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• Normal sample for patient SA229

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA229

  Dataset EGAD00001009552

• Normal sample for patient SA230

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA230

  Dataset EGAD00001009553

• Normal sample for patient SA231

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA231

  Dataset EGAD00001009554

• Normal sample for patient SA232

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA232

  Dataset EGAD00001009555

• Normal sample for patient SA233

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA233

  Dataset EGAD00001009556

• Normal sample for patient SA234

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA234

  Dataset EGAD00001009557

• Normal sample for patient SA237

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA237

  Dataset EGAD00001009558

• Normal sample for patient SA271

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA271

  Dataset EGAD00001009559

• Normal sample for patient SA277

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA277

  Dataset EGAD00001009560

• Normal sample for patient SA278

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA278

  Dataset EGAD00001009561

• Normal sample for patient SA997

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA997

  Dataset EGAD00001009573

• Normal sample for patient SA998

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA998

  Dataset EGAD00001009574

• Tumour sample for patient SA416

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA416

  Dataset EGAD00001009575

• Normal sample for patient SA597

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA597

  Dataset EGAD00001009572

• Normal sample for patient SA095

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA095

  Dataset EGAD00001009600

• Tumour sample for patient SA400

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA400

  Dataset EGAD00001009576

• Tumour sample for patient SA290

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA290

  Dataset EGAD00001009577

• Tumour sample for patient SA288

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA288

  Dataset EGAD00001009578

• Tumour sample for patient SA299

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA299

  Dataset EGAD00001009579

• Tumour sample for patient SA095

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA095

  Dataset EGAD00001009580

• Tumour sample for patient SA415

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA415

  Dataset EGAD00001009581

• Normal sample for patient SA718

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA718

  Dataset EGAD00001009582

• Normal sample for patient SA720

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA720

  Dataset EGAD00001009583

• Tumour sample for patient SA718

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA718

  Dataset EGAD00001009584

• Tumour sample for patient SA720

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA720

  Dataset EGAD00001009585

• Normal sample for patient SA1017

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1017

  Dataset EGAD00001009586

• Normal sample for patient SA1026

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009587

• Normal sample for patient SA1027

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1027

  Dataset EGAD00001009588

• Normal sample for patient SA1028

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009589

• Normal sample for patient SA1040

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1040

  Dataset EGAD00001009590

• Normal sample for patient SA1064

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1064

  Dataset EGAD00001009591

• Normal sample for patient SA1065

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1065

  Dataset EGAD00001009592

• Normal sample for patient SA1069

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1069

  Dataset EGAD00001009593

• Normal sample for patient SA1073

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1073

  Dataset EGAD00001009594

• Normal sample for patient SA1074

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1074

  Dataset EGAD00001009595

• Normal sample for patient SA576

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009596

• Normal sample for patient SA610

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA610

  Dataset EGAD00001009597

• Normal sample for patient SA992

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA992

  Dataset EGAD00001009598

• Normal sample for patient SA994

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA994

  Dataset EGAD00001009599

• Normal sample for patient SA288

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA288

  Dataset EGAD00001009601

• Normal sample for patient SA299

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA299

  Dataset EGAD00001009602

• Normal sample for patient SA415

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA415

  Dataset EGAD00001009603

• Normal sample for patient SA666

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA666

  Dataset EGAD00001009604

• Tumour sample for patient SA1065

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1065

  Dataset EGAD00001009605

• Tumour sample for patient SA1064

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1064

  Dataset EGAD00001009606

• Tumour sample for patient SA1026

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009607

• Tumour sample for patient SA1069

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1069

  Dataset EGAD00001009608

• Tumour sample for patient SA1017

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1017

  Dataset EGAD00001009609

• Tumour sample for patient SA1027

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1027

  Dataset EGAD00001009610

• Tumour sample for patient SA1073

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1073

  Dataset EGAD00001009611

• Tumour sample for patient SA1074

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1074

  Dataset EGAD00001009612

• Tumour sample for patient SA576

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009613

• Tumour sample for patient SA1028

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009614

• Tumour sample for patient SA1040

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1040

  Dataset EGAD00001009615

• Tumour sample for patient SA992

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA992

  Dataset EGAD00001009616

• Tumour sample for patient SA610

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA610

  Dataset EGAD00001009617

• Normal sample for patient SA416

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA416

  Dataset EGAD00001009618

• Normal sample for patient SA1070

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1070

  Dataset EGAD00001009619

• Tumour sample for patient SA1070

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1070

  Dataset EGAD00001009620

• Tumour sample for patient SA997

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA997

  Dataset EGAD00001009621

• Cannabis impacts female fertility as evidenced by an in vitro investigation and a case-control study

  Single immature human oocytes were cultured in vitro (24h) either under control, THC1 () or THC2 (100 ng/mL Δ9-THC, 50 ng/mL 11-OH-THC, 200 ng/mL 11-COOH-THC) conditions. Oocytes were collected and subjected to SmartSeq v4 cDNA conversion, library prep (Nextera XT)), and sequencing (NovaSeq S4). The goal of this study was to determine the impact of THC on the human oocyte transcriptome.

  Study EGAS50000001052

• Effect of inflammation on human hematopoietic stem cells in a xenograft model

  Transcriptional heterogeneity in cord blood HSCs was investigated by 10X single-cell Multiome (RNA+ATAC). To investigate the effect of inflammatory stress on human HSC, 10X single-cell Multiome profiling of cells from xenografted mice recovered from repeated inflammatory challenge was performed. To investigate transmission of inflammatory stress-related molecular programs from HSC to their progeny, 10X single-cell RNA sequencing was performed on committed progenitor and myeloid cells from the same mice.

  Study EGAS50000001624

• UK_RCC_GWAS

  Renal cell carcinoma (RCC) cases comprised adult patients with histologically proven RCC were collected through two sources within the UK. First, 856 cases from SORCE, a MRC collection of surgically treated RCC cases ascertained through UK clinical oncology centres. Second, 189 RCC cases collected through the ICR and Royal Marsden NHS Hospitals Trust. Cases included 590 clear cell carcinomas (CCCs), 42 papillary carcinomas (PCs), 33 chromophobe carcinomas (CCs) and 19 mixed or other histological subtypes. DNA was extracted from EDTA-venous blood samples using the conventional methods and quantified using PicoGreen (Invitrogen). Cases were genotyped using the Human OmniExpress-12 BeadChip according to the manufacturer's recommendations (Illumina Inc, San Diego, CA, USA). After strict QC, 944 cases were retained. Data provided in plink format. Controls used were data from the Wellcome Trust Case Control Consortium 2 (WTCCC2) 1958 birth cohort and the UK Blood Service Control Group (available as EGAS00000000028). 

  Dataset EGAD00010002310

• Transcriptome Sequencing of Cancer Cell Lines

  In this study we will sequence the transcriptome of Verified Cancer Cell lines. This will be married up to whole exome and whole genome sequencing data to establish a full catalog of the variations and mutations found.

  Dataset EGAD00001000359

• Matched Pair Cell Line Tumour RNAseq

  In this study we will sequence the transcriptome of Verified Matched Pair Cancer Cell line tumour samples. This will be married up to whole exome and whole genome sequencing data to establish a full catalog of the variations and mutations found.

  Dataset EGAD00001000630

• The Transcriptional Landscape of SHH Medulloblastoma

  To further understand the biology of Sonic hedgehog medulloblastoma and its molecular subtypes, we studied 250 human Shh-MB using strand-specific RNA sequencing. We identified novel alterations within the cAMP dependent pathway and found that 18% of tumors have genetic events that directly target the abundance and/or stability of MYCN. We also discovered an extensive network of fusions in focally amplified regions, and several loss-of-function fusions in tumor suppressor genes PTCH, SUFU and NCOR1. Molecular convergence on a core of specific genes by nucleotide variants, copy number aberrations, and gene fusions highlights key roles of specific pathways in the pathogenesis of Sonic hedgehog medulloblastoma.

  Dataset EGAD00001006305

• RNA-sequencing of a representative cohort of 209 AML cases

  Sample libraries were prepped using 500 ng of input RNA according to the KAPA RNA HyperPrep Kit with RiboErase (HMR) (Roche) using Unique Dual Index adapters (Integrated DNA Technologies, Inc.). Amplified sample libraries were paired-end sequenced (2x100 bp) on the Novaseq 6000 platform (Illumina) and aligned against the human genome (hg19) using STAR v2.5.4b2.

  Dataset EGAD00001007581

• Single cell transcriptomic data of human gut macrophages

  Cellular suspensions (∼15000 cells, with expected recovery of ∼7500 cells) of sorted CD45+ HLA-DR+ CD14+ macrophages from colonic mucosa and muscularis propria were loaded on the 10X Chromium Controller instrument (10X Genomics) according to the manufacturer’s protocol using the 10X GEMCode proprietary technology. All samples from individual patients were loaded in one batch. The Chromium Single Cell 3´ v2 Reagent kit (10X Genomics) was used to generate the cDNA and prepare the libraries, according to the manufacturer’s protocol. The libraries were then equimolarly pooled and sequenced on an Illumina NextSeq500 using HighOutput flow cells v2.5. A coverage of 400M reads per sample was targeted, in order to obtain 50 000 reads per cell. The raw data were then demultiplexed and processed with the Cell Ranger software (10X Genomics) v2.1.1.

  Dataset EGAD00001007765

• Disease specific alterations in the olfactory mucosa of patients with Alzheimer’s disease

  The sample AD_Library_1, AD_Library_2 and Control_Library were run on a Chromium Chip B with the Chromium Single Cell 3′ Library & Gel Bead Kit v3 kit (10x Genomics, CA, USA) . The 3’ gene expression libraries were sequenced at an approximate depth of 50,000 reads per cell using the NovaSeq 6000 S1 (Illumina, San Diego, CA, USA) flow cells. Cell Ranger v.3.0.2 was used to analyze the raw base call files. FASTQ files and raw gene-barcode matrices were generated and aligned human genome GRCh37 (hg19). The samples were integrated in R v.4.0.3 and generated Seurat objects, two related to AD samples and one to control samples, were analyzed using the Seurat package v.4.0.3 to perform downstream analysis, clustering of the cells and differential expression.

  Dataset EGAD00001008560

• SPECIAL: scRNA-seq

  Tumour biopsies were collected from twenty-three patients (46 samples) (of which 20 were matched) with locally advanced or metastatic melanoma (stage IIIB – stage IV). Library construction was done either with the Chromium Single Cell 3ʹ GEM, Library & Gel Bead Kit v3 (n = 16; 10x genomics, Cat#1000092) or the Chromium Single Cell A Chip Kit and 5’ Library & Gel Bead Kit (10x genomics, Cat#1000014). All libraries were sequenced on Illumina NextSeq, HiSeq4000 or NovaSeq6000 until sufficient saturation was reached (60% on average). The reference genome used in this study was GRCh38.

  Dataset EGAD00001009291

• RNAseq data

  RNAseq FASTq files from 797 tumors from AVANT. Sequencing libraries were generated with the TruSeq Stranded Total RNA kit (Illumina) following ribosomal RNA (rRNA) depletion with the Ribo-Zero Gold kit (Illumina). The libraries were sequenced on the HiSeq4000 (Illumina) with a sequencing protocol of 75 bp paired-end sequencing. Note: 10 samples used in the original publication were excluded from this upload due to regulations from the Human Genetics Resources Administration of China (HGRAC).

  Dataset EGAD00001009728

• Shallow whole genome sequencing

  This dataset contains a collection of tumour samples from high grade serous ovarian carcinoma patients with recurrent disease collected near the point of diagnosis as well as tumour samples collected after patient relapse upon or some time after study entry. The majority of diagnosis samples were preserved in neutral buffered formalin whereas the majority of post-relapse samples were preserved in universal molecular fixative (UMFIX, Sakura Finetek USA, Inc). DNA was extracted and whole genome sequencing libraries were prepared either using the TruSeq DNA Nano kit (Illumina) or the ThruPLEX DNA-Seq Kit (Takara Bio), which were respectively sequenced at low depth (~0.1-0.5X) on illumina HiSeq 2500 and HiSeq 4000 sequencing platforms. Sequenced reads were aligned to the GRCh37 reference genome (release hs37d5).

  Dataset EGAD00001011049

• Convergent somatic mutations in effectors of insulin signalling in chronic liver disease

  Chronic liver disease is associated with metabolic dysregulation, liver failure and hepatocellular carcinoma. We analysed somatic mutations from 1202 genomes across 32 liver samples, including normal controls, alcohol-related and non-alcoholic fatty liver disease. Five of 27 patients with liver disease carried hotspot driver mutations in FOXO1, the major transcription factor downstream of insulin signalling. FOXO1 mutations were independently acquired by up to 5 distinct clones within the same patient’s sample, and impaired insulin-mediated nuclear export of FOXO1. GPAM, which produces storage triacylglycerol from dietary calories, also had significant excess of mutations, similarly exhibiting convergent evolution within biopsies. Telomeres were shorter in diseased than normal liver, with attrition more pronounced in larger clones. Multiple independent acquisitions of drivers within one small liver sample imply that such mutations could affect hundreds of grams of tissue across the whole organ, potentially contributing to systemic metabolic dysfunction.

  Dataset EGAD00001006255

• The Dutch Microbiome Project Batch 2

  The Dutch Microbiome Project (DMP) data includes shotgun metagenomic sequencing of faecal samples 8,208 Dutch individuals. Paired-end sequencing was performed using Illumina HiSeq 2000 platform. Data is archived in two batches to facilitate easier data access and upload to EGA. Batch 2 of DMP includes ~400 samples.

  Dataset EGAD00001007501

• Genetics causes of male infertility in 185 patient-parent trios from Netherlands and UK

  The dataset is composed by the raw and processed sequencing data generated from 185 Patients affected by azoospermia or severe oligozoospermia recruited from the Netherlands and the UK.

  Dataset EGAD00001007862

• Exome Sequencing for Head and Neck Cancer Susceptibility

  This study seeks to find low frequency or rare genetic variants in genes carried by subjects from families with unexplained familial risk of head and neck cancer. Whole exome sequencing was used to identify genetic variation in head and neck cancer cases.

  Study phs002571

• TenK10K Phase 1: scRNA-seq fastq format sequencing data

  Single cell RNA-sequencing data for PBMCs from the Tenk10k Phase 1 cohort (1925 individuals post-QC). Libraries were prepared using the 10x Genomics 3’ Chromium Next GEM Single Cell HT V3.1 kit and sequenced on the NovaSeq 6000 platform. This analysis consists of 4,768 fastq format files in 1,192 runs, and an analysis which provides the relationship of 298 library pool samples to 1,925 individual samples.

  Dataset EGAD50000002517

• Somatic copy number alterations profiling in non-small cell lung cancer and their correlation with clinical efficacy in first-line treatment

  Genomic data from FFPE tumor samples of 258 patients with non-small cell lung cancer (NSCLC), profiled by shallow whole-genome sequencing (sWGS) at 1x. The study aimed to characterize somatic copy number alterations (SCNAs) and SCNA burden to assess their clinical relevance for patient stratification across different first-line treatments.

  Dataset EGAD50000002322

• Raw and processed snRNAseq data of Choroid Plexus Tumors

  This dataset contains raw sequencing and processed data of single nuclei transcriptomes. The data were generated with 10x chromium probe-based single-nucleus assay on FFPE tissues of 43 Choroid Plexus Tumour resections and 12 non-neoplastic fetal and adult ChP resections. Raw data contain BAM files of the sequencing. Processed data contain count matrices as generated by 10x CellRanger for raw and filtered running options.

  Dataset EGAD50000002320

• Single-cell RNA sequencing of Small Intestinal Neuroendocrine Tumors (SI-NET)

  Single-cell RNA sequencing (scRNA-seq) was performed on cells isolated from intestinal mucosa and small intestinal neuroendocrine tumors (SI-NETs) to compare their transcriptomic profiles. Tumor and adjacent normal tissues were collected from two SI-NET patients, enzymatically dissociated, and processed into single-cell suspensions. Single-cell libraries were generated using the Chromium Single Cell 3′ Reagent Kit v3 and sequenced on an Illumina platform to produce FASTQ files for downstream analysis.

  Dataset EGAD50000002265

• Small RNA-Seq of MicroRNA's in extracellular vesicles of Ushers Syndrome patients

  Small RNA-Seq dataset of MicroRNA's found in tear extracellular vesicles (EVs) and hiPSC-derived retinal pigment epithelium (RPE) cells of Ushers Syndrome patients and control samples. 14 samples were isolated and assessed for quality before preparing libraries using the miReasy and TruSeq Small RNA Library Preparation Kit. Sequencing was performed on the Illumina NextSeq 2000 platform, generating paired end FASTQ files.

  Dataset EGAD50000001709

• RNA-seq FASTQ files studied in Generation of hypoimmunogenic induced pluripotent stem cells by CRISPR-Cas9 system and detailed evaluation for clinical application

  This data set contains 12 paired fastq files (RNA-seq) from one wild-type iPS cell, two HLA-KO iPS clones and three CD14+ monocytes differentiated from them. The RNA-seq libraries were generated with Illumina TruSeq Stranded Total RNA library prep kit. The libraries were sequenced with Illumina NovaSeq 6000 in paired-end mode.

  Dataset EGAD50000001700

• RNA-sequencing from ALL patients treated on the Australasian Leukaemia and Lymphoma Group (ALLG) ALL06 study.

  Dataset contains RNA-sequencing data (fastq files) from 45 patients treated on the the Australasian Leukaemia and Lymphoma Group (ALLG) ALL06 study, an MRD-stratified Paediatric Protocol. Patient samples (bone marrow or peripheral blood) were taken at diagnosis. mRNA was extracted from blast cells and underwent paired-end sequencing (75bp) using the illumina NextSeq platform. Genomic data was used for identification of genomic drivers of acute lymphoblastic leukaemia (ALL).

  Dataset EGAD50000001111

• WES of serrated polyposis syndrome

  Samples were obtained from patients diagnosed with serrated polyposis syndrome (SPS). To provide further detail, the dataset comprises two whole-exome sequencing samples from patients within the same family, uploaded in BAM file format with IDs 449758 and 449549. Exomic regions were sequenced using HiSeq2000 Platform (Illumina, San Diego, USA) and Sure SelectXT All Exon v5 kit (Agilent, Santa Clara, CA, USA) for exon enrichment. Only those potentially pathogenic germline genetic variants shared by both individuals were considered.

  Dataset EGAD50000001126

• RNAseq of pre- and post-5AZA-treated head and neck cancer patients refractory to anti-PD-1 therapy

  RNA sequencing (RNA-seq) was performed on baseline (before low-dose 5-aza treatment) and on-treatment (low-dose 5-aza treatment for 5 or 10 days) tumor samples from eight participants with head and neck cancer who were refractory to anti-PD-1 therapy. RNA library preparations, sequencing reactions, and initial bioinformatics analyses were conducted at GENEWIZ, LLC. In total, 16 150-bp pair-end RNAseq data were generated.

  Dataset EGAD50000001010

• Clinical data and mapping file

  Patient demographic characteristics, treatment, and survival data for IMbrave151 (N = 162), with one patient per line. Mapping file to map anonymized subject IDs to RNAseq IDs

  Dataset EGAD50000000569

• Reconstructed VDJ sequences from Smart-seq2 data

  The dataset contains reconstructed VDJ sequences (fasta files) and accompanying metadata for each cell (csv file) from scRNA-seq data generated with the Smart-seq2 protocol. The VDJ sequences were reconstructed with the computational tool BraCeR using raw fastq files as input. The dataset contains sequences from 355 IgA+ peripheral blood B-lineage cells of two untreated celiac disease patients. The sequences comprise both IgA+ transglutaminase 2-specific and other IgA+ B cells.

  Dataset EGAD50000000341

• EED inhibition of organoid development

  Transcriptomics and epigenomics after chemical inhibition of EED during early human brain organoid development. To investigate the role of H3K27me3 inhibition during neuroectoderm induction, we treated brain organoids with 3 concentrations of EED inhibitor (and control) during the transition of pluripotency to neuroepithelium. At the neuroepithelium stage, we profiled the organoids using scRNA-seq (transcriptome) and bulk Cut&Tag (H3K27me3, H3K27ac). Library preparation for scRNA-seq was carried out with the 10x genomics platform.

  Dataset EGAD50000000224

• bfast CohortD clinical data

  Clinical data for BFAST Cohort D, including time-to-event, tumor response, and baseline prognostics data.

  Dataset EGAD50000000149

• bfast CohortD OSPL

  ctDNA sample data (one sample per line) for BFAST Cohort D including sample-level summaries like tumor fraction (cTF).

  Dataset EGAD50000000147

• PBMC all cell metadata

  Cell annotation for single cells of PBMC collected from 113 patients, including UMAP coordinates and annotation.

  Dataset EGAD50000000145

• CD8 T cell PBMC metadata

  Cell annotation for single CD8 T cells of PBMC collected from 113 patients, including UMAP coordinates and annotation

  Dataset EGAD50000000144

• Clinical data IMvigor130 cohort

  Clinical data for IMvigor130 cohort of patients, including treatment, response, survial time, and tumor PDL1-IC staining score.

  Dataset EGAD50000000143

• Sequencing data for oesophageal and related samples - OACs release 6 (RNA)

  Dataset EGAD00001005375

• Sequencing data for oesophageal and related samples - Normals release 3 (RNA)

  Dataset EGAD00001003902

• Sequencing data for oesophageal and related samples - Normals release 1 (RNA)

  Dataset EGAD00001002258

• Mutational processes moulding the genomes of 21 breast cancers

  The expression data for this study can be found here: http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1088/and its SNP6 data can be found here:http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1087/

  Dataset EGAD00001000138

• BLUEPRINT September 2016, RNA-Seq Acute Lymphocytic Leukemia from bone marrow, on Genome GRCh38

  RNA-Seq data for 1 sample(s) Acute Lymphocytic Leukemia from bone marrow, on Genome GRCh38. 1 run(s), 1 experiment(s), 0 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001002954

• BLUEPRINT September 2016, RNA-Seq Acute Myeloid Leukemia from venous blood, on Genome GRCh38

  RNA-Seq data for 1 sample(s) Acute Myeloid Leukemia from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 0 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001002958

• RNA sequencing data of pediatric KMT2A-rearranged acute lymphoblastic leukemia (set 1)

  The dataset includes RNA sequencing FASTQ files from diagnostic tumor samples of two KMT2A-r ALL patients (ALLT-325, ALLT-346). NEBNext Ultra Directional RNA Library Prep Kit (New England Biolabs) was used in library preparation, and Globin-Zero Gold rRNA Removal Kit (Illumina) was utilized for rRNA removal. The diagnostic tumor samples were sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb per sample.

  Dataset EGAD50000002582