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• 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

• Dataset for Sarcoma-WES linked from study EGAS00001004813

  Sequencing data of 144 sarcoma tumor and control runs, which were uploaded to EGAS00001004813. The sequencing was always paired.

  Dataset EGAD00001010258

• MutWP5: CRUK Mutographs of Cancer: Breast: Cancer Mastectomy (WG) (2020-01-29)

  Sequencing of LCM-derived microbiopsies from 40 women who underwent mastectomies due to breast cancer. LCM and sequencing will be conducted on both normal, unaffected breast, and, where possible, tumour tissue. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue, and compare findings between the normal and associated cancer tissues. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this portion of the study will be compared to women who had cosmetic breast reduction surgeries and those who are BRCA carriers. . This dataset contains all the data available for this study on 2020-01-29.

  Dataset EGAD00001005922

• CD19 CAR-T cells from non-Hodgkin's lymphoma patients

  The samples across 17 NHL patient samples with the CD19 CAR-T treatments are sequenced in 10x genomics. Refer to the manuscript supplementary tables and "https://github.com/hwanglab/hwanglab_2021_tigitCarT" for the sequencing sample sheets, the patient clinical information, processed data, and source codes.

  Dataset EGAD00001007741

• Use of Deep Sequencing to Dectect Clonal Mutations In Sun Exposed Skin Epidermis PART2

  This study aims to define the landscape of somatic mutations in sun exposed human skin by deep sequencing, analyse their frequency and use the data to infer the effect of mutations on proliferating cell behaviour. The frequency of each mutation will reflect the size of the clone of cells in the tissue sample. By analyzing small samples, clones with as few as 100 cells will be detectable. Allele frequency distributions for each mutation will be used to infer cell fate using published methods (Klein et al. 2010). This study will shed unprecedented light on the early clonal events that lead to the emergence of cancer.

  Dataset EGAD00001000825

• RNA-seq and Hi-C data of a chromothripsis patient's iPS cells

  This dataset contains RNA-seq and Hi-C data files of induced pluripotent stem (iPS) cells and iPS cell-derived neural progenitors (NPCs) derived from a germline chromothripsis patient and both parents. iPS cells of the patient (cell lines 14 and 15), the father (lines 23 (with two replicates) and 32) and mother (line 30) were differentiated to NPCs and RNA was collected on day 0, day 7 and day 10 of differentiation. In addition, Hi-C data for two iPS cell-derived NPC lines from the patient (14 and 15) and two lines from the father (23 and 32) was generated.

  Dataset EGAD00001002242

• Whole genome sequencing of multiple myeloma patient samples.

  Tumor (CD138+ plasma cells) and non-tumor (peripheral blood white cells or stem cell harvest) DNA from patients with a plasma cell dyscrasias were sequenced. Whole genome sequencing using high molecular weight DNA was performed using the 10X Genomics Chromium platform on either a HiSeq4000 or NovaSeq (Illumina) using 100 to 150 bp paired-end reads. The dataset consists of 111 patients, and 223 samples in total (matched tumor and control per patient; one patient had 2 tumor samples sequenced). Diseases consisted of 2 MGUS patients, 8 SMM patients, 91 newly diagnosed myeloma patients, 1 previously treated patient, 4 relapsed MM patients, and 5 PCL patients. Paired RNA-seq data are available for 81 of the samples under study EGAS00001003411.

  Dataset EGAD00001004452

• Use of Deep Sequencing to Dectect Clonal Mutations In Sun Exposed Skin Epidermis

  This study aims to define the landscape of somatic mutations in sun exposed human skin by deep sequencing, analyse their frequency and use the data to infer the effect of mutations on proliferating cell behaviour. The frequency of each mutation will reflect the size of the clone of cells in the tissue sample. By analyzing small samples, clones with as few as 100 cells will be detectable. Allele frequency distributions for each mutation will be used to infer cell fate using published methods (Klein et al. 2010). This study will shed unprecedented light on the early clonal events that lead to the emergence of cancer.

  Dataset EGAD00001001090

• GoT2D: Genetics of Type 2 Diabetes, a study of the the genetic architecture of type 2 diabetes

  The GoT2D study includes ~2800 samples, half T2D cases and half T2D controls, of Northern European ancestry sequenced over 3 three technologies: deep whole exome sequencing, low-pass (4x) whole genome sequencing, and OMNI 2.5M genotyping. Samples were ascertained to be phenotypically "extreme" (e.g. leaner, younger cases and older, more obese controls). Genotypes (SNVs, INDELs, and SVs) were called separately for each technology and then integrated via genotype refinement into a single phased reference panel; samples and variants were then excluded based on QC procedures described in Fuchsberger et al. Please note that 2 of the samples in the GoT2D vcf do not have phenotype data.

  Dataset EGAD00001002247

• Investigating low frequency variants in CAD/MI cases, controls and pedigrees using whole exome sequencing and custom pulldowns

  The Cardiogenics re-sequencing study will consist of three parts: Eight pools of 25 individuals will be sequenced using a Nimblegen hybrid-capture solution specific to miRNA sequences, 80 pools of 25 individuals will be sequenced using a custom Agilent SureSelect array covering genes associated with coronary artery disease (CAD) and myocardial infarction (MI), 10 individuals from families with a history of CAD/MI will be exome sequenced using the Sanger exome array. The experiment will use the early onset patients from the German MI cohort and the UK BHF CAD/MI cohort both of which have strong family history. For controls we will consider individuals from the UKBS and KORA cohorts.

  Dataset EGAD00001000400