple Co-Expressed Gene Sets Based on Lasso Multinomial Regression Models

Biotechnology ◽  
2019 ◽  
pp. 940-968
Author(s):  
Hong Hu ◽  
Yang Dai
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Regression Models ◽  
Expression Profiles ◽  
Transcription Factor Binding Sites ◽  
Computational Prediction ◽  
Transcription Factor Binding ◽  
Multinomial Regression ◽  
Factor Binding

Computational prediction of cis-regulatory elements for a set of co-expressed genes based on sequence analysis provides an overwhelming volume of potential transcription factor binding sites. It presents a challenge to prioritize a set of functional transcription factors and their binding sites on the regulatory regions of the target genes that are relevant to the gene expression study. A novel approach based on the use of lasso multinomial regression models is proposed to address this problem. We examine the ability of the lasso models using a time-course microarray data obtained from a comprehensive study of gene expression profiles in skin and mucosal in mouse over all stages of wound healing.

scholarly journals Differential gene regulation in DAPT-treated Hydra reveals candidate direct notch signalling targets

2021 ◽  
Author(s):  
Jasmin Moneer ◽  
Stefan Siebert ◽  
Stefan Krebs ◽  
Jack Cazet ◽  
Andrea Prexl ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Expression Patterns ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Notch Signalling ◽  
Data Set ◽  
Regulatory Regions ◽  
Factor Binding

In Hydra, Notch inhibition causes defects in head patterning and prevents differentiation of proliferating nematocyte progenitor cells into mature nematocytes. To understand the molecular mechanisms by which the Notch pathway regulates these processes we performed RNAseq and identified genes that are differentially regulated in response to 48 hours of treating the animals with the Notch-inhibitor DAPT. To identify candidate direct regulators of Notch-signalling, we profiled gene expression changes that occur during subsequent restoration of Notch-activity and performed promoter analyses to identify RBPJ transcription factor binding sites in the regulatory regions of Notch-responsive genes. Interrogating the available single cell sequencing data set revealed gene expression patterns of Notch-regulated Hydra genes. By these analyses a comprehensive picture of the molecular pathways regulated by Notch signalling in head patterning and in interstitial cell differentiation in Hydra emerged. As prime candidates for direct Notch-target genes, in addition to HyHes, we suggest Sp5 and HyAlx. They rapidly recovered their expression levels after DAPT removal and possess Notch-responsive RBPJ transcription factor binding sites in their regulatory regions.

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