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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).


Compiles the genomic localization. ReMap is an atlas of regulatory regions for over 480 transcriptional regulators (TRs) across about 340 cell types, for a total of more than 80M DNA binding regions. Each experiment included in this resource is manually curated to provide accurate meta-data annotation. It provides DNA-binding locations for each TR, either for each experiment, at cell line or primary cell level. The web interface supplies various options to visualize and browse this catalog.


An integrative genomics method for the prediction of regulatory features and cis-regulatory modules in Human, Mouse, and Fly. ii-cisTarget enables: (i) to detect transcription factor motifs in a set of peaks (e.g. differentially active peaks based on H3K27ac ChIP-seq between 2 conditions) or co-expressed genes, (ii) to detect overrepresented in vivo features (histone modifications, TF ChIP-seq, DHS, Faire) for gene signatures or peaks. These regulatory features help to improve motif discovery and candidate target gene prediction, (iii) to dissect a set of co-expressed genes into direct target genes of different transcription factor motifs or ChIP-seq tracks. Some of the key features of i-cisTarget are: (i) over-represented motifs are predicted in the set of co-expressed genes, using entire intergenic and intronic sequences, (ii) 10 vertebrate species are used for motif scoring in Human and Mouse version, 12 Drosophila species are used in Drosophila version.

LOLA / Location Overlap Analysis

A package for genomic locus overlap enrichment. Roughly analogous to what GSEA does for gene sets, LOLA does for genomic regions, which can be defined however you like, including experiments like ChIP-seq, BS-seq, DNase-seq, etc. LOLA lets you test your genomic ranges of interest against a database of other genomic range sets to identify enrichment of overlap, tying external annotation to your regions of interest. A complete enrichment analysis against a database of thousands of region sets requires just 3 lines of R code and completes in minutes.

GLANET / Genomic Loci AssociatioN and Enrichment Tool

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Offers useful features for performing flexible annotation and enrichment analysis of a given set of fixed or varying length loci. GLANET is an easy-to-run desktop and command line application that utilizes a rich pre-defined annotation library that contains regions defined. This software is suitable for research groups that generate genomic interval data or gene sets through a variety of high throughput experiments and routinely perform enrichment analysis.


A web-based service for evaluating the colocation of genomic features. Users submit genomic regions of interest, for example, a set of locations from a ChIP-seq analysis. ColoWeb compares the submitted regions of interest to the location of other genomic features such as transcription factors and chromatin modifiers. To facilitate comparisons among various genomic features, the output consists of both graphical representations and quantitative measures of the degree of colocalization between user’s genomic regions and selected features. Frequent colocation may indicate a biological relationship.

GenometriCorr / Genometric Correlation

A method for identifying whether two sets of intervals are spatially correlated across a genome, detected as a deviation from a nonuniform distribution of one set of intervals with respect to the other. GenometriCorr performs all analyses on each input, so that a variety of biologically significant relationships are queried. This includes looking for proximity, looking for uniform spacing, looking for increased or decreased overlaps of intervals or points, and presenting the data in a way that a biologist can understand.

REEF / REgionally Enriched Features

Aims at the identification of genomic regions containing features locally clustered. REEF scans the considered genome using a sliding window approach and adopts the False Discovery Rate (FDR) to give a genome-wide significance of observed local enrichment. Its graphical display facilities give several advantages and allow an intuitive and efficient evaluation of analysis results at a glance. It represents an innovative and general tool for detecting the localization of genomic regions of clustered features, thus helping to deepen the knowledge on the architecture and function of genomes.