12302 results for "cancer rna-seq"

in 49.58 milliseconds.

• mRNA-Seq dataset

  mRNA-Seq, HiSeq 2000 dataset of the Cell-line use case

  Dataset EGAD00001002250

• Reference epigenome IPS-NPC02 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007288

• Paired-end snATAC-seq analysis of the 3D spatially mapped GBM samples.

  Dataset contains 11 paired-end snATAC-seq samples from 4 patients.

  Dataset EGAD00001010313

• Bulk RNA sequencing of ALS patients

  We performed RNA sequencing to screen for alternative splicing alterations in patients with ALS to identify those linked to ALS pathology.

  Study JGAS000851

• RNA Sequencing of paediatric patients with B lymphoblastic leukemia

  RNA sequencing was performed on 148 Bone marrow of peripheral blood samples of paediatric patients with B-ALL.

  Study EGAS00001004532

• RNA sequencing data of pediatric TCF3::PBX1 acute lymphoblastic leukemia (set 2)

  The dataset includes RNA sequencing FASTQ files from a diagnostic tumor sample of one TCF3::PBX1 ALL patient (ALLT-376). NEBNext Ultra Directional RNA Library Prep Kit (New England Biolabs) was used in library preparation, and TruSeq Stranded Total RNA Library Prep (Illumina) kit was utilized for rRNA removal. The diagnostic tumor sample was sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb.

  Dataset EGAD50000002578

• RNA sequencing data of pediatric KMT2A-rearranged acute lymphoblastic leukemia (set 3)

  The dataset includes RNA sequencing FASTQ files from a diagnostic tumor sample of one KMT2A-r ALL patient (ALLT-375). NEBNext Ultra Directional RNA Library Prep Kit (New England Biolabs) was used in library preparation, and TruSeq Stranded Total RNA Library Prep (Illumina) kit was utilized for rRNA removal. The diagnostic tumor sample was sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb.

  Dataset EGAD50000002581

• RNA sequencing data of pediatric ETV6::RUNX1 acute lymphoblastic leukemia (set 5)

  The dataset includes RNA sequencing FASTQ files from a diagnostic tumor sample of one ETV6::RUNX1 ALL patient (ALLT-380). NEBNext Ultra Directional RNA Library Prep Kit (New England Biolabs) was used in library preparation, and TruSeq Stranded Total RNA Library Prep (Illumina) kit was utilized for rRNA removal. The diagnostic tumor sample was sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb.

  Dataset EGAD50000002591

• RNA sequencing data of pediatric B-other acute lymphoblastic leukemia (set 3)

  The dataset includes RNA sequencing FASTQ files from diagnostic tumor samples of two B-other ALL patients (ALLT-369, ALLT-378). NEBNext Ultra Directional RNA Library Prep Kit (New England Biolabs) was used in library preparation, and TruSeq Stranded Total RNA Library Prep (Illumina) kit was utilized for rRNA removal. The diagnostic tumor samples were sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb.

  Dataset EGAD50000002600

• Transcriptome_analysis_of_LCM_samples_

  We aim to set up a RNA pipeline for LCM samples to study transcriptome landscape of tumor and normal tissues. RNA was extracted from Lazer capture micro-dissection tissues and cDNA libraries were prepared with SMARTseq2 protocol. Libraries are pooled to be sequenced.

  Study EGAS00001003862

• MiRNome_pituitary_adenoma

  Small RNA (<100 bases in length) were purified from total RNA using miRNeasy kit (Qiagen), then sequenced. Libraries were prepared at the Genomics Platform of the Cochin Institute, following the TruSeq small RNA protocol (Illumina), starting from 1 µg of high quality total RNA. Single read (1 × 75 bp) sequencing was performed on a Nextseq 500 platform (Illumina). FASTQ sequences were aligned on miRBase v.2052, then counted with STAR (v.2.5.2a).

  Dataset EGAD00001004998

• reChIP-seq reveals widespread bivalency of H3K4me3 and H3K27me3 in CD4+ memory T-Cells

  Identification of genomic loci exhibiting the physical co-localization of two distinct DNA-associated proteins remains a technical challenge. To tackle this challenge we have developed a novel reChIP-seq approach that enriches for two distinct DNA-associated proteins in an unbiased and genome-wide manner. We illustrate the utility of our approach by identifying nucleosomes bivalently modified by H3K4me3 and H3K27me3 in primary human CD4+ central memory T-cells (TCMs). We unravel widespread bivalency of many unmethylated CpG-rich regions in TCMs overlapping with transcriptional start sites of developmental regulators. Furthermore, reChIP-seq uncovers two yet unobserved classes of bivalency, where either H3K4me3 or H3K27me3 is mixed with low but detectable bivalency. We provide evidence that bivalency is established by an interplay between genome and epigenome and thus attains stability. Our described reChIP-seq approach augments conventional ChIP-seq and is broadly applicable in the study of co-localization of DNA-associated proteins to unravel the intricate possibilities of combinatorial action.

  Study EGAS00001001568

• Genomic Analysis of Follicular Lymphoma

  We included tumors from newly diagnosed or untreated follicular lymphoma in this study. Samples consented to the University of Iowa and Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE) were included for data sharing. RNA was extracted from 1) CD19 sorted B cells from tumor cell suspension frozen in dimethyl sulfoxide (DMSO; RNA Tumor B Cell, n=77), 2) tumor cell suspensions frozen in DMSO (RNA Tumor DMSO, n=78), and 3) tumor tissue sections frozen in optimal cutting temperature compound (OCT; RNA Tumor OCT, n=53). For RNA sequencing, library preparation was done using the Illumina TruSeq RNA Exome Kit and sequenced with 100 nucleotide paired-end reads using the HiSeq 4000. DNA was extracted from 1) tumor cell suspensions frozen in DMSO (DNA Tumor DMSO, n=78), 2) tumor tissue sections frozen in OCT (DNA Tumor OCT, n=33), and 3) germline DNA from peripheral blood (n=102) and from normal tissue adjacent tumor (n=9). For whole exome sequencing (WES), library preparation was done using the Agilent SureSelect XT kit and sequenced with 100 nucleotide paired-end reads using the Illumina HiSeq 4000.

  Study phs002989

• scRaCH-seq data targeting BTK and SF3B1

  Data set for scRaCH-seq manuscript. We base call the fast5 using guppy.

  Dataset EGAD50000000235

• Reference epigenome ADMSC04_smRNA-Seq data generated from KEP study

  A ADMSC04_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003870

• dataset_CML_chipseq_pairend_fastq

  BMI1 ChIP-seq on human K562

  Dataset EGAD00001002061

• dataset_CML_chipseq_pairend_bam

  BMI1 ChIP-seq on human K562

  Dataset EGAD00001002062

• Reference epigenome ADMSC02 mRNA-Seq data generated from KEP study

  ADMSC02 mRNA-Seq paired end data

  Dataset EGAD00001007151

• Reference epigenome ADMSC05 miRNA-Seq data generated from KEP study

  ADMSC05 miRNA-Seq single end data

  Dataset EGAD00001007177

• Reference epigenome ADMSC06 miRNA-Seq data generated from KEP study

  ADMSC06 miRNA-Seq single end data

  Dataset EGAD00001007178

• Reference epigenome ADMSC07 miRNA-Seq data generated from KEP study

  ADMSC07 miRNA-Seq single end data

  Dataset EGAD00001007179

• Reference epigenome ADMSC08 miRNA-Seq data generated from KEP study

  ADMSC08 miRNA-Seq single end data

  Dataset EGAD00001007180

• Pathogenic Neurofibromatosis type 1 (NF1) RNA splicing resolved by targeted RNAseq

  Neurofibromatosis type 1 (NF1) is caused by loss-of-function variants in the NF1 gene. Approximately 10% of these variants affect RNA splicing and are either missed by conventional DNA diagnostics or are misinterpreted by in silico splicing predictions. Therefore, a targeted RNAseq-based approach was designed to detect pathogenic RNA splicing and associated pathogenic DNA variants. an in-house developed tool (QURNAS) was used to calculate the enrichment score (ERS) for each splicing event. RNA enrichment of NF1 and SPRED1 was done using SPET (NUGEN - NF1 only) and using SureSelect (Agilent - NF1 and SPRED1).

  Study EGAS00001005530

• Human tumor ChIP-seq.

  We profiled 15 patient brain tumor samples by ChIP-seq. Inputs are provided for 16 samples, H3K27ac is provided for 15 samples, H3K27me3 is provided for 10 samples and H3K27me3 is provided for 5 samples. The raw bam files are provided.

  Dataset EGAD00001008350

• RNAseq of baseline tumor tissue obtained during TUR to identify biomarkers for ICB response in MIBC

  RNAseq of baseline tumor tissue obtained during TUR to identify biomarkers for ICB response in MIBC. Constitutes the entire NABUCCO phase 1B clinical trial cohort. The NABUCCO 1 trial contains 18 single-end RNA sequenced samples and 6 paired end RNA sequenced samples. The NABUCCO 2 trial contains 25 paired end RNA sequenced samples. The file type for all of the samples is FASTQ. For single end RNA sequencing, Illumina HiSeq 2500 was used as the sequencing platform. For paired end RNA sequencing, the Illumina NovaSeq 6000 was used as the sequencing platform.

  Dataset EGAD50000002556

• Reference epigenome IPS01_N_Fibroblast_mRNA-Seq data generated from KEP study

  A IPS01_N_Fibroblast_mRNA-Seq paired end data for iPSC(Oct4)

  Dataset EGAD00001003488

• Reference epigenome IPS04_X_Fibroblast_mRNA-Seq data generated from KEP study

  A IPS04_X_Fibroblast_mRNA-Seq paired end data for iPSC(Oct4)

  Dataset EGAD00001003491

• Reference epigenome IPS01_N_Fibroblast_smRNA-Seq data generated from KEP study

  A IPS01_N_Fibroblast_smRNA-Seq single end data for iPSC(Oct4)

  Dataset EGAD00001003501

• Reference epigenome IPS04_X_Fibroblast_smRNA-Seq data generated from KEP study

  A IPS04_X_Fibroblast_smRNA-Seq single end data for iPSC(Oct4)

  Dataset EGAD00001003504

• McGill EMC Release 4 for assay "ChIP-Seq Input"

  McGill EMC Release 4 for assay "ChIP-Seq Input"

  Dataset EGAD00001001293

• Reference epigenome ADMSC01 mRNA-Seq data generated from KEP study

  ADMSC01 mRNA-Seq paired end data

  Dataset EGAD00001007150

• Reference epigenome ADMSC03 mRNA-Seq data generated from KEP study

  ADMSC03 mRNA-Seq paired end data

  Dataset EGAD00001007152

• Reference epigenome ADMSC05 mRNA-Seq data generated from KEP study

  ADMSC05 mRNA-Seq paired end data

  Dataset EGAD00001007153

• Reference epigenome ADMSC06 mRNA-Seq data generated from KEP study

  ADMSC06 mRNA-Seq paired end data

  Dataset EGAD00001007154

• Reference epigenome ADMSC07 mRNA-Seq data generated from KEP study

  ADMSC07 mRNA-Seq paired end data

  Dataset EGAD00001007155

• Reference epigenome ADMSC08 mRNA-Seq data generated from KEP study

  ADMSC08 mRNA-Seq paired end data

  Dataset EGAD00001007156

• Reference epigenome SMC01 mRNA-Seq data generated from KEP study

  SMC01 mRNA-Seq paired end data

  Dataset EGAD00001007169

• Reference epigenome SMC02 mRNA-Seq data generated from KEP study

  SMC02 mRNA-Seq paired end data

  Dataset EGAD00001007170

• Reference epigenome SMC03 mRNA-Seq data generated from KEP study

  SMC03 mRNA-Seq paired end data

  Dataset EGAD00001007171

• Reference epigenome SMC04 mRNA-Seq data generated from KEP study

  SMC04 mRNA-Seq paired end data

  Dataset EGAD00001007172

• Reference epigenome SMC05 mRNA-Seq data generated from KEP study

  SMC05 mRNA-Seq paired end data

  Dataset EGAD00001007173

• Reference epigenome SMC07 mRNA-Seq data generated from KEP study

  SMC07 mRNA-Seq paired end data

  Dataset EGAD00001007174

• Reference epigenome SMC08 mRNA-Seq data generated from KEP study

  SMC08 mRNA-Seq paired end data

  Dataset EGAD00001007175

• Reference epigenome SMC09 mRNA-Seq data generated from KEP study

  SMC09 mRNA-Seq paired end data

  Dataset EGAD00001007176

• scRNA-seq of placental immune cells

  scRNA-seq analysis of 384 placental immune cells

  Dataset EGAD00001010040

• STAG1-ChIP-Seq of STAG2-mutated and cohesin wildtype adult AMLs

  ChIPseq targeting the cohesin subunit STAG1 in STAG2-mutated AMLs or cohesin wildtype AMLs

  Dataset EGAD00001011206

• Single Cell RNA Sequencing of Human Hematopoiesis

  This study includes single cell RNA sequencing of human CD34+ hematopoietic stem and progenitor cells throughout human life spanning midgestation into mature adulthood.

  Study phs002750

• Global RNA sequencing data of human iPSC-derived microglia from frontotemporal dementia (FTD) patients

  Global RNA sequencing of RNA samples extracted from human iPSC-derived microglia from healthy controls and sporadic (non-genetic) and genetic C9orf72 hexanucleotide repeat expansion-carrying patients with FTD

  Dataset EGAD50000002423

• RNA sequencing data pre-treatment and post-treatment for NABUCCO cohort 1

  NABUCCO cohort 1 sequencing data. The dataset includes RNA sequencing pre-treatment on tumor samples (n=18) and RNA sequencing post-treatment on bladder tumor samples (n=18).

  Dataset EGAD00001006855

• Pilocytic Astrocytoma RNA sequencing

  This dataset contains RNA sequencing samples from the Duesseldorf/LIsbon pilocytic astrocytoma cohort which was profiled using Proteogenomics (RNA Sequencing, proteomics and methylation). There are 48 samples which were sequenced using paired-end sequencing at the University Hopstial Dusseldorf.

  Dataset EGAD00001009053

• Targeted PacBio long-read scRNA-seq

  Twist custom capture–based targeted PacBio long-read scRNA-seq datasets for human adrenal sampleA and sampleB

  Dataset EGAD50000002212

• Reference epigenome CKD23_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003481

• Reference epigenome CKD27_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003484

• Reference epigenome CKD23_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003497

• Reference epigenome CKD27_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003500

• Histone marks

  EZH2, H3K4me3, H3K27ac and H3K27me3 ChIP-seq data consisting of fastq single-end reads from peripheral blood CLL cells

  Dataset EGAD00001004063

• Reference epigenome KNIH010 mRNA-seq data generated from KEP study

  A KNIH010 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002176

• Reference epigenome KNIH011 mRNA-seq data generated from KEP study

  A KNIH011 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002177

• Reference epigenome KNIH010 miRNA-seq data generated from KEP study

  A KNIH010 miRNA-seq single end data for podocytes

  Dataset EGAD00001002769

• Reference epigenome KNIH011 miRNA-seq data generated from KEP study

  A KNIH011 miRNA-seq single end data for podocytes

  Dataset EGAD00001002770

• ATAC-Seq datasets in human islets

  13 ATAC-Seq datasets of human pancreatic islets from 13 donors

  Dataset EGAD00001005201

• Reference epigenome Pancreas-Islet06 miRNA-Seq data generated from KEP study

  Pancreas-Islet06 miRNA-Seq single end data

  Dataset EGAD00001007187

• Reference epigenome Pancreas-Islet07 miRNA-Seq data generated from KEP study

  Pancreas-Islet07 miRNA-Seq single end data

  Dataset EGAD00001007188

• Reference epigenome Pancreas-Islet08 miRNA-Seq data generated from KEP study

  Pancreas-Islet08 miRNA-Seq single end data

  Dataset EGAD00001007189

• Reference epigenome Fat-adipocyte03 miRNA-Seq data generated from KEP study

  Fat-adipocyte03 miRNA-Seq single end data

  Dataset EGAD00001007190

• Reference epigenome Fat-adipocyte04 miRNA-Seq data generated from KEP study

  Fat-adipocyte04 miRNA-Seq single end data

  Dataset EGAD00001007191

• Reference epigenome Fat-adipocyte05 miRNA-Seq data generated from KEP study

  Fat-adipocyte05 miRNA-Seq single end data

  Dataset EGAD00001007192

• Reference epigenome Islet-derived_MSC08 mRNA-Seq data generated from KEP study

  Islet-derived_MSC08 mRNA-Seq paired end data

  Dataset EGAD00001007879

• Reference epigenome Islet-derived_iPSC04 mRNA-Seq data generated from KEP study

  Islet-derived_iPSC04 mRNA-Seq paired end data

  Dataset EGAD00001007880

• Reference epigenome Islet-derived_MSC06 miRNA-Seq data generated from KEP study

  Islet-derived_MSC06 miRNA-Seq single end data

  Dataset EGAD00001007881

• Reference epigenome Islet-derived_MSC08 miRNA-Seq data generated from KEP study

  Islet-derived_MSC08 miRNA-Seq single end data

  Dataset EGAD00001007882

• Reference epigenome Islet-derived_iPSC04 miRNA-Seq data generated from KEP study

  Islet-derived_iPSC04 miRNA-Seq single end data

  Dataset EGAD00001007883

• Dataset for iAMP21 scRNA-seq

  10x Genomics 5' library scRNA-seq data for 4 iAMP21 patients

  Dataset EGAD00001009504

• RNA sequencing from patient-derived intestinal organoids

  uman duodenal tissues for establishing organoid cultures used in this study were sourced from de-identified surgical specimens (n = 3) of the duodenum obtained from patients who had undergone biopsy procedures unrelated to CeD at Tampere University Hospital. The protocol was approved by the Ethics Committee of Tampere University Hospital, Tampere, Finland (ETL code R18082). RNA from the duodenal organoids was isolated using an RNeasy Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. RNA purity and concentration were measured using a NanoDrop One spectrophotometer (NanoDrop Technologies, Wilmington, Delaware, USA). Preparation of the RNA library and transcriptome sequencing was conducted by Novogene Co., LTD (Cambridge, UK). Messenger RNA was purified from total RNA using polyA selection and subjected to library construction. Sequencing was performed on an Illumina platform, and 150 bp paired-end reads were generated.

  Dataset EGAD50000000492

• Single-cell multi-omics and mtDNA genotyping of human peripheral blood cells

  In this study, we characterize human peripheral blood cells using mitochondrial single-cell ATAC-seq (mtscATAC-seq), which simultaneously profiles mitochondrial genomes and chromatin accessibility at single-cell resolution. This submission includes PBMCs from one healthy pediatric donor and one 80-year-old patient diagnosed with mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome.

  Study EGAS50000001020

• Whole genome sequencing of adult glioblastoma nuclei

  Nuclei were extracted from three primary adult glioblastoma samples and processed for scATAC-seq, and residual nuclei left over from these same samples were used to extract DNA and processed and sequenced for WGS. This WGS data was then used to cross-validate assessment of large-scale copy number alterations with scATAC-seq using the Copy-scAT tool.

  Study EGAS00001005256

• ChIP-seq bulk

  This dataset gather ChIP-seq data produced by immunoprecipitating CTCF factor in own laboratory in MM.1S cell line in EtOH and Dex conditions. It also gather ChIP-seq dataset produced by external laboratory (Active Motif) for H3K27ac mark and GR transcription factor in same cell line and conditions ( MM.1S ETOH/Dex)

  Dataset EGAD00001011136

• Ancestry and somatic profile predict acral melanoma origin and prognosis – RNA

  Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, we characterise the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower median number of structural variants. The tumours with activating BRAF mutations have a transcriptional profile more similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and quadruple wild-type samples (non BRAF/NRAS/NF1/KIT) differed from mutated samples in their structural genomic profile and overall and recurrence-free survival patterns. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. We highlight potential novel low-frequency drivers, such as PTPRJ, NF2 and RDH5. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

  Dataset EGAD00001015756

• Single cell RNA and ATAC sequencing data from GBM patients

  Glioblastoma cells infiltrate and incorporate into the normal brain parenchyma. We conduct unbiased multimodal single-cell and spatial sequencing to capture GBM cells and the surrounding tumor microenvironment using a regional sampling strategy. For each patient, we profile the peritumoral region, tumor edge, and tumor core, providing a comprehensive characterization of the transcriptional and epigenetic landscape of GBM spatial heterogeneity, with a specific focus on infiltrative cells.

  Study EGAS50000000547

• Whole exome sequencing data of tumor/normal pairs for the study "TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells"

  Tumor DNA was extracted from formalin-fixed and paraffin embedded tumors of a large cohort of bladder cancer patients before treatment with anti-PD-L1. Normal DNA was extracted from matched PBMCs. Whole exome sequencing was performed. This is a subset of patients for which RNA sequencing is also provided (with more detailed phenotypic information).

  Dataset EGAD00001004218

• Genomic characterisation of SDH deficient renal cell carcinoma - WGS

  SDH deficient renal cell carcinomas are a rare and recently defined subtype of kidney cancer, often associated with an inherited mutation in one of the SDH gene subunits. This dataset sought to understand the genomic events that underpin tumour formation, from putative cell of origin, characterisation of the tumour microenvironment, to the genomic evolution of these rare tumours. We performed whole genome and RNA sequencing of 4 patients with SDH deficient renal cell carcinomas, including one patient who had an additional paraganglioma. An addition patient in this cohort had the initial diagnosis revised to a clear cell renal cell carcinoma.

  Dataset EGAD00001008469

• Single cell RNA sequencing of colorectal cancer patients (CRC-SG1)

  Raw sequencing reads were processed as single end sequencing, aligned to the human reference genome GRCh38 and processed using CellRanger 3.1.

  Dataset EGAD00001008555

• Dataset for upper_gastrointestinal_tumor-RNA

  Rare cancer sequencing data of 44 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008853

• Transcriptomic analyses of a large cohort of adult B cell acute lyphoblastic leukemia

  Total mononuclear cells (MNC) were isolated from peripheral blood (PB) or bone marrow (BM) samples of 105 Ph-/-/- (B-cell acute lymphoblastic leukemia [B-ALL] triple negative) and 31 Ph+ B-ALL adult patients using Lymphosep (Biowest, Nuaillé, France). A total of 15 samples from healthy subjects were processed including hematopoietic stem-progenitor cells (CD34+) from bone marrow specimens (n = 3), and bone marrow mononuclear cell samples (n = 3) from STEMCELL Technologies (Vancouver, Canada). PB MNC samples (n=5) and cord blood samples (n = 4). CD34+ cells were enriched from cord blood samples by immunomagnetic separation (CD34 MicroBead Kit, Miltenyi Biotec, Bergisch Gladbach, Germany).Libraries were prepared using the TruSight RNA Pan-Cancer Panel Kit (Illumina, San Diego, California, USA), following the manufacturer’s protocol. Sequencing was performed using the Illumina MiSeq instrument.

  Dataset EGAD00001010837

• An MTOR mutation hitchhikes through renal embryogenesis, driving multifocal, multiphenotypic tumours - RNA

  Embryogenesis is a vulnerable time. Mutations in developmental cells can result in the seeding of cells predisposed to disease within mature organs, creating a field effect. We characterise an embryonic cancer mutation that drives multifocal, multiphenotypic renal tumours in a 14-year-old girl. Their shared MTOR mutation, absent from normal tissues, increases protein flexibility which enables a FAT domain hinge to dramatically increase mTORC1 activity. Developmental mutations, not usually detected in traditional genetic screening, have vital clinical importance in guiding prognosis, targeted treatment, and family screening decisions for paediatric tumours.

  Dataset EGAD00001011646

• RNA sequencing reads

  Paired-end, ribosome depleted, total RNA Sequencing

  Dataset EGAD00001004327

• 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

• Reference epigenome CKD25_C_Podo_mRNA-Seq data generated from KEP study

  A CKD25_C_Podo_mRNA-Seq paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003483

• Reference epigenome OB57_D_PreA_mRNA-Seq data generated from KEP study

  A OB57_D_PreA_mRNA-Seq paired end data for Preadipocyte(fat)

  Dataset EGAD00001003487

• Reference epigenome OB56_N_PreA_smRNA-Seq data generated from KEP study

  A OB56_N_PreA_smRNA-Seq single end data for Preadipocytes(fat)

  Dataset EGAD00001003495

• Reference epigenome OB57_D_PreA_smRNA-Seq data generated from KEP study

  A OB57_D_PreA_smRNA-Seq single end data for Preadipocyte(fat)

  Dataset EGAD00001003496

• Reference epigenome CKD24_C_Podo_smRNA-Seq data generated from KEP study

  A CKD24_C_Podo_smRNA-Seq single end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003498

• Reference epigenome CKD25_C_Podo_smRNA-Seq data generated from KEP study

  A CKD25_C_Podo_smRNA-Seq single end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003499

• ATAC-seq for placental and buffycoat cells.

  We performed ATAC-seq experiments using 2 placental samples and 2 buffycoat samples.

  Dataset EGAD00001004291

• Reference epigenome Fat-adipocyte03 mRNA-Seq data generated from KEP study

  Fat-adipocyte03 mRNA-Seq paired end data

  Dataset EGAD00001007157

• Reference epigenome Fat-adipocyte04 mRNA-Seq data generated from KEP study

  Fat-adipocyte04 mRNA-Seq paired end data

  Dataset EGAD00001007158

• Reference epigenome Fat-adipocyte05 mRNA-Seq data generated from KEP study

  Fat-adipocyte05 mRNA-Seq paired end data

  Dataset EGAD00001007159

• Reference epigenome Pancreas-Islet06 mRNA-Seq data generated from KEP study

  Pancreas-Islet06 mRNA-Seq paired end data

  Dataset EGAD00001007166

• Reference epigenome Pancreas-Islet07 mRNA-Seq data generated from KEP study

  Pancreas-Islet07 mRNA-Seq paired end data

  Dataset EGAD00001007167

• Reference epigenome Pancreas-Islet08 mRNA-Seq data generated from KEP study

  Pancreas-Islet08 mRNA-Seq paired end data

  Dataset EGAD00001007168