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• Tumour sample for patient SA289

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA289

  Dataset EGAD00001009359

• Tumour sample for patient SA286

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA286

  Dataset EGAD00001009358

• Tumour sample for patient SA495

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA495

  Dataset EGAD00001009357

• Tumour sample for patient SA425

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA425

  Dataset EGAD00001009356

• Tumour sample for patient SA423

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA423

  Dataset EGAD00001009355

• Tumour sample for patient SA300

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA300

  Dataset EGAD00001009354

• Tumour sample for patient SA239

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA239

  Dataset EGAD00001009353

• A96175C

  Whole genome sequencing for single cells for library A96175C 1473 samples; filetype=bam

  Dataset EGAD00001008243

• A96172B

  Whole genome sequencing for single cells for library A96172B 1694 samples; filetype=bam

  Dataset EGAD00001008241

• A96162B

  Whole genome sequencing for single cells for library A96162B 1316 samples; filetype=bam

  Dataset EGAD00001008238

• A96157C

  Whole genome sequencing for single cells for library A96157C 938 samples; filetype=bam

  Dataset EGAD00001008237

• A96149B

  Whole genome sequencing for single cells for library A96149B 1792 samples; filetype=bam

  Dataset EGAD00001008235

• A96149A

  Whole genome sequencing for single cells for library A96149A 751 samples; filetype=bam

  Dataset EGAD00001008234

• A96146A

  Whole genome sequencing for single cells for library A96146A 1195 samples; filetype=bam

  Dataset EGAD00001008233

• A96228B

  Whole genome sequencing for single cells for library A96228B 1125 samples; filetype=bam

  Dataset EGAD00001008218

• RNA sequencing data of in vitro differentiated megakaryocyte cells transduced with E527K and WT SRC

  RNA sequencing data of in vitro differentiated megakaryocyte cells transduced with E527K and WT SRC. CD34+ hematopoietic stem cells (HSC) were isolated from healthy controls before transduction with WT-SRC and E527K-SRC lentiviral vectors in triplicate and differentiation to MK. Three replicates each of two pools were generated for both WT and E527K SRC transduced cells, resulting in 3 WT pool 1 samples, 3 WT pool 2 samples, 3 E527K pool 1 samples and 3 E527K pool 2 samples for a total of 12 samples. RNA was extracted and sequenced with following parameters: Platform: Illumina HiSeq4000, Library Prep Kit: TruSeq stranded mRNA, Sequencing Kit: Illumina HiSeq4000 100 cycles (76-8-8-7), Fragments: single end / fr-firststrand.

  Dataset EGAD00001008356

• RNA data for medulloblastoma samples (ICGC)

  RNA sequencing data for 170 medulloblastoma tumor samples

  Dataset EGAD00001003279

• GUARDIAN: The Insulin Resistance Atherosclerosis Study (IRAS Classic)

  The Insulin Resistance Atherosclerosis Study (IRAS) was an epidemiologic cohort study designed to examine the relationship between insulin resistance and carotid atherosclerosis across a range of glucose tolerance. Individuals of self-reported Mexican-American ethnicity were recruited in San Antonio, TX and San Luis Valley, CO. Recruitment was balanced across age and glucose tolerance status. GUARDIAN includes 173 individuals from the IRAS. Insulin sensitivity was obtained by FSIGT.

  Study phs001014

• Evolving Cell States and Oncogenic Drivers during the Progression of IDH-Mutant Gliomas

  The study aims to understand the changes in cell states during the progression of IDH mutant gliomas. We generated scATAC-seq data from tumor samples of patients with IDH mutant gliomas, ranging from grade 2 to grade 4, including both IDH mutant oligodendroglioma and IDH mutant astrocytoma samples. Additionally, we have generated scRNA-seq data from patient-derived organoids that were treated with a DNMT1 inhibitor.

  Study phs003697

• scKaryoSeq of CRISPR-Cas9 edited primary human T cells

  To explore T cell-intrinsic driving forces of CRISPR-induced aneuploidy at the TRAC gene locus, we performed scKaryo-seq (Bolhaqueiro et al., Nature Genetics 2019). We compared the impact of the small molecule pifithrin-alpha in activated TRAC KO T cells. In a separate experiment, we determined the effect of pifithrin-alpha on non-activated T cells edited at the TRAC locus or treated with non-targeting Cas9 RNPs.

  Dataset EGAD50000000929

• Peripheral blood RNA-sequencing in 4,732 participants of the INTERVAL cohort

  We performed bulk RNA-sequencing on peripheral blood collected from 4,732 blood donors recruited as part of the INTERVAL study. Using these data, we mapped gene expression and splicing quantitative trait loci (QTLs). Then, we integrated these data with protein, metabolite and lipid QTLs in the same individuals. The study aimed to identify the shared genetic etiology across transcriptional phenotypes, molecular traits and health outcomes in humans.

  Dataset EGAD00001008015

• 10xchromium 3' v3 sequencing from cerebellum, lung, and heart aligned to GRCh38 genome

  Each run contains single cell RNA-seq data from unbiased sampling of single cells from the indicated human tissue. Single cell suspensions were prepared using enzymatic dissociation followed by tituration. The samples were processed using the 10XChromium 3' v3 sequencing pipeline, sequenced on an Illumina NovaSeq 6000, and analyzed using the cellranger software and aligned to the human GRCh38 genome version 93.

  Dataset EGAD00001006110

• 110 "KOREAN" never-smoker female adenocarcinoma RNA-seq

  All sequencing was performed within the DNAlink (Korea) by using the Solexa sequencing technology (Illumina, San Diego, CA). mRNA was isolated from total RNA using poly-T oligo-attached magnetic beads and was fragmented with fragmentation buffer to an average size of 300 bp. The libraries were prepared by using TruSeq RNA Library Prep Kit v2 (Illumina) and were sequenced on the Illumina HiSeq2000 using the manufacturer’s recommended protocols

  Dataset EGAD00001005358

• Genomic Profiling of Sequentially Acquired Metastatic Sites from an "Exceptional Responder" Lung Adenocarcinoma Patient Reveals Extensive Genomic Heterogeneity and Novel Somatic Variants

  We employed next-generation sequencing to identify somatic alterations in multiple metastatic sites from an "exceptional responder" lung adenocarcinoma patient during his seven year course of ERBB2-directed therapies. The degree of heterogeneity was unprecedented, with ~1% similarity between somatic alterations of the lung and lymph nodes. One novel translocation, PLAG1-ACTA2, present in both sites, up-regulated ACTA2 expression. ERBB2, the predominant driver oncogene, was amplified in both sites, more pronounced in the lung, and harbored an L869R mutation in the lymph node. Functional studies demonstrated increased proliferation, migration, metastasis, and resistance to ERBB2-directed therapy due to L869R mutation and increased migration due to ACTA2 overexpression. Within the lung, a nonfunctional CDK12, due to a novel G879V mutation, correlated with down-regulation of DNA damage response genes, causing genomic instability, and sensitivity to chemotherapy. We propose a model whereby a sub-clone metastasized early from the primary site and evolved independently in lymph nodes.

  Study phs001159

• Chromatin accessibility in stem cells unveils progressive transcriptional reprogramming in myelodysplastic syndrome

  Myelodysplastic syndromes (MDS) originate from hematopoietic stem cell (HSC) clones with acquired gene mutations/deletions. However, our knowledge on the epigenetic properties of MDS stem cells are limited. Here, we examined chromatin accessibility of MDS stem cells. Chromatin accessibility profiles of MDS stem cells represented the disease status better than progenitor cells and unveiled the process of stem cell reprograming during disease progression. Characterization of differentially accessible regions (DARs) revealed that MDS stem cells acquire progenitor-like chromatin accessibility with disease progression, resulting in imprecise stem-progenitor hierarchy. Profiling of transcriptional factor binding motifs at DARs revealed precocious inactivation and activation of HSC and myeloid transcriptional networks, respectively, in MDS stem cells. Increased chromatin accessibility at CEBP target sites well represented the myeloid reprograming of MDS stem cells and stratified MDS into distinct subgroups. Newly developed "Progenitor scores" based on chromatin accessibility well stratified the disease status and correlated with prognosis. These results highlight that the chromatin properties of MDS stem cells define the cell-autonomous behavior of MDS and disease progression.

  Study JGAS000718