Target gene identification software tools | ChIP sequencing data analysis
Chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) has great potential for elucidating transcriptional networks, by measuring genome-wide binding of transcription factors (TFs) at high resolution. Despite the precision of these experiments, identification of genes directly regulated by a TF (target genes) is not trivial.
Provides a range of functionalities for ChIP data analyses. CisGenome allows visualization, data normalization, peak detection, false discovery rate (FDR) computation, gene-peak association, and sequence and motif analysis. The software can handle data from two types of designs common in ChIP-seq experiments. The basic functionalities of CisGenome can be used in combination to address several different biological questions.
Predicts functions of cis-regulatory regions. Many coding genes are well annotated with their biological functions. Non-coding regions typically lack such annotation. GREAT assigns biological meaning to a set of non-coding genomic regions by analyzing the annotations of the nearby genes. Thus, it is particularly useful in studying cis functions of sets of non-coding genomic regions. Cis-regulatory regions can be identified via both experimental methods (e.g. ChIP-seq) and by computational methods (e.g. comparative genomics).
Allows users to identify weak binding events. ChIP-BIT is a program able to detect both strong and weak binding events at gene promoter regions. The second version, ChIP-BIT2 expands the capability of the first version for identifying binding events of transcription factors (TFs) or histone markers (HMs) at any regions including gene promoter, enhancer or simply the whole genome.
Predicts transcription factor function. PRISM combines genome-wide conserved binding site prediction with transcription factor and binding site function prediction. The software offers an interface to explore our predictions from the perspective of transcription factors, biological roles, target genes, or target binding sites/regions. It integrates with GREAT and the UCSC Genome Browser.
Identifies Transcription Factor (TF) target genes based on ChIP-seq or ChIP-chip data. The TIP model provides a gene list ranked by the regulatory potential by the TF and gives a confidence score, which allows the user to select a subset of genes for creation of testable hypothesis and further experimental investigation. With ever-increasing genomic occupation data, the model provides a powerful tool for understanding gene regulation.
Predicts important transcription factors (TFs) and their binding sites responsible for dynamic processes. DynaMO uses global chromatin profiling data from a dynamic process and TF binding motifs as input. The software can also be used to extract temporal characteristics of the dynamic process via curve fitting using data from time-course experiments and evaluate dynamic changes in the detected binding sites.
Allows analysis of factor binding and differential expression in mammalian genomes. BETA has three main functions: (i) to predict whether a factor has activating or repressive function; (ii) to infer the factor’s target genes and (iii) to identify the binding motif of the factor and its collaborators, which might modulate the factor’s activating or repressive function.
Predicts genome-wide co-binding between biological regulators such as transcription factors (TFs) and DNaseI data. CCAT combines binding sites matches to TF positional weight matrices (PWMs) with DNaseI accessibility experiments to obtain genome-wide TF binding site predictions that are comparable in quality with ChIP experiments results. It also allows users to discover the dynamic usage of combinatorial motif pairs in different stages.
Predicts the regulatory targets of a transcription factor (TF) using the correlation between a histone mark at the TF’s bound sites and the expression of each gene across a panel of tissues. CISMAPPER provides a method that is more accurate than simple distance-based methods, but that places a minimum burden on the user to provide auxiliary data. It uses only data for a single histone modification and gene expression across a small panel of tissues. The use of CISMAPPER need not be restricted to the analysis of TF ChIP-seq data.
Predicts biological contexts in which a transcription factor (TF) and its target genes are functionally active. ChIP-PED is a software which allows users to extend the scope of their ChIPx data to possibly novel biological systems without performing additional costly and time-consuming ChIPx experiments. Given a TF and its activated and repressed target genes defined using ChIPx and gene expression data in one or more biological contexts, the software searches for other contexts in which the TF is likely to be functionally active.
Provides a probabilistic model for de novo DNA motif pair discovery on paired sequences. MotifHyades is more accurate than the previous ad hoc computational pipeline for DNA motif pair discovery. In particular, the de novo nature can enable to discover novel motif pairs on the rapidly growing chromatin interaction and genome segmentation datasets. In addition, MotifHyades was applied to discover thousands of DNA motif pairs with higher gold standard motif matching ratio, higher DNase accessibility and higher evolutionary conservation than the previous ones in the human K562 cell line.
Predicts transcription factors whose genomic binding profile correlates with query cis-regulatory regions to regulate gene expression in human or mouse genomes. BART is based on a curated union DNaseI hypersensitive sites (UDHS) used as a repertoire of all cis-regulatory regions in the genome. This software offers functional interpretations to differential gene expression analysis and can be applied to validate knock-down or knock-out experiments.
A pipeline for the identification of transcription factor target genes. TargetOrtho uses experimentally derived consensus-binding sites together with an alignment-free assignment of conservation. TargetOrtho offers a complementary approach to existing software that focuses mainly on de novo motif discovery by instead beginning with an experimentally validated motif and searching for conserved regulatory target genes.
Adjusts how expression data is provided to the network algorithm. ExRANGES prioritizes targets differently than traditional expression and improves the ability of these networks to accurately predict known regulator targets. It improved the ability to correctly identify targets of transcription factors in large data sets in four different model systems: mouse, human, Arabidopsis, and yeast. Finally, the performance of ExRANGES was examined on a small data set from field-grown Oryza sativa and found that it also improved the ability to identify known targets even with a limited data set.
Generalizes transcription factor (TF) dimer prediction. TACO is applicable to regulatory element annotations from any source, rather than being restricted to DNase-seq datasets. It was applied to 127 replicates from 94 ChIP-seq experiments in K562 cells. This tool can be useful for regulatory annotations from assay, since the algorithm allows a great deal of flexibility in data type and mode of analysis.
Supports target gene identification in seven species, ranging from yeast to human. To facilitate investigating the quality of ChIP-seq/ChIP-chip data, the iTAR web server generates the chart of the characteristic binding profiles and the density plot of normalized regulatory scores. The web server is a useful tool in identifying Transcription Factor (TF) target genes from ChIP-seq/ChIP-chip data and discovering biological insights.
Aims to improve identification of transcription factor (TF) target gene from ChIP-seq and ChIP-chip data using publicly available gene expression profiles. ChIPXpress is an R package that uses regulatory information from the diverse cell types, tissues, and diseases in publicly available gene expression data (PED) to enhance functional TF target gene prediction from ChIPx data. The software was developed using two large compendiums of human and mouse gene expression profiles collected from GEO.
Allows users to create customized libraries of transcription factor (TF) binding site matrices based on user-defined sets of training sequences. Target Explorer is an online application including a search function for clusters of binding sites for specified sets of TFs. It also assists users in the extraction of annotation for potential target genes regulated by a specified set of TFs. This tool is specifically designed for the well-annotated D. melanogaster genome.
Identifies the correct motif from a set of co-expressed genes and predicts target genes of individual Transcription Factors (TFs). cisTargetX uses statistical correlations between co-expressed gene sets and genome-wide target prioritizations on the basis of rankings of conserved motif cluster predictions. It can determine whether a set of candidate genes, for example a mixture of direct and indirect target genes, is enriched for direct targets of a certain TF or combination of TFs.
Provides several methods for predicting transcription factor (TF) target genes using ChIP-seq data. TargetCaller is an R package that can facilitate the parallel testing of the various methods on any given ChIP-seq dataset.