scholarly journals Raw sequence to target gene prediction: An integrated inference pipeline for ChIP-seq and RNA-seq datasets

2017 ◽  
Author(s):  
Nisar Wani ◽  
Khalid Raza
Keyword(s):  
Gene Expression ◽  
Target Gene ◽  
Target Genes ◽  
Gene Prediction ◽  
Expression Patterns ◽  
Dna Binding Proteins ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cellular Processes ◽  
Regulatory Effects

AbstractGene expression patterns determine the manner whereby organisms regulate various cellular processes and therefore their organ functions.These patterns do not emerge on their own, but as a result of diverse regulatory factors such as, DNA binding proteins known as transcription factors (TF), chromatin structure and various other environmental factors. TFs play a pivotal role in gene regulation by binding to different locations on the genome and influencing the expression of their target genes. Therefore, predicting target genes and their regulation becomes an important task for understanding mechanisms that control cellular processes governing both healthy and diseased cells.In this paper, we propose an integrated inference pipeline for predicting target genes and their regulatory effects for a specific TF using next-generation data analysis tools.

scholarly journals Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1933 ◽  
Author(s):  
Ruipeng Lu ◽  
Peter K. Rogan
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Transcription Factor ◽  
Binding Site ◽  
In Silico ◽  
Target Gene ◽  
Target Genes ◽  
Gene Prediction ◽  
Expression Patterns ◽  
Tree Classifier

Background:The distribution and composition ofcis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets.Methods:Genes with correlated expression patterns across 53 tissues and TF targets were respectively identified from Bray-Curtis Similarity and TF knockdown experiments. Corresponding promoter sequences were reduced to DNase I-accessible intervals; TFBSs were then identified within these intervals using information theory-based position weight matrices for each TF (iPWMs) and clustered. Features from information-dense TFBS clusters predicted these genes with machine learning classifiers, which were evaluated for accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed toin silicoexamine their impact on cluster densities and the regulatory states of target genes.Results:  We initially chose the glucocorticoid receptor gene (NR3C1), whose regulation has been extensively studied, to test this approach.SLC25A32andTANKwere found to exhibit the most similar expression patterns toNR3C1. A Decision Tree classifier exhibited the largest area under the Receiver Operating Characteristic (ROC) curve in detecting such genes. Target gene prediction was confirmed using siRNA knockdown of TFs, which was found to be more accurate than those predicted after CRISPR/CAS9 inactivation.In-silicomutation analyses of TFBSs also revealed that one or more information-dense TFBS clusters in promoters are required for accurate target gene prediction. Conclusions: Machine learning based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.

scholarly journals Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1933 ◽  
Author(s):  
Ruipeng Lu ◽  
Peter K. Rogan
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Site ◽  
Target Gene ◽  
Target Genes ◽  
Gene Prediction ◽  
Expression Patterns ◽  
Specificity And Sensitivity ◽  
Tree Classifier ◽  
Glucocorticoid Receptor Gene

Background:The distribution and composition ofcis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML).Methods:Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding promoter sequences using information theory-based position weight matrices (iPWMs) for each TF. Features from information-dense clusters of TFBSs were input to ML classifiers which predict these gene targets along with their accuracy, specificity and sensitivity. Mutations in TFBSs were analyzedin silicoto examine their impact on TFBS clustering and predict changes in gene regulation.Results: The glucocorticoid receptor gene (NR3C1), whose regulation has been extensively studied, was selected to test this approach.SLC25A32andTANKexhibited the most similar expression patterns toNR3C1. A Decision Tree classifier exhibited the best performance in detecting such genes, based on Area Under the Receiver Operating Characteristic curve (ROC). TF target gene prediction was confirmed using siRNA knockdown, which was more accurate than CRISPR/CAS9 inactivation. TFBS mutation analyses revealed that accurate target gene prediction required  at least 1  information-dense TFBS cluster. Conclusions: ML based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.

scholarly journals Clustered, information-dense transcription factor binding sites identify genes with similar tissue-wide expression profiles

2018 ◽  
Author(s):  
Ruipeng Lu ◽  
Peter K. Rogan
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Transcription Factor ◽  
Binding Site ◽  
Target Gene ◽  
Target Genes ◽  
Gene Prediction ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Factor Binding

ABSTRACTBackgroundThe distribution and composition ofcis-regulatory modules (e.g. transcription factor binding site (TFBS) clusters) in promoters substantially determine gene expression patterns and TF targets, whose expression levels are significantly regulated by TF binding. TF knockdown experiments have revealed correlations between TF binding profiles and gene expression levels. We present a general framework capable of predicting genes with similar tissue-wide expression patterns from activated or repressed TF targets using machine learning to combine TF binding and epigenetic features.MethodsGenes with correlated expression patterns across 53 tissues were identified according to their Bray-Curtis similarity. DNase I HyperSensitive region (DHS) -accessible promoter intervals of direct TF target genes were scanned with previously derived information theory-based position weight matrices (iPWMs) of 82 TFs. Features from information density-based TFBS clusters were used to predict target genes with machine learning classifiers. The accuracy, specificity and sensitivity of the classifiers were determined for different feature sets. Mutations in TFBSs were also introduced to examine their impact on cluster densities and the regulatory states of predicted target genes.ResultsWe initially chose the glucocorticoid receptor gene (NR3C1), whose regulation has been extensively studied, to test this approach.SLC25A32andTANKwere found to exhibit the most similar expression patterns to this gene across 53 tissues. Prediction of other genes with similar expression profiles was significantly improved by eliminating inaccessible promoter intervals based on DHSs. A Random Forest classifier exhibited the best performance in detecting such coordinately regulated genes (accuracy was 0.972 for training, 0.976 for testing). Target gene prediction was confirmed using CRISPR knockdown data of TFs, which was more accurate than siRNA inactivation. Mutation analyses of TFBSs also revealed that one or more information-dense TFBS clusters in promoters are required for accurate target gene prediction.ConclusionsMachine learning based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple, information-dense TFBS clusters in promoters appear to protect promoters from the effects of deleterious binding site mutations in a single TFBS that would effectively alter the expression state of these genes.

scholarly journals Elucidation of Regulatory Modes for Five Two-Component Systems in Escherichia coli Reveals Novel Relationships

mSystems ◽  
2020 ◽  
Vol 5 (6) ◽  
Author(s):  
Kumari Sonal Choudhary ◽  
Julia A. Kleinmanns ◽  
Katherine Decker ◽  
Anand V. Sastry ◽  
Ye Gao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Target Gene ◽  
Target Genes ◽  
Rna Seq ◽  
Content Type ◽  
E Coli ◽  
Component Systems ◽  
Two Component ◽  
Two Component Systems

ABSTRACT Escherichia coli uses two-component systems (TCSs) to respond to environmental signals. TCSs affect gene expression and are parts of E. coli’s global transcriptional regulatory network (TRN). Here, we identified the regulons of five TCSs in E. coli MG1655: BaeSR and CpxAR, which were stimulated by ethanol stress; KdpDE and PhoRB, induced by limiting potassium and phosphate, respectively; and ZraSR, stimulated by zinc. We analyzed RNA-seq data using independent component analysis (ICA). ChIP-exo data were used to validate condition-specific target gene binding sites. Based on these data, we do the following: (i) identify the target genes for each TCS; (ii) show how the target genes are transcribed in response to stimulus; and (iii) reveal novel relationships between TCSs, which indicate noncognate inducers for various response regulators, such as BaeR to iron starvation, CpxR to phosphate limitation, and PhoB and ZraR to cell envelope stress. Our understanding of the TRN in E. coli is thus notably expanded. IMPORTANCE E. coli is a common commensal microbe found in the human gut microenvironment; however, some strains cause diseases like diarrhea, urinary tract infections, and meningitis. E. coli’s two-component systems (TCSs) modulate target gene expression, especially related to virulence, pathogenesis, and antimicrobial peptides, in response to environmental stimuli. Thus, it is of utmost importance to understand the transcriptional regulation of TCSs to infer bacterial environmental adaptation and disease pathogenicity. Utilizing a combinatorial approach integrating RNA sequencing (RNA-seq), independent component analysis, chromatin immunoprecipitation coupled with exonuclease treatment (ChIP-exo), and data mining, we suggest five different modes of TCS transcriptional regulation. Our data further highlight noncognate inducers of TCSs, which emphasizes the cross-regulatory nature of TCSs in E. coli and suggests that TCSs may have a role beyond their cognate functionalities. In summary, these results can lead to an understanding of the metabolic capabilities of bacteria and correctly predict complex phenotype under diverse conditions, especially when further incorporated with genome-scale metabolic models.

scholarly journals Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

2020 ◽  
Author(s):  
Timothy J. Durham ◽  
Riza M. Daza ◽  
Louis Gevirtzman ◽  
Darren A. Cusanovich ◽  
William Stafford Noble ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Specific ◽  
C Elegans

AbstractRecently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

Differences in gene expression patterns, revealed by RNA-seq analysis, between various Sanfilippo and Morquio disease subtypes

Gene ◽  
2021 ◽  
pp. 146090
Author(s):  
Karolina Wiśniewska ◽  
Lidia Gaffke ◽  
Karolina Krzelowska ◽  
Grzegorz Węgrzyn ◽  
Karolina Pierzynowska
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Morquio Disease ◽  
Disease Subtypes
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