ReMap ChIP-seq Track Settings
ReMap Atlas of Regulatory Regions   (All Expression and Regulation tracks)

Display mode:       Reset to defaults

Filter items in 'Transcriptional regulators' field: using

Filter items in 'Biotypes (cell lines, tissues...)' field: using

Filter items by: (select multiple categories and items - help)
Biotypes (cell lines, tissues...) Transcriptional regulators
Label: Name of item.    Transcriptionnal regulators    biotype, cellular type, tissue...   

Display data as a density graph:
List subtracks: only selected/visible    all  
 ReMap density  ReMap density   Schema 
 ReMap ChIP-seq  ReMap Atlas of Regulatory Regions   Schema 

new Note: Released Apr. 26, 2022


This track represents the ReMap Atlas of regulatory regions, which consists of a large-scale integrative analysis of all Public ChIP-seq data for transcriptional regulators from GEO, ArrayExpress, and ENCODE.

Below is a schematic diagram of the types of regulatory regions:

  • ReMap 2022 Atlas (all peaks for each analyzed data set)
  • ReMap 2022 Non-redundant peaks (merged similar target)
  • ReMap 2022 Cis Regulatory Modules

Display Conventions and Configuration

  • Each transcription factor follows a specific RGB color.
  • ChIP-seq peak summits are represented by vertical bars.
  • Hsap: A data set is defined as a ChIP/Exo-seq experiment in a given GEO/ArrayExpress/ENCODE series (e.g. GSE41561), for a given TF (e.g. ESR1), in a particular biological condition (e.g. MCF-7).
    Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE41561.ESR1.MCF-7).
  • Atha: The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE94486), for a given target (e.g. ARR1), in a particular biological condition (i.e. ecotype, tissue type, experimental conditions; e.g. Col-0_seedling_3d-6BA-4h).
    Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE94486.ARR1.Col-0_seedling_3d-6BA-4h).


This release of ReMap (2022) presents the analysis of 5,505 quality controlled mouse ChIP-seq (n=7,317 before QCs) from public sources (GEO & ENCODE). Those ChIP-seq data sets have been mapped to the GRCm38/mm10 mouse assembly. The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE122715), for a given TF (e.g. USF1), in a particular biological condition (i.e. cell line, tissue type, disease state, or experimental conditions; e.g. mESC). Data sets were labeled by concatenating these three pieces of information, such as GSE122715.USF1.mESC.

Those merged analyses cover a total of 656 DNA-binding proteins (transcriptional regulators) such as a variety of transcription factors (TFs), transcription co-activators (TCFs), and chromatin-remodeling factors (CRFs) for 123 million peaks.


Available ENCODE ChIP-seq data sets for transcriptional regulators from the ENCODE portal were processed with the standardized ReMap pipeline. The list of ENCODE data was retrieved as FASTQ files from the ENCODE portal using filters. Metadata information in JSON format and FASTQ files were retrieved using the Python requests module.

ChIP-seq processing

Both Public and ENCODE data were processed similarly. Bowtie 2 (PMC3322381) (version 2.2.9) with options -end-to-end -sensitive was used to align all reads on the genome. Biological and technical replicates for each unique combination of GSE/TF/Cell type or Biological condition were used for peak calling. TFBS were identified using MACS2 peak-calling tool (PMC3120977) (version in order to follow ENCODE ChIP-seq guidelines, with stringent thresholds (MACS2 default thresholds, p-value: 1e-5). An input data set was used when available.

Quality assessment

To assess the quality of public data sets, a score was computed based on the cross-correlation and the FRiP (fraction of reads in peaks) metrics developed by the ENCODE Consortium ( Two thresholds were defined for each of the two cross-correlation ratios (NSC, normalized strand coefficient: 1.05 and 1.10; RSC, relative strand coefficient: 0.8 and 1.0). Detailed descriptions of the ENCODE quality coefficients can be found at The phantompeak tools suite was used ( to compute RSC and NSC.

Please refer to the ReMap 2022, 2020, and 2018 publications for more details (citation below).

Data Access

ReMap Atlas of regulatory regions data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser's REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large.

Individual BED files for specific TFs, cells/biotypes, or data sets can be found and downloaded on the ReMap website.


Chèneby J, Gheorghe M, Artufel M, Mathelier A, Ballester B. ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP- seq experiments. Nucleic Acids Res. 2018 Jan 4;46(D1):D267-D275. PMID: 29126285; PMC: PMC5753247

Chèneby J, Ménétrier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, Bergon A, Lopez F, Ballester B. ReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis DNA-binding sequencing experiments. Nucleic Acids Res. 2020 Jan 8;48(D1):D180-D188. PMID: 31665499; PMC: PMC7145625

Griffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B. Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape. Nucleic Acids Res. 2015 Feb 27;43(4):e27. PMID: 25477382; PMC: PMC4344487

Hammal F, de Langen P, Bergon A, Lopez F, Ballester B. ReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments. Nucleic Acids Res. 2022 Jan 7;50(D1):D316-D325. PMID: 34751401; PMC: PMC8728178