ReMap Atlas of Regulatory Regions
This track hub represents the ReMap Atlas of regulatory regions which consists in a large scale integrative analysis of all Public and ENCODE ChIP-seq data for transcriptional regulators.
To enable genome-wide identification of regulatory elements we have collected, curated, analysed a total of 3,180 ChIP-seq data sets from Public source (GEO, ArrayExpress) and ENCODE. After applying our quality filters we retained 1,763 data sets from Public source (GEO, ArrayExpress), and retained 1,066 data sets from the ENCODE project.
Those merged analyses covers a total of 485 DNA-binding protein (transcriptional regulators) such as a variety of transcription factors (TFs), transcription co-activators (TCFs) and chromatin-remodeling factors (CRFs) for 80 million peaks.
Below a schematic diagram of the three types of regulatory regions:
- ReMap 2018 Public-only peaks
- ReMap 2018 ENCODE-only peaks
- ReMap 2018 Atlas: called "All" peaks
Individual BED files for specific TFs, or Cells or datasets can be found and downloaded on the ReMap website http://remap.cisreg.eu or http://tagc.univ-mrs.fr/remap/
Display Conventions and Configuration
Each transcription factor follow a specific RGB color.
ChIP-seq peak summits are represented by vertical bars.
A data set is defined as a ChIP-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 labelled with the concatenation of these three pieces of information (e.g. GSE41561.ESR1.MCF-7).
Data sets sources
GEO & ArrayExpress
Public ChIP-seq data sets were extracted from Gene Expression Omnibus (GEO) and ArrayExpress (AE) databases. For GEO, the query '('chip seq' OR 'chipseq' OR 'chip sequencing') AND 'Genome binding/occupancy profiling by high throughput sequencing' AND 'homo sapiens'[organism] AND NOT 'ENCODE'[project]' was used to return a list of all potential data sets to analyse, which were then manually assessed for further analyses. Data sets involving polymerases (i.e. Pol2 and Pol3), and some mutated or fused TFs (e.g. KAP1 N/C terminal mutation, GSE27929) were exckuded.
Available ENCODE ChIP-seq data sets for transcriptional regulators from www.encodeproject.org portal were processed with the our uniform workflow. We retrieved the list of ENCODE data as FASTQ files from the ENCODE portal (https://www.encodeproject.org/) using the following filters: Assay: "ChIP-seq", Organism: "Homo sapiens", Target of assay: "transcription factor", Available data: "fastq" on 2016 June 21st. Metadata information in JSON format and FASTQ files were retrieved using the Python requests module.
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 human genome (GRCh38/hg38 assembly). 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 188.8.131.52) 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.
To assess the quality of public data sets, we computed a score based on the cross-correlation and the FRiP (fraction of reads in peaks) metrics developed by the ENCODE Consortium (http://genome.ucsc.edu/ENCODE/qualityMetrics.html). 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 http://genome.ucsc.edu/ENCODE/qualityMetrics.html. We used the phantompeak tools suite (https://code.google.com/p/phantompeakqualtools/) to compute RSC and NSC.
Full details of our methods can be found in the references below.
The ReMap BED files are available for download at the ReMap website http://remap.cisreg.eu in the download tab.
Papers to cite
Cheneby J., Gheorghe M., Artufel M., Mathelier A., Ballester, B.
ReMap 2018: An updated regulatory regions atlas from an integrative analysis of DNA-binding ChIP-seq experiments.
Nucleic Acids Research (2018) gkx1092 https://doi.org/10.1093/nar/gkx1092.
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 Research (2015) 43 (4): e27.
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