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• Chracterising cellur pathways underlying CD3/CD28 activation of human CD4+ cells

  We devised an approach to disentangle the TCR and CD28 pathways upon stimulation in naive and memory primary human CD4+ T cells (Tcons) in response to defined stimulatory signals. Sorted Tcons were activated using a titration of anti-CD3 and anti-CD28 in combination as well as individually. As a control we cultured cells in the same conditions but without the stimuli. In total, we defined seven conditions from four individuals for sequencing. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ .

  Dataset EGAD00001006612

• SCRNA10X_SA_CHIP0080_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0080_001 1 samples; filetype=fastq

  Dataset EGAD00001006461

• SCRNA10X_SA_CHIP0080_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0080_002 1 samples; filetype=fastq

  Dataset EGAD00001006462

• SCRNA10X_SA_CHIP0006_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0006_001 1 samples; filetype=fastq

  Dataset EGAD00001006460

• SCRNA10X_SA_CHIP0141_004

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0141_004 1 samples; filetype=fastq

  Dataset EGAD00001006463

• SCRNA10X_SA_CHIP0142_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0142_001 1 samples; filetype=fastq

  Dataset EGAD00001006464

• SCRNA10X_SA_CHIP0142_003

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0142_003 1 samples; filetype=fastq

  Dataset EGAD00001006465

• SCRNA10X_SA_CHIP0146_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0146_002 1 samples; filetype=fastq

  Dataset EGAD00001006466

• SCRNA10X_SA_CHIP0149_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0149_001 1 samples; filetype=fastq

  Dataset EGAD00001006467

• SCRNA10X_SA_CHIP0149_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0149_002 1 samples; filetype=fastq

  Dataset EGAD00001006468

• SCRNA10X_SA_CHIP0150_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0150_001 1 samples; filetype=fastq

  Dataset EGAD00001006469

• SCRNA10X_SA_CHIP0150_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0150_002 1 samples; filetype=fastq

  Dataset EGAD00001006470

• SCRNA10X_SA_CHIP0152_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0152_001 1 samples; filetype=fastq

  Dataset EGAD00001006471

• SCRNA10X_SA_CHIP0152_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0152_002 1 samples; filetype=fastq

  Dataset EGAD00001006472

• SCRNA10X_SA_CHIP0163_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_001 1 samples; filetype=fastq

  Dataset EGAD00001006473

• SCRNA10X_SA_CHIP0163_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_002 1 samples; filetype=fastq

  Dataset EGAD00001006474

• SCRNA10X_SA_CHIP0172_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_001 1 samples; filetype=fastq

  Dataset EGAD00001006475

• SCRNA10X_SA_CHIP0172_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_002 1 samples; filetype=fastq

  Dataset EGAD00001006476

• TENX062

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX062 1 samples; filetype=fastq

  Dataset EGAD00001006477

• SCRNA10X_SA_CHIP0063_000

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0063_000 1 samples; filetype=fastq

  Dataset EGAD00001006478

• TENX064

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX064 1 samples; filetype=fastq

  Dataset EGAD00001006479

• TENX065

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX065 1 samples; filetype=fastq

  Dataset EGAD00001006480

• TENX066

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX066 1 samples; filetype=fastq

  Dataset EGAD00001006481

• TENX068

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX068 1 samples; filetype=fastq

  Dataset EGAD00001006482

• TENX069

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX069 1 samples; filetype=fastq

  Dataset EGAD00001006483

• Oxford Nanopore targeted RNA-based amplicon data of 12 classical HLA genes

  The dataset consists of Oxford Nanopore targeted RNA-based amplicon data of 12 classical HLA genes (HLA-A, -B, -C, -DRA, -DRB1, -DRB3, -DRB4, -DRB5, -DQA1, -DQB1, -DPA1, and DPB1) of 50 healthy individuals. The 12 classical genes were sequenced in two separate gene pools on R9.4 flowcells using MinION sequencer. Per individual, gene pool 1 contains HLA-A, -B, -C, -DRB1, -DRB3, -DRB4, -DRB5, and -DPB1 and gene pool 2 HLA-DRA, -DQA1, -DQB1, and -DPA1. The dataset includes 100 fastq files of Oxford Nanopore 2D reads (50 for gene pool 1 and 50 for gene pool 2).

  Dataset EGAD00001006814

• TS and WGS data

  The dataset contains the targeted sequencing (TS) and the whole genome low pass (WGS) BAM files of the study. For the TS: Samples: TS_XXX There are normal and tumor DNA samples. Each DNA strand has been sequenced independently (PoolA and PoolB). A TruSeq Custom Amplicon panel of 20 genes frequently mutated in ILC and/or ER-positive BC in general was designed using DesignStudio from Illumina: AKT1, ARID1A, CDH1, ERBB2, ERBB3, ESR1, FOXA1, GATA3, IGF1R, JAK2, MAP2K4, MAP3K1, NF1, PIK3CA, PTEN, RB1, RUNX1, STAT3, TBX3, and, TP53. For the WGS: Samples: WGS_XXX There are normal and tumor DNA samples. Samples were sequenced to an average target coverage of 0.5X.

  Dataset EGAD00001006393

• Summary Statistics GWAS SSNS

  Column 1 “rsid”: SNP identifier Column 2 “chromosome”: name of chromosome on which the SNP is located Column 3: “position”: base pair position on the chromosome Column 4 “minor_test_allele”: the base that constitutes the minor allele Column 5 “major_allele”: the base that constitutes the major allele Column 6 “maf”: the frequency of the minor allele, indicated as a fraction of 1 Column 7 “allele_freq_cases”: the minor allele frequency in cases Column 8 “allele_freq_controls”: the minor allele frequency in controls Column 9 “regression_pvalue”: the p-value for the difference in allele frequency between cases and controls Column 10 “odds_ratio”: the odds ratio, as calculated using logistic regression under an additive model with adjustment for the first ten principal components of ancestry

  Dataset EGAD00001008782

• Single cell sequencing of human normal luminal cells

  The dataset includes 12 paired FASTQ files (6 samples) with single cell transcriptome sequencing data of normal breast luminal cells from ducts and TDLUs derived from reduction mammoplasties from three patients. Chromium Single Cell 3’ Reagent Kit v2 or v3 (10x Genomics) were used for processing of cells, whereafter sequencing was performed using the Illumina® NextSeq500/550 High Output Kit v2. Cell Ranger was used for generating FASTQ files and files from different lanes were concatenated prior to uploading the data to EGA. "R1" files include the feature barcodes and UMIs, while "R2" files include the reads.

  Dataset EGAD00001008499

• Whole exome and transcriptome sequencing of BPDCN

  This data set contains whole exome and transcriptome data from 47 case with BPDCN. For exome data, bam files are provided (mapped against GRCh38), for transcriptome raw fastq-files (paired-end data).

  Dataset EGAD00001008692

• Single-cell omics data for COVID-19 patients

  Single-cell RNA-seq, single-cell ATAC-seq, and genotypes used in the analysis for the study "Altered and allele-specific open chromatin landscape reveal epigenetic and genetic regulators of innate immunity in COVID-19". The RNA-seq and ATAC-seq are raw data in FASTQ format while the genotypes are in the VCF format which was filtered and imputed (more details are available in the main text of the study).

  Dataset EGAD00001009331

• Induction Failure in Childhood and Young Adult T-cell Acute Lymphoblastic Leukemia

  We carried out WGS and RNAseq on a cohort of 48 children and young adults with induction failure in T-cell Acute Lymphoblastic Leukemia (T-ALL) to identify genomic drivers of treatment resistance. The study includes WGS for 33 tumour/normal pairs and 15 tumour-only samples. In addition, there is RNAseq data for 37 cases.

  Dataset EGAD00001009058

• Single Cell Genome Sequence for DLP+ library A96210B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient SA1047B on DLP+ library A96210B

  Dataset EGAD00001009475

• Immune Response Targeted Panel + B/TCR Profiling + AbSeq + Sample Tag Multiplexing

  Multiomics data for a cohort of 20 COVID-19 patients (10 patients mild, 3 patients moderate, 4 patients severe, 3 patients critical) obtained from peripheral blood mononuclear cells (PBMCs) from longitudinally sampled at hospital admission, discharge, and 1 month thereafter. The data has been obtained a multiwell-based single-cell technology (BD Rhapsody) that includes the targeted expression of BD Immune Response Targeted Panel, the TCR/BCR profiling and a set of 52 surface proteins. The samples of the different patients at different collection times were labelled using a cell hashing strategy with the BD Single-Cell Multiplexing Kit (6 samples for each run).

  Dataset EGAD00001011319

• Centre Leon Berard - DAC - seq data Neuroendocrine tumors - B Gibert

  Review committee for the neuroendocrine tumor genetic data set uploaded by Benjamin GIBERT (CRCL), Anthony FERRARI (CLB) and Thomas WALTER (HCL)

  Dac EGAC50000000218

• DAC for "Capturing disease severity in LIS1-lissencephaly reveals proteostasis dysregulation in patient-derived forebrain organoids" with Julia Ladewig(Julia.ladewig@zi-mannheim.de), Moritz Mall(m.mall@dkfz-heidelberg.de), and Matteo Gasparotto(matteo.gasparotto@zi-mannheim.de)

  Dac EGAC00001003588

• Walter and Eliza Hall - Aix-Marseille Université

  Data Access Committee for the Liao et al. 2025 study, D4Z4End2End: complete genetic and epigenetic architecture of D4Z4 macrosatellites in FSHD, BAMS and reference cohorts.

  Dac EGAC50000000655

• Division of Cancer Evolution National Cancer Center Research Institute Data Access Committee

  Data Access Committee for data from Division of Cancer Evolution, National Cancer Center Research Institute

  Dac EGAC50000000737

• MD Anderson OPMD DAC

  Datasets and agreement used for the OPMD dataset access of the publication Reconstructing oral cavity tumor evolution through brush biopsy, Springer Nature, DOI: 10.1038/s41598-024-72946-3

  Dac EGAC50000000384

• SiMSen‑Seq

  High resolution amplicon-bases sequencing o screen for PIK3CA hot spot mutations

  Dataset EGAD50000001668

• GEOCODE Cohort

  Transcriptomic profile for whole blood after 24hours of stimulations

  Dataset EGAD50000001320

• Effects of interferon-gamma treatment on human small intestinal organoids.

  Intestinal organoids treated with interferon-gamma 1 ng/mL either for 6h or 18h.

  Dataset EGAD50000000115

• Medulloblastoma dataset

  This dataset contains a tumor + normal DNA sequence data and tumor RNA seq data for a medulloblastoma patient.

  Dataset EGAD50000000384

• gz_bjREP

  DNA for 2979 individuals from Guangzhou was extract from peripheral blood and genotyped by Sequenom, with digit-number working memory, visuospatial working memory, recent long-term memory measured.

  Dataset EGAD00010001528

• GabonDiv_SNP_genotyping

  Genome-wide SNP genotyping data for 1,235 western Africans by Illumina HumanOmniExpress-12 array, used in the EGAS00001002078 study

  Dataset EGAD00010001209

• RNA-Seq Pair End

  RNA analysis of two patients 11 and 15 with WGS done on Illumina HiSeq2000. For research purpose and authorised user only.

  Dataset EGAD00001003429

• Illumina HiSeq 2000

  Three files of patients 20, 23 and 25 with WGS done on Illumina HiSeq 2000. For research purpose and authorised user only.

  Dataset EGAD00001003438

• Reference epigenome IPS02_N_NPC_WGBS data generated from KEP study

  A IPS02_N_NPC_WGBS paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003474

• Reference epigenome IPS03_N_ENeuron_WGBS data generated from KEP study

  A IPS03_N_ENeuron_WGBS paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003475

• Reference epigenome CKD24_C_Podo_mRNA-Seq data generated from KEP study

  A CKD24_C_Podo_mRNA-Seq paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003482