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A sequence-based computational method to predict the effect of regulatory variation, using a classifier (gkm-SVM) that encodes cell type-specific regulatory sequence vocabularies. The induced change in the gkm-SVM score, deltaSVM, quantifies the effect of variants. We show that deltaSVM accurately predicts the impact of SNPs on DNase I sensitivity in their native genomic contexts and accurately predicts the results of dense mutagenesis of several enhancers in reporter assays. deltaSVM provides a powerful computational approach to systematically identify functional regulatory variants.
TRAP / Transcription factor Affinity Prediction
Determines the total affinity of a sequence for a given transcription factor, thus removing the need for a threshold value. TRAP ranks all promoter sequences of a genome on the basis of their overall affinity for that factor to proceed. It can serve to estimate the most enriched factor into a given sequence, the sequences with the highest affinity for a factor of interest, or the binding sites of a factor affected by the given single nucleotide polymorphisms (SNPs).
A tool for exploring annotations of the noncoding genome at variants on haplotype blocks, such as candidate regulatory SNPs at disease-associated loci. Using LD information from the 1000 Genomes Project, linked SNPs and small indels can be visualized along with chromatin state and protein binding annotation from the Roadmap Epigenomics and ENCODE projects, sequence conservation across mammals, the effect of SNPs on regulatory motifs, and the effect of SNPs on expression from eQTL studies. HaploReg is designed for researchers developing mechanistic hypotheses of the impact of non-coding variants on clinical phenotypes and normal variation.
An automated framework for the statistical analysis and interpretation of the functional impact of SNP sets using regulatory datasets from the ENCODE, Roadmap Epigenomics and other projects. GenomeRunner prioritizes regulatory datasets most significantly enriched in SNP sets and visualizes the most significant enrichments, thus suggesting regulatory mechanisms that may be altered by them. In addition to prioritizing SNP set-specific regulatory enrichments (functional impact), GenomeRunner implements three novel approaches: 1) regulatory similarity analysis, aimed at identifying groups of SNP sets having similar functional impact; 2) differential regulatory analysis, developed to identify functional impact specific for a group of SNP sets; and 3) cell type regulatory enrichment analysis, designed to identify cell type specificity of the functional impact.
A user friendly tool for annotating cancer mutations in cis-regulatory regions of DNA. OncoCis integrates publicly available datasets representing a wide range of cancer types from genome-wide chromatin accessibility and histone modification profiles obtained from ENCODE and the Human Epigenome Atlas to identify mutations that occur within potential cis-regulatory regions. The use of cell type-specific information and gene expression can significantly reduce the number of candidate cis-regulatory mutations compared with existing tools designed for the annotation of cis-regulatory SNPs.
Identifies and quantifies footprints of the effects of noncoding variants on transcription factor (TF) binding. Sasquatch provides a relatively simple and yet informative approach, requiring only a single DNase-seq data set from the appropriate cell type. It can use data from any genotype to assess variants that are appropriate to that cell type. It can employ publicly available data of any reasonable depth and quality, generated by any of the existing DNase-seq protocols, including low-input DNase-seq protocols.
BayesPI-BAR / Bayesian method for protein–DNA interaction with binding affinity ranking
Quantifies the effect of sequence variations on protein binding. BayesPI-BAR uses biophysical modeling of protein–DNA interactions to predict single nucleotide polymorphisms (SNPs) that cause significant changes in the binding affinity of a regulatory region for transcription factors (TFs). The method includes two new parameters (TF chemical potentials or protein concentrations and direct TF binding targets) that are neglected by previous methods. BayesPI-BAR is a useful tool for detecting functional driver mutation in the noncoding part of the genome and exploring massive genome-wide sequence data that are constantly generated by large consortia, such as the International Cancer Genome Consortium and the Cancer Genome Atlas.
An R package for predicting the disruptiveness of single nucleotide polymorphisms on transcription factor binding sites. motifbreakR allows the biologist to judge whether the sequence surrounding a polymorphism or mutation is a good match, and how much information is gained or lost in one allele of the polymorphism or mutation relative to the other. MotifbreakR is flexible, giving a choice of algorithms for interrogation of genomes with motifs from many public sources that users can choose from. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design.
GERV / Generative Evaluation of Regulatory Variants
A computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer-based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor's canonical motif and associated co-factor motifs. We show that GERV outperforms existing methods in predicting single-nucleotide polymorphisms associated with allele-specific binding. GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis.
atSNP / affinity testing for regulatory SNPs
A computationally efficient R package for identifying rSNPs in silico. atSNP implements an importance sampling algorithm coupled with a first-order Markov model for the background nucleotide sequences to test the significance of affinity scores and SNP-driven changes in these scores. Application of atSNP with >20K SNPs indicates that atSNP is the only available tool for such a large-scale task. atSNP provides user-friendly output in the form of both tables and composite logo plots for visualizing SNP-motif interactions.
An informatics strategy that integrates several established bioinformatics tools, for prioritizing regulatory SNPs, i.e. the SNPs in the promoter regions that potentially affect phenotype through changing transcription of downstream genes. Comparing to existing tools, regSNPs has two distinct features. It considers degenerative features of binding motifs by calculating the differences on the binding affinity caused by the candidate variants and integrates potential phenotypic effects of various transcription factors.
A user-friendly Bioconductor R package to investigate, quantify and visualise the local epigenetic neighbourhood of a set of SNPs in terms of chromatin marks or transcription factor binding sites using data from NGS experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize, quantify, and visualize read counts or enrichment over input in the local neighbourhood of regions of interest (e.g. SNPs) across multiple samples (e.g. individuals). Functionalities in SNPhood are largely complementary to and extend current tools used for ChIP-Seq data analysis. We believe that it will be a helpful tool to generate new biological hypotheses by integrating molecular-phenotype data in an unbiased and position-specific manner.
MPRAnator / Massively Parallel Reporter Assays
Facilitates rapid design of massively parallel reporter assays (MPRA) experiments. MPRAnator allows systematic design of MPRA experiments for the investigation of the effects of single nucleotide polymorphisms (SNPs) and motifs on regulatory sequences. MPRAnator provides support for four different types of investigations. The MPRA Motif design tool can be used to systematically generate synthetic sequences with single motifs or combinations of motifs placed at preselected positions. The MPRA SNP design tool can be used to examine the regulatory effects of single or combinations of SNPs for every provided sequence. The PWM Seq-Gen tool performs probabilistic realizations of pulse width modulations (PWMs) or generates all the corresponding k-mer motifs exceeding a probability threshold. The Transmutation tool allows for the design of different types of negative controls for MPRA experiments.
HPRS pipeline / Human Projection of Regulatory Sequences pipeline
Predicts equivalent information in other mammalian species. HPRS pipeline utilizes genomic and epigenomic data for human genome to realize these predictions. This tool can be used in the case of possible improvement of pre/post genome wide association studies (GWAS) analysis and genomic prediction models for example. This method uses: (1) the conservation of regulatory elements at the DNA sequence and genome organizational levels to map these elements to other mammalian species; (2) species-specific data to filter these mapped sequences, to predict a set of high confidence regulatory regions.
PERFECTOS-APE / Predicting Regulatory Functional Effect by Approximate P-value Estimation
Identifies transcription factors whose binding sites can be significantly affected by given nucleotide substitutions. PERFECTOS-APE is the software to PrEdict Regulatory Functional Effect of SNPs by Approximate P-value Estimation. It supports both classic Position Weight Matrices (PWMs) under the position independency assumption, and dinucleotide PWMs accounting for the dinucleotide composition and correlations between nucleotides in adjacent positions within binding sites.
Identifies cis-regulatory mutations in a cancer sample, but it can also to filter, annotate and prioritize non coding-variants based on their putative effect on the underlying 'personal' gene regulatory network. The concept behind µ-cisTarget is to simultaneously identify “personalized” candidate master regulators for a given cancer sample, based on the gene expression profile of the sample. It concerns to priorities single nucleotide variants (SNVs) and insertions/deletions (INDELs) in the non-coding genome of the sample by their likelihood to generate de novo binding sites for any of these master regulators
Brings together single nucleotide polymorphism (SNP) information from numerous sources to provide a comprehensive SNP selection, annotation and prioritization system for design and analysis of genotyping projects. SNPLogic integrates information about the genetic context of SNPs (gene, chromosomal region, functional location, haplotypes tags and overlap with transcription factor binding sites, splicing sites, miRNAs and evolutionarily conserved regions), genotypic data (allele frequencies per population and validation method), coverage of commercial arrays (ParAllele, Affymetrix and Illumina), functional predictions (modeled on structure and sequence) and connections or established associations (biological pathways, gene ontology terms and OMIM disease terms). The SNPLogic web interface facilitates construction and annotation of user-defined SNP lists that can be saved, shared and exported. Thus, SNPLogic can be used to identify and prioritize candidate SNPs, assess custom and commercial arrays panels and annotate new SNP data with publicly available information.
A powerful tool for the large-scale analysis of regulatory SNPs. Its main purpose is to evaluate the potential effect of a SNP on predicted transcription factor binding sites, by measuring the change in the predictive score of the transcription factor binding site (TFBS) as a consequence of the allele change. rSNP-MAPPER is optimized for large scale work, allowing for the efficient annotation of thousands of SNPs, and is designed to assist in the genome-wide investigation of transcriptional regulatory networks, prioritizing potential rSNPs for subsequent experimental validation.
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An automatically generated comprehensive database of SNPs affecting one or several TFBSs from the JASPAR database. SNP2TFBS consists of a collection of text files providing specific annotations for human single nucleotide polymorphisms (SNPs), namely whether they are predicted to abolish, create or change the affinity of one or several transcription factor binding sites (TFBSs). A SNP's effect on transcription factor (TF) binding is estimated based on a position weight matrix (PWM) model for the binding specificity of the corresponding factor. These data files are regenerated at regular intervals by an automatic procedure that takes as input a reference genome, a comprehensive SNP catalogue and a collection of PWMs. SNP2TFBS is accessible over a web interface, enabling users to view the information, to extract SNPs based on various search criteria, to annotate uploaded sets of SNPs or to display statistics about the frequencies of binding sites affected by selected SNPs.
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