Predicting Transcription Factor Binding Sites and Their Cognate Transcription Factors Using Gene Expression Data

2017 ◽  
pp. 271-282 ◽  
Author(s):  
Chun-Ping Yu ◽  
Wen-Hsiung Li
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Gene Expression Data ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Expression Data ◽  
Factor Binding

CLUSTERING AND RE-CLUSTERING FOR PATTERN DISCOVERY IN GENE EXPRESSION DATA

2005 ◽  
Vol 03 (02) ◽  
pp. 281-301 ◽  
Author(s):  
PATRICK C. H. MA ◽  
KEITH C. C. CHAN ◽  
DAVID K. Y. CHIU
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Gene Expression Data ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Pairwise Distance ◽  
Expression Data ◽  
Promoter Regions ◽  
Factor Binding

The combined interpretation of gene expression data and gene sequences is important for the investigation of the intricate relationships of gene expression at the transcription level. The expression data produced by microarray hybridization experiments can lead to the identification of clusters of co-expressed genes that are likely co-regulated by the same regulatory mechanisms. By analyzing the promoter regions of co-expressed genes, the common regulatory patterns characterized by transcription factor binding sites can be revealed. Many clustering algorithms have been used to uncover inherent clusters in gene expression data. In this paper, based on experiments using simulated and real data, we show that the performance of these algorithms could be further improved. For the clustering of expression data typically characterized by a lot of noise, we propose to use a two-phase clustering algorithm consisting of an initial clustering phase and a second re-clustering phase. The proposed algorithm has several desirable features: (i) it utilizes both local and global information by computing both a "local" pairwise distance between two gene expression profiles in Phase 1 and a "global" probabilistic measure of interestingness of cluster patterns in Phase 2, (ii) it distinguishes between relevant and irrelevant expression values when performing re-clustering, and (iii) it makes explicit the patterns discovered in each cluster for possible interpretations. Experimental results show that the proposed algorithm can be an effective algorithm for discovering clusters in the presence of very noisy data. The patterns that are discovered in each cluster are found to be meaningful and statistically significant, and cannot otherwise be easily discovered. Based on these discovered patterns, genes co-expressed under the same experimental conditions and range of expression levels have been identified and evaluated. When identifying regulatory patterns at the promoter regions of the co-expressed genes, we also discovered well-known transcription factor binding sites in them. These binding sites can provide explanations for the co-expressed patterns.

scholarly journals Integrating binding and expression data to predict transcription factors combined function

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Mahmoud Ahmed ◽  
Do Sik Min ◽  
Deok Ryong Kim
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Regulatory Region ◽  
Transcription Factor Binding ◽  
Expression Data ◽  
Dominant Type ◽  
Factor Binding ◽  
Two Factors

Abstract Background Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. The binding of two factors to a shared site does not always lead to a functional interaction. Results We propose a method to predict the combined functions of two factors using comparable binding and expression data (target). We based this method on binding and expression target analysis (BETA), which we re-implemented in R and extended for this purpose. target ranks the factor’s targets by importance and predicts the dominant type of interaction between two transcription factors. We applied the method to simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of factors. We found that Yin Yang 1 transcription factor (YY1) and YY2 have antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a few shared targets. Conclusion We developed an R package and a web application to integrate binding (ChIP-seq) and expression (microarrays or RNA-seq) data to determine the cooperative or competitive combined function of two transcription factors.

scholarly journals Understanding Transcription Factor Regulation by Integrating Gene Expression and DNase I Hypersensitive Sites

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Guohua Wang ◽  
Fang Wang ◽  
Qian Huang ◽  
Yu Li ◽  
Yunlong Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Interferon Treatment ◽  
Factor Binding ◽  
Hypersensitive Sites

Transcription factors are proteins that bind to DNA sequences to regulate gene transcription. The transcription factor binding sites are short DNA sequences (5–20 bp long) specifically bound by one or more transcription factors. The identification of transcription factor binding sites and prediction of their function continue to be challenging problems in computational biology. In this study, by integrating the DNase I hypersensitive sites with known position weight matrices in the TRANSFAC database, the transcription factor binding sites in gene regulatory region are identified. Based on the global gene expression patterns in cervical cancer HeLaS3 cell and HelaS3-ifnα4h cell (interferon treatment on HeLaS3 cell for 4 hours), we present a model-based computational approach to predict a set of transcription factors that potentially cause such differential gene expression. Significantly, 6 out 10 predicted functional factors, including IRF, IRF-2, IRF-9, IRF-1 and IRF-3, ICSBP, belong to interferon regulatory factor family and upregulate the gene expression levels responding to the interferon treatment. Another factor, ISGF-3, is also a transcriptional activator induced by interferon alpha. Using the different transcription factor binding sites selected criteria, the prediction result of our model is consistent. Our model demonstrated the potential to computationally identify the functional transcription factors in gene regulation.

scholarly journals In Silico Analysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding Sites

2016 ◽  
Vol 2016 ◽  
pp. 1-27 ◽  
Author(s):  
Kristopher J. L. Irizarry ◽  
Randall L. Bryden
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Coat Color ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Color Variation ◽  
Factor Binding ◽  
Conserved Sequence ◽  
Evolutionarily Conserved

Color variation provides the opportunity to investigate the genetic basis of evolution and selection. Reptiles are less studied than mammals. Comparative genomics approaches allow for knowledge gained in one species to be leveraged for use in another species. We describe a comparative vertebrate analysis of conserved regulatory modules in pythons aimed at assessing bioinformatics evidence that transcription factors important in mammalian pigmentation phenotypes may also be important in python pigmentation phenotypes. We identified 23 python orthologs of mammalian genes associated with variation in coat color phenotypes for which we assessed the extent of pairwise protein sequence identity between pythons and mouse, dog, horse, cow, chicken, anole lizard, and garter snake. We next identified a set of melanocyte/pigment associated transcription factors (CREB, FOXD3, LEF-1, MITF, POU3F2, and USF-1) that exhibit relatively conserved sequence similarity within their DNA binding regions across species based on orthologous alignments across multiple species. Finally, we identified 27 evolutionarily conserved clusters of transcription factor binding sites within ~200-nucleotide intervals of the 1500-nucleotide upstream regions of AIM1, DCT, MC1R, MITF, MLANA, OA1, PMEL, RAB27A, and TYR from Python bivittatus. Our results provide insight into pigment phenotypes in pythons.

scholarly journals Bioinformatics Analysis of SNPs in IL-6 Gene Promoter of Jinghai Yellow Chickens

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 446 ◽  
Author(s):  
Shijie Xin ◽  
Xiaohui Wang ◽  
Guojun Dai ◽  
Jingjing Zhang ◽  
Tingting An ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Promoter Region ◽  
Bioinformatics Analysis ◽  
Cpg Islands ◽  
Regulatory Region ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding

The proinflammatory cytokine, interleukin-6 (IL-6), plays a critical role in many chronic inflammatory diseases, particularly inflammatory bowel disease. To investigate the regulation of IL-6 gene expression at the molecular level, genomic DNA sequencing of Jinghai yellow chickens (Gallus gallus) was performed to detect single-nucleotide polymorphisms (SNPs) in the region −2200 base pairs (bp) upstream to 500 bp downstream of IL-6. Transcription factor binding sites and CpG islands in the IL-6 promoter region were predicted using bioinformatics software. Twenty-eight SNP sites were identified in IL-6. Four of these 28 SNPs, three [−357 (G > A), −447 (C > G), and −663 (A > G)] in the 5′ regulatory region and one in the 3′ non-coding region [3177 (C > T)] are not labelled in GenBank. Bioinformatics analysis revealed 11 SNPs within the promoter region that altered putative transcription factor binding sites. Furthermore, the C-939G mutation in the promoter region may change the number of CpG islands, and SNPs in the 5′ regulatory region may influence IL-6 gene expression by altering transcription factor binding or CpG methylation status. Genetic diversity analysis revealed that the newly discovered A-663G site significantly deviated from Hardy-Weinberg equilibrium. These results provide a basis for further exploration of the promoter function of the IL-6 gene and the relationships of these SNPs to intestinal inflammation resistance in chickens.

scholarly journals Pleiotropy in enhancer function is encoded through diverse genetic architectures

2017 ◽  
Author(s):  
Ella Preger-Ben Noon ◽  
Gonzalo Sabarís ◽  
Daniela Ortiz ◽  
Jonathan Sager ◽  
Anna Liebowitz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Developmental Stages ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Drive Gene Expression ◽  
Enhancer Function ◽  
Drive Gene

AbstractDevelopmental genes can have complex c/s-regulatory regions, with multiple enhancers scattered across stretches of DNA spanning tens or hundreds of kilobases. Early work revealed remarkable modularity of enhancers, where distinct regions of DNA, bound by combinations of transcription factors, drive gene expression in defined spatio-temporal domains. Nevertheless, a few reports have shown that enhancer function may be required in multiple developmental stages, implying that regulatory elements can be pleiotropic. In these cases, it is not clear whether the pleiotropic enhancers employ the same transcription factor binding sites to drive expression at multiple developmental stages or whether enhancers function as chromatin scaffolds, where independent sets of transcription factor binding sites act at different stages. In this work we have studied the activity of the enhancers of the shavenbaby gene throughout D. melanogaster development. We found that all seven shavenbaby enhancers drive gene expression in multiple tissues and developmental stages at varying levels of redundancy. We have explored how this pleiotropy is encoded in two of these enhancers. In one enhancer, the same transcription factor binding sites contribute to embryonic and pupal expression, whereas for a second enhancer, these roles are largely encoded by distinct transcription factor binding sites. Our data suggest that enhancer pleiotropy might be a common feature of c/s-regulatory regions of developmental genes and that this pleiotropy can be encoded through multiple genetic architectures.

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