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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).

ChIP-BIT / Bayesian Inference of Target genes using ChIP-seq profiles

A Bayesian approach to reliably detect transcription factor binding sites (TFBSs) and their target genes by jointly modeling binding signal intensities and binding locations of TFBSs. Specifically, a Gaussian mixture model is used to capture both binding and background signals in sample data. As a unique feature of ChIP-BIT, background signals are modeled by a local Gaussian distribution that is accurately estimated from the input data. Extensive simulation studies showed a significantly improved performance of ChIP-BIT in target gene prediction, particularly for detecting weak binding signals at gene promoter regions.


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.

TIP / Target Identification from Profiles

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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.


Based on ChIP-Seq peak lists of transcription factors (TFs) from ENCODE and annotated human lncRNAs from GENCODE, a web-based interface was developed where TF peaks from each ChIP-Seq experiment are crossed with the genomic coordinates of a set of input lncRNAs, to identify which TFs present a statistically significant number of binding sites (peaks) within the regulatory region of the input lncRNA genes. The input can be a set of coexpressed lncRNA genes or any other cluster of lncRNA genes. Users can thus infer which TFs are likely to be common transcription regulators of the set of lncRNAs. In addition, users can retrieve all lncRNAs potentially regulated by a specific TF in a specific cell line of interest or retrieve all TFs that have one or more binding sites in the regulatory region of a given lncRNA in the specific cell line. TF2LncRNA is an efficient and easy-to-use web-based tool.

Target Explorer

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.

TF Target Mapper

Allows users to Transcription Factors (TF) Target Genes. TF Target Mapper in a BLAST search tool that aims to distinguish extract annotated information in chromatin immunoprecipitated sequences and to facilitate large sequence data collections from ChIP experiments analysis. It integrates several functionalities such as pattern recognition for specific transcription factor binding sites or options for cleaning and filtering sequences from vector and repetitive elements.