scholarly journals EpiSAFARI: Sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements

2019 ◽  
Author(s):  
Arif Harmanci ◽  
Akdes Serin Harmanci ◽  
Jyothishmathi Swaminathan ◽  
Vidya Gopalakrishnan
Keyword(s):  
Transcription Factor ◽  
Regulatory Elements ◽  
Transcription Factor Binding ◽  
Computational Method ◽  
Sensitive Detection ◽  
Supplementary Information ◽  
Chip Sequencing ◽  
Factor Binding ◽  
Nucleotide Resolution ◽  
Systematic Identification

Abstract Motivation Functional genomics experiments generate genomewide signal profiles that are dense information sources for annotating the regulatory elements. These profiles measure epigenetic activity at the nucleotide resolution and they exhibit distinctive patterns as they fluctuate along the genome. Most notable of these patterns are the valley patterns that are prevalently observed in assays such as ChIP Sequencing and bisulfite sequencing. The genomic positions of valleys pinpoint locations of cis-regulatory elements such as enhancers and insulators. Systematic identification of the valleys provides novel information for delineating the annotation of regulatory elements. Nevertheless, the valleys are not reported by majority of the analysis pipelines. Results We describe EpiSAFARI, a computational method for sensitive detection of valleys from diverse types of epigenetic profiles. EpiSAFARI employs a novel smoothing method for decreasing noise in signal profiles and accounts for technical factors such as sparse signals, mappability, and nucleotide content. In performance comparisons, EpiSAFARI performs favorably in terms of accuracy. The histone modification valleys detected by EpiSAFARI exhibit high conservation, transcription factor binding, and they are enriched in nascent transcription. In addition, the large clusters of histone valleys are found to be enriched at the promoters of the developmentally associated genes. Differential histone valleys exhibit concordance with differential DNase signal at cell line specific valleys. DNA methylation valleys exhibit elevated conservation and high transcription factor binding. Specifically, we observed enriched binding of transcription factors associated with chromatin structure around methyl-valleys. Availability EpiSAFARI is publicly available at https://github.com/harmancilab/EpiSAFARI Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals EpiSAFARI: Sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements

2019 ◽  
Author(s):  
Arif Harmanci ◽  
Akdes Serin Harmanci ◽  
Jyothishmathi Swaminathan ◽  
Vidya Gopalakrishnan
Keyword(s):  
Bisulfite Sequencing ◽  
Regulatory Elements ◽  
Computational Method ◽  
Sensitive Detection ◽  
Sparse Signals ◽  
High Conservation ◽  
Technical Factors ◽  
Nucleotide Resolution ◽  
Large Clusters ◽  
Systematic Identification

AbstractThe genomewide signal profiles from functional genomics experiments are dense information sources for annotating the regulatory elements. These profiles measure epigenetic activity at the nucleotide resolution and they exhibit distinct patterns along the genome. Most notable of these patterns are the valley patterns that are prevalently observed in many epigenetic assays such as ChIP-Seq and bisulfite sequencing. Valleys mark locations of cis-regulatory elements such as enhancers. Systematic identification of the valleys provides novel information for delineating the annotation of regulatory elements using epigenetic data. Nevertheless, the valleys are generally not reported by analysis pipelines. Here, we describe EpiSAFARI, a computational method for sensitive detection of valleys from diverse types of epigenetic profiles. EpiSAFARI employs a novel smoothing method for decreasing noise in signal profiles and accounts for technical factors such as sparse signals, mappability, and nucleotide content. In performance comparisons, EpiSAFARI performs favorably in terms of accuracy. The histone modification and DNA methylation valleys detected by EpiSAFARI exhibit high conservation, transcription factor binding, and they are enriched in nascent transcription. In addition, the large clusters of histone valleys are found to be enriched at the promoters of the developmentally associated genes.

scholarly journals Base-pair resolution detection of transcription factor binding site by deep deconvolutional network

2018 ◽  
Author(s):  
Sirajul Salekin ◽  
Jianqiu (Michelle) Zhang ◽  
Yufei Huang
Keyword(s):  
Transcription Factor ◽  
Base Pair ◽  
De Novo ◽  
Learning Algorithm ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Deep Learning Algorithm ◽  
Single Base Pair ◽  
Regulatory Analysis ◽  
Nucleotide Resolution

AbstractMotivationTranscription factor (TF) binds to the promoter region of a gene to control gene expression. Identifying precise transcription factor binding sites (TFBS) is essential for understanding the detailed mechanisms of TF mediated gene regulation. However, there is a shortage of computational approach that can deliver single base pair (bp) resolution prediction of TFBS.ResultsIn this paper, we propose DeepSNR, a Deep Learning algorithm for predicting transcription factor binding location at Single Nucleotide Resolution de novo from DNA sequence. DeepSNR adopts a novel deconvolutional network (deconvNet) model and is inspired by the similarity to image segmentation by deconvNet. The proposed deconvNet architecture is constructed on top of ‘Deep-Bind’ and we trained the entire model using TF specific data from ChIP-exonuclease (ChIP-exo) experiments. DeepSNR has been shown to outperform motif search based methods for several evaluation metrics. We have also demonstrated the usefulness of DeepSNR in the regulatory analysis of TFBS as well as in improving the TFBS prediction specificity using ChIP-seq data.AvailabilityDeepSNR is available open source in the GitHub repository (https://github.com/sirajulsalekin/DeepSNR)[email protected]

scholarly journals Predicting the effects of SNPs on transcription factor binding affinity

2019 ◽  
Author(s):  
Sierra S Nishizaki ◽  
Natalie Ng ◽  
Shengcheng Dong ◽  
Robert S Porter ◽  
Cody Morterud ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Affinity ◽  
Association Studies ◽  
Transcription Factor Binding ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Factor Binding ◽  
Genome Wide

Abstract Motivation Genome-wide association studies have revealed that 88% of disease-associated single-nucleotide polymorphisms (SNPs) reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). Results SEMpl estimates transcription factor-binding affinity by observing differences in chromatin immunoprecipitation followed by deep sequencing signal intensity for SNPs within functional transcription factor-binding sites (TFBSs) genome-wide. By cataloging the effects of every possible mutation within the TFBS motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci. Availability and implementation SEMpl is available from https://github.com/Boyle-Lab/SEM_CPP. Supplementary information Supplementary data are available at Bioinformatics online.

Some microsatellites may act as novel polymorphic cis-regulatory elements through transcription factor binding

Gene ◽  
2004 ◽  
Vol 341 ◽  
pp. 149-165 ◽  
Author(s):  
Alvaro Rada Iglesias ◽  
Ellen Kindlund ◽  
Martti Tammi ◽  
Claes Wadelius
Keyword(s):  
Transcription Factor ◽  
Regulatory Elements ◽  
Transcription Factor Binding ◽  
Factor Binding
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