Comprehensive epigenomic profiling reveals disease-specific chromatin states and enhances therapeutic target discovery in ankylosing spondylitis
Year: 2022
Type: dataset
Abstract: We performed comprehensive epigenetic profiling in immune cell samples from patients with ankylosing spondylitis and healthy controls. Note: Due to Zenodo updating their maximum file limit to 100 files, version 4 (v4) of this archive has been split into 5 compressed archive (tar.gz) files containing all previous and additional files. Version 4 of this archive (updated 03/06/2025) adds 4 files to the archive that were omitted in previous versions which have now been made available. These were: RNA_CD8_raw_counts.txt.gz RNA_CD8_normalised_counts.txt.gz RNA_CD14_raw_counts.txt.gz RNA_CD14_normalised_counts.txt.gz -------------------------------------------------------------------------------------------------------------------------- RNA-seq/ATAC-seq/ChIPm/eRNA: Raw and normalised count data for each gene or epigenetic peak in CD4+ T cells, CD8+ T cells, and CD14+ monocytes from AS patients and healthy controls. File name is in the format: "modality_cell-type_raw/normalised_counts.txt.gz". Table S2 shows which experiments were performed on which samples. This data can be found in the "Raw_Counts.tar.gz" and "Normalised_Counts.tar.gz" archives. -------------------------------------------------------------------------------------------------------------------------- ChromHMM: We used ChromHMM to integrate epigenomic data into a 14-emission state model detailing chromatin functionality in AS patients and healthy controls. ChromHMM filenames are in the format: "ChromHMM_sampleID_celltype_n.bed.gz" where n is the number of states in the ChromHMM emission model. This data can be found in the "ChromHMM_AS_HV.tar.gz" archive. -------------------------------------------------------------------------------------------------------------------------- Capture-C: We performed Capture-C to detect chromosome looping interactions between gene promoters and SNPs associated with ankylosing spondylitis. Capture-C count data are shown in the format: "CaptureC_celltype_gene_Pro/SNP_normalised.unionbdg". We used PeakY to calculate a score for each interaction. PeakY scores are given in the following format: "PeakY_AS/HV_celltype_tier_chrloc_gene_Pro/SNP.txt". gene_Pro and gene_SNP relate to the baitsets given in Table S8. This data can be found in the "CapC_Count_Data.tar.gz" and "PeakY_regions.tar.gz" archives.
Authors: Brown, Andrew, Cohen, Carla, Mielczarek, Olga, Migliorini, Gabriele, Costantino, Felicie, Allcock, Alice, Davidson, Connor, Elliott, Katherine, Fang, Hai, Lledo Lara, Alicia, Martin, Alice, Osgood, Julie, Sanniti, Anna, Scozzafava, Giuseppe, Vecellio, Matteo, Zhang, Ping, Black, Mary, Li, Shuwei, Truong, Dongnhu, Molineros, Julio, Howe, Trevor, Wordsworth, Paul, Bowness, Paul, Knight, Julian
Institutions: Centre for Human Genetics, University of Oxford, Horizon Discovery (United Kingdom), Inserm, Université de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Infection et inflammation, Shanghai Jiao Tong University, Ruijin Hospital, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, Janssen (United States)
Language: English
Subfield: Rheumatology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: Wellcome Trust
Awards: Understanding the genetic basis of common human diseases: core funding for the Wellcome Trust Centre for Human Genetics., A systematic approach to understanding the biology underpinning GWAS hits., Characterising extreme innate immune response phenotypes informative for disease using a functional genomics approach
OpenAlex ID: W4394052979