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• A non-canonical lymphoblast in refractory childhood T-cell leukaemia

  Refractory cancers may arise either through the acquisition of resistance mechanisms or represent distinct disease states. The origin of childhood T-cell acute lymphoblastic leukaemia (T-ALL) that does not respond to initial treatment, i.e. refractory disease, is unknown. Refractory T-ALL carries a poor prognosis and cannot be predicted at diagnosis. Here, we perform single cell mRNA sequencing of T-ALL from 58 children (84 samples) who did, or did not respond to initial treatment. We identify a transcriptionally distinctive blast population, exhibiting features of innate-like lymphocytes, as the major source of refractory disease. Evidence of such blasts at diagnosis heralds refractory disease across independent datasets and is associated with survival in a large, contemporary trial cohort. Our findings portray refractory T-ALL as a distinct disease with the potential for immediate clinical utility. This dataset contains 56 single-cell RNA sequencing data files with corresponding single-cell T cell receptor sequencing data files. The data here form the validation cohort in our paper.

  Dataset EGAD00001015673

• Variant calling dataset from the whole-exome study of familial pulmonary fibrosis in the Canary Islands-VCF files

  Sequence reads were obtained from a set of 13 families with familial pulmonary fibrosis in the Canary Islands, Spain, using Illumina paired-end reads, respectively, at the Institute of Technology and Renewable Energy (ITER). Briefly, we used bcl2fastq v2.18 to perform sample demultiplexing and BWA-MEM v0.7.15 (https://github.com/lh3/bwa) to align reads to GRCh37/hg19 reference. Resulting BAM files were assessed with SAMtools v1.3 (http://www.htslib.org) and Picard v2.10.10 (https://broadinstitute.github.io/picard/) for quality control steps. Small insertions/deletions (< 50 bp) and single nucleotide variants (SNVs) were identified using an in-house bioinformatics pipeline based on GATK HaplotypeCaller v3.8 (https://gatk.broadinstitute.org/hc/en-us/articles/360037225632-HaplotypeCaller). This pipeline follows the Best Practices recommendations for germline variant calling and its description is publicly available (https://github.com/genomicsITER/benchmarking/tree/master/WES).

  Dataset EGAD50000001152

• Single-cell RNA-sequencing of CSF cells and PBMCs from individuals with neurological disorders

  We undertook single-cell RNA sequencing to establish gene expression and lymphocyte receptor sequences of CSF and peripheral blood leukocytes derived from patients with a variety of inflammatory, infectious and non-inflammatory neurological disorders. The aim of the study was the investigation of functional changes of the cells involved in neuroinflammatory responses.

  Study EGAS00001007954

• Heterogeneity and evolution of DNA mutation rates in microsatellite stable colorectal cancer. Higher mutation rates (MR) in metastatic tumours

  Building on the observation that MR appears to be a unique and inheritable feature of each cancer cell, we explored whether MR is subject to selective pressure during cancer progression. These data suggest that a higher MR is selected during CRC metastatic progression.

  Study EGAS50000000147

• TRACERx 100: RNAseq data from the first 100 TRACERx tumours

  TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC). This study looks at the multi-region RNAseq data from the TRACERx100 cohort with high enough quality RNA available. There is RNAseq data from 164 regions (64 patients).

  Study EGAS00001003458

• Targeted DNA panel sequencing analysis of PanNEN tumors of varying grades using custom and commercial panels

  Targeted panel sequencing data from PanNEN samples. Sample ID is annotated in the following manner: each patient is given a number and "P" is appended to the patient number if it is a primary tumor, "M” if it is metastasis and "N" if it is normal (healthy tissue) sample. All NETG1 and NETG2 samples underwent panel sequencing using a custom panel (in-house PanNEN panel). All NEC and NETG3 samples (except PNET2, PNET77P and PNET77M) underwent panel sequencing using a commercial CCP panel.

  Dataset EGAD00001008432

• Paired FastQ files from deep targeted DNA sequencing

  This dataset comprises paired-end and time-matched FastQ files from 356 cell-free DNA samples and 356 white blood cell DNA samples, derived from 299 patients with metastatic renal cell carcinoma or urothelial carcinoma. A baseline sample was available for all patients. Sequencing was performed using Illumina technology.

  Dataset EGAD50000001267

• Exome and transcriptome sequencing of CDS and ES tumor samples

  This dataset contains WES and RNASeq data of 31 CDS and ES tumor and control samples. Sequencing was performed on Illumina NovaSeq 6000. The sequencing was performed paired in bulk.

  Dataset EGAD00001015608

• Berlin Neuroblastoma Patient Genomic Data from Targeted Sequencing for Detection of TERT rearrangement breakpoints to monitor neuroblastoma

  Our objective was to establish a liquid biopsy-based monitoring strategy for pediatric high-risk neuroblastomas that are harboring genomic TERT rearrangements at diagnosis. TERT rearrangement breakpoints are detected by a hybrid capture-based neuroblastoma DNA panel sequencing (published in PMID: 34442335) in tumor material and are reflected in cell-free tumor DNA and can serve as robust biomarkers for disease activity. Within the dataset, 5 tumors of 4 pediatric patients with a neuroblastoma were DNA sequenced. Provided are FASTQ data files, bam and bambai files, as well as breakpoint spanning and encompassing read (enspan) bam and bambai files. Sequencing bam data files are aligned to GRCh37.p13 reference genome (processed).

  Dataset EGAD00001011088

• Single-Nuclei RNA Sequencing and Spatial Transcriptomics of Human Heart Right Atrial Appendage and Pericardial Fluid in Cardiovascular Disease

  This dataset includes single-nuclei RNA sequencing (snRNA-seq) and spatial transcriptomics data from biopsies of the right atrial appendage and pericardial fluid, collected during open-heart surgeries. The samples represent various clinical contexts, including control, ischemic heart disease, heart failure, and myocardial infarction. For each biopsy, nuclei were isolated, and RNA sequencing libraries were prepared using the 10x Genomics Chromium v3 platform. Additionally, spatial transcriptomics was performed on the heart samples. The dataset provides raw sequencing data, which includes fastq files generated from these libraries, offering valuable insights into the cellular composition of heart tissues and associated fluids across different disease states.

  Dataset EGAD50000000927