2197 results for "盛和资源+机构评级+2026年3月最新"

in 21.25 milliseconds.

• Transfer Learning Associates CAFs with EMT and Inflammation in Tumor Cells in Human Tumors and Organoid Co-Culture in Pancreatic Ductal Adenocarcinoma

  This study examined the transcriptional changes induced by cancer associated fibroblasts (CAFs) in pancreatic ductal adenocarcinoma patient-derived tumor organoids. We hypothesized that cancer associated fibroblasts impacted tumor cell transcription to promote tumorigenic behavior. Using patient-matched cells from 3 patients, we compared the transcriptional profiles of patient-derived organoids in monoculture to patient-derived organoids co-cultured with CAFs. Similarly, we looked at the transcriptional changes in matched patient-derived cancer associated fibroblasts grown in monoculture or co-cultured with patient-derived organoids. With a focus on intercellular crosstalk, we identified crosstalk between cancer associated fibroblasts and patient-derived organoids through VEGFA and NRP1. This was identified by bulk RNA sequencing and examined dynamically in organoid-CAF co-culture. This dbGaP deposit is the bulk RNA sequencing data for these 3 patients.

  Study phs003563

• "Distinct immunometabolic signatures in circulating immune cells define disease outcome in acute-on-chronic liver failure"

  We performed single-cell transcriptomics of PBMC’s from healthy (n= 3), acute decompensated (AD) (n= 3) and Acute-on-chronic liver failure (ACLF) patients. Lay Summary: Acute-on-chronic liver failure (ACLF) develops in cirrhotic patients and is characterized by an hyper-inflammatory immune state driving multiple organ failure(s). As a paradox, circulating immune cells also show transcription and metabolic evidence of exhaustion, explaining why these patients are often susceptible to secondary infections. We characterized at a transcriptional level all circulating immune cells of ACLF patiens, and specifically unraveled a distinct recovery and non-recovery ACLF signature within the monocyte population. Moreover, these changes were closely linked to metabolic differences within these cells. The described alterations allowed for a robust prediction of recovery and non-recovery of patients at admission to the hospital.

  Study EGAS50000000391

• Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer

  A subset of Cancer-Associated Fibroblasts (CAF-S1) mediates immunosuppression in breast cancers (BC), but its heterogeneity and its impact on immunotherapy response remain unknown. Here, we identify 8 CAF-S1 clusters by analyzing more than 18000 single CAF-S1 fibroblasts from BC. We validate the 5 most abundant clusters by flow cytometry and in silico analyses in other cancer types, highlighting their relevance. Myofibroblasts from clusters 0 and 3, characterized by extra-cellular matrix proteins and TGF signaling respectively, are indicative of primary resistance to immunotherapies. Cluster 0 up-regulates PD-1 and CTLA-4 protein levels in regulatory T lymphocytes (Tregs), which in turn increases CAF-S1 cluster 3 cellular content. Thus, our study highlights a positive feedback loop between specific CAF-S1 clusters and Tregs and uncovers their role in immunotherapy resistance.

  Study EGAS00001004030

• EPIC methylation array of human lung fibroblasts

  EPIC Array data from human lung fibroblasts isolated from fresh and cryopreserved lung tissue (16 samples, 3 donors)

  Dataset EGAD00010002463

• Short read sequencing dataset of pathogen-stimulated PBMCs

  QuantSeq 3'-mRNAseq. 5 donors, 4 stimuli, 2 time points

  Dataset EGAD50000000012

• Deep sequencing of 151 cancer genes

  Deep sequencing of 151 cancer genes in 6 synchronous CRC of 3 patients

  Dataset EGAD00001001870

• RNA-seq from normal human tissues

  RNA-seq from normal human tissues (2 x 75 bp)

  Dataset EGAD00001001057

• HipSci - Macular Dystrophy - Exome Sequencing - July 2017

  HipSci - Macular Dystrophy - Exome Sequencing - July 2017

  Dataset EGAD00001003525

• PD-associated regulatory variants in human dopaminergic neurons reveals modulators of SCARB2 and BAG3 expression

  SNPs have been associated with the risk of Parkinson’s disease but not in regulatory regions. While those potentially disrupt TFBS altering gene expression and contributing to cis-regulatory variation. To study changes in A and B compartment interactions and in topologically associated domains (TADs), we sequenced: - 3D chromatin contact using LowC, 6 samples. 3 replicates for each condition: smNPC (controls) cells and differentiated neurons after 30 days. Resulting paired-end are provided as BAM files. In order to confirm the predicted impact of the PD-associated allele of rs144814361 on BAG3 promoter, we proceeded with genome editing of the TH-REP1 cell line by using prime editing to insert the “T” allele at the position chr10:119651405 in the BAG3 promoter. - Chromatin accessibility using ATAC-seq, 9 samples of derived cell line TH-REP1. 3 replicates for each condition: 1) iPSC Wild Type 2) SNP-BAG3 variant in iPSC 3) SNP-BAG3 variant in smNPC. BAG3 variant: heterozygous for the SNP variant: rs144814361 (chr10 119651405) Resulting sequencing are provided as paired-end FASTQ files.

  Dataset EGAD50000002258

• Long-term organoid culture of a small intestinal neuroendocrine tumor

  tissue, organoid and normal bam files for whole exome samples

  Dataset EGAD00001010162

• EGYPT_HIGH_COVERAGE_vcf

  Integrated callset of high coverage Egyptian genomes from the Pagani et al. 2015 AJHG paper (doi: http://dx.doi.org/10.1016/j.ajhg.2015.04.019)

  Dataset EGAD00001003295

• The UCSF Low Grade Glioma Genome Project #2"

  Dataset contains WES data from 3 astrocytoma patients: blood as control, primary tumor and recurrent tumor

  Dataset EGAD00001001305

• Integrative analysis of exome-seq, RNA-seq, ATAC-seq (bulk and single-cell), and Hi-C data generated from 3-D spatially mapped samples acquired during surgical resection from 10 patients diagnosed with IDH-WT glioblastoma

  Glioblastoma (GBM), the most common primary brain cancer in adults, remains incurable with no targeted therapies approved despite decades of investigation into its molecular landscape. Treatment failure is attributed to intratumoral heterogeneity in the GBM genome and epigenome, which foster tumor evolution and selection of resistant clones. However, tumor evolution and intratumoral heterogeneity remain poorly understood on the level of the whole tumor as most studies are based on single samples per patient and lack spatial context. Here, we have used 3-D neuronavigation during surgical resection for 10 primary IDH-WT GBM patients to collect 102 samples representing maximal tumor diversity, each mapped by 3-D spatial coordinates. We have applied a strategic set of genomic and epigenomic assays spanning multiple levels of resolution to discover, orthogonally validate, and functionally assess drivers of tumor evolution and intratumoral heterogeneity. These include extrachromosomal DNA amplifications, chromothripsis events, inversions, and translocations that disrupt both the GBM genome and epigenome while generating fusion transcripts and opportunities for therapeutic intervention. We define epigenomic programs that contribute to GBM evolution and intratumoral heterogeneity, revealing their 3-D spatial patterning within whole tumors and their cell type(s) of origin in single-cell data from the same tumor samples. Notably, we distinguish neuronal, glial, and immune programs aberrantly active in tumor cells from their counterparts in normal cells and discover NEUROD1, JUN/FOS, and NF1 transcription factors as key drivers of GBM evolution and growth. Collectively, these data provide unprecedented insight into GBM evolution and intratumoral heterogeneity from single-cell to whole-tumor resolution, redefining current understanding and providing a rich resource of targets for therapeutic investigation.

  Study EGAS00001006785

• Dutch - BP

  The dataset encompasses 1110 Runs from the WGSPD Project 3 - Genomic Strategies to Identify High-impact Psychiatric Risk Variants Project

  Dataset EGAD50000001738

• Dutch - SCZ

  The dataset encompasses 804 Runs from the WGSPD Project 3 - Genomic Strategies to Identify High-impact Psychiatric Risk Variants Project

  Dataset EGAD50000001739

• HipSci - Bardet-Biedl Syndrome - Exome Sequencing - January 2016

  HipSci - Bardet-Biedl Syndrome - Exome Sequencing - January 2016

  Dataset EGAD00001001950

• HipSci - Bardet-Biedl Syndrome - RNA Sequencing - January 2016

  HipSci - Bardet-Biedl Syndrome - RNA Sequencing - January 2016

  Dataset EGAD00001001954

• HipSci - Bardet-Biedl Syndrome - Exome Sequencing - April 2015

  HipSci - Bardet-Biedl Syndrome - Exome Sequencing - April 2015

  Dataset EGAD00001001422

• Additional Neuroblastoma whole genome sequencing data

  Additional Neuroblastoma whole genome sequencing data

  Dataset EGAD00001006196

• SUM-seq data for spontaneous differentiation of iPSCs upon perturbation of GATA2, NR4A2 or SOX17

  We combined SUM-seq with arrayed CRISPR screening, modulating the expression of key lineage transcription factors (TFs) (GATA2 - mesoderm, SOX17 - endoderm, and NR4A2 - neuroectoderm) in hiPSCs via CRISPR interference (CRISPRi) or activation (CRISPRa) over a time course of spontaneous differentiation (days cultured in vitro: 0, 4, 12, and 18). This totalled to 54 samples (4 day/time points x 3 target TFs x 2-3 gRNAs per target x CRISPRi/a). Single-cell chromatin accessibility and gene expression data is provided as demultiplexed fastq files.

  Dataset EGAD50000001205

• Longitudinal Transcriptomic Profiling of Endothelial Progenitor Cells in Post-COVID-19 Patients: Insights at 3 and 6-Months Post-SARS-CoV-2 Infection

  Raw transcriptomic data from endothelial cells of post-COVID patients and healthy controls. Samples were collected from post-COVID patients at 3 months (n = 6) and 6 months (n = 6) post-infection, as well as from healthy control individuals (n = 5). All post-COVID patients were hospitalized during the first wave of the pandemic. Among them, half developed pulmonary embolism (TEP; n = 6), while the other half did not (no TEP; n = 6).

  Dataset EGAD50000001452

• scRNA dataset for 15 samples

  This dataset includes single cell (sc)RNA-seq of N=3 patient-derived xenograft (PDX) samples profiled at baseline (no drug treatment) and N=12 samples from one PDX profiled during the course of treatment (N=1 sample each for 4 treatment arms x 3 timepoints). The dataset includes bam files that have been purged of reads mapping to cells where a majority of the reads from that cell mapped to the mouse genome. All scRNA-seq data were generated using the Chromium Next GEM Single Cell 5' Reagent Kits v2 (Dual Index) from 10X Genomics.

  Dataset EGAD50000001424

• PRO-SPeCT: PROstate cancer heterogeneity deconvolution through Single cell Profiling of Chromatin accessibility and Transcriptomic output

  38 prostate cancer specimens derived from five human patients with ISUP ≥ 3 pathology who underwent radical prostatectomy and pelvic lymph node dissection at surgery as part of the International Cancer Genome Consortium (ICGC) late-onset cohort. Using single-cell multiome (expression and chromatin accessibility from RNA and ATAC, respectively) sequencing assay, we characterized 282,956 nuclei/cells, with a median of 6 primary tumor foci from the prostate gland and 3 locoregional lymph node metastases across all patients.

  Dataset EGAD50000000745

• Single-nuclei multiomic analysis of KOLF2.1J human induced pluripotent stem cells-derived differentiated dopaminergic neurons

  Datasets consists of 12 samples of nuclei from hIPSCs differentiated optimized protocols for generating dopaminergic neurons. 6 single nuclei (RNA-ATAC) samples of XMAS protocol with cells collected at days 0,11,16,28,42 and 56 after differentiation. 3 snRNA-seq samples from AMS protocol of cells collected at days 14,16 and 28 after differentiation and 3 snRNA-seq samples with AMS protocol until day 14 grafted and grafts processed after 5 months grafting.

  Dataset EGAD50000002054

• ChIP-seq analysis of clear cell renal cell carcinoma

  In order to understand the epigenetic basis on ccRCC, we performed ChIP-seq on 3 ccRCC cell lines (786-O, A498, UMRC-2), and 1 normal kidney epithelium cell line (HK-2), cultured under normoxic (20% O2) and hypoxic (0.05% O2) conditions. This study analysed data from H3k4me1, H3K4me3, H3K27ac, H3k27me3, H3k36me3, CTCF, and HIF1A

  Study EGAS50000001322

• ATAC-seq of uncultured CD112high and CD112low long-term(LT) human hematopoietic stem cells (HSC) from umbilical cord blood

  ATAC-sequencing was performed to establish chromatin accessibility signatures that underlie the differential in vitro priming and regeneration phenotypes of CD112high and CD112low LT-HSC. CD112high and CD112low LT-HSC from 2-3 independent human umbilical cord blood pools were prospectively isolated for DNA extraction, library preparation and sequencing.

  Study EGAS00001005121

• Single-cell RNA sequencing dissects gene-environment interactions on gene expression and regulation in immune cells.

  This study aims to dissect gene-environment interactions on gene expression and regulation in immune cells using scRNA-seq data from 120 donors being stimulated with 3 pathogens, for 2 different timepoints. More specifically, we identified context- and cell-type-specific eQTLs and co-expression QTLs in PBMCs.

  Study EGAS00001005376

• Transcriptome profiling of three giant cell tumour of bone (GCTB) cell lines

  This dataset contains RNA-seq data of giant cell tumour of bone (GCTB) cell lines (n=3). Cell lines consist of neoplastic "stromal" cells harboring a heterozygous H3F3A p.G34W mutation. RNA-seq was performed on the BGISEQ-500 platform (PE100) and uploaded data contains fastq files, vcf files, and gene expression values (TPM). Cell lines, data generation, and data analysis are described in the following publication: Venneker et al., Histone deacetylase inhibitors as a therapeutic strategy to eliminate neoplastic “stromal” cells from giant cell tumors of bone, 2022.

  Dataset EGAD00001009074

• Orthotopic murine Group 3 medulloblastoma in C57Bl/6 mice treated with saline, cyclophosphamide or gemcitabine

  Mice with medulloblastoma (Group 3) were treated with saline, cyclophosphamide, or gemcitabine as described by Abbas et al (2020). Total RNA was isolated with RNeasy Plus Mini Kit (Qiagen), library preparation (SureSelect, Agilent), rRNA depletion (Ribo-Zero Plus, Illumina) and sequencing were carried out by GenomicsWA or Australian Genome Research Facility. Libraries were sequenced on NovaSeq 6000 S1 flow cells as paired-end 150bp reads (Illumina).

  Dataset EGAD00001008511

• Whole Transcriptome Analysis + AbSeq + Sample Tag Multiplexing

  Multiomics data for a cohort of 20 COVID-19 patients (10 patients mild, 3 patients moderate, 4 patients severe, 3 patients critical) obtained from peripheral blood mononuclear cells (PBMCs) from longitudinally sampled at hospital admission, discharge, and 1 month thereafter. The data has been obtained a multiwell-based single-cell technology (BD Rhapsody) that includes the analysis of PBMCs' whole transcriptome and a set of 52 surface proteins. The samples of the different patients at different collection times were labelled using a cell hashing strategy with the BD Single-Cell Multiplexing Kit (6 samples for each run).

  Dataset EGAD00001011318

• NABUCCO cohort 1 (NCT03387761) - Neo-Adjuvant Bladder Urothelial Carcinoma COmbination-immunotherapy

  NABUCCO cohort 1 sequencing data. The dataset includes: * Whole exome DNAseq pre-treatment on tumor samples (n=24) matched with blood samples (n=24) * RNAseq pre-treatment on tumor samples (n=18) * RNAseq post-treatment on tumor samples (n=18). Not all pre-treatment samples are linked with pre-treatment samples * High coverage Whole exome DNAseq on pre-treatment tumor samples (n=3) matched with post-treatment metastasized lymph nodes isolated with laser microdissection (n=3) * All samples are labelled with the response phenotype (Complete Responder or Non-Complete Responder)

  Dataset EGAD00001006250

• CCA ChIP-seq data (63 CCA, 8 normal, 19 cell-line)

  Fastq files for H3K27ac ChIP-seq (with matched input-DNA control) for 63 CCAs, 8 normal bile duct tissues, 16 CCA cell-lines (including replicates), and 3 normal cholangiocyte cell-lines (including replicates). Library prep was performed with the NEBNext ChIP-seq library preparation kit. Each library (including matching input DNA) was sequenced to an average depth of 20 to 30 million raw reads on Illumina HiSeq4000 sequencer, with paired-end sequencing (except for 3 normal bile tissues done with single-end sequencing).

  Dataset EGAD00001012103

• Whole genome sequencing of rhabdoid tumor tissue and organoids

  Dataset EGAD00001007947

• Finnish - THL SUPER

  The dataset encompasses 852 Runs from the WGSPD Project 3 - Genomic Strategies to Identify High-impact Psychiatric Risk Variants Project

  Dataset EGAD50000001466

• scRNAseq of ovarian cancer patient derived organoids

  scRNAseq of ovarian cancer patient derived organoids with or without estradiol

  Dataset EGAD50000002058

• Targeted capture ctDNA Library CRCQV42Run039-21

  Targeted capture ctDNA Library CRCQV42Run039-21 from patient PBC0003014, plasma 3 month sample

  Dataset EGAD00001010616

• Targeted capture ctDNA Library CRCQV42Run046-20

  Targeted capture ctDNA Library CRCQV42Run046-20 from patient PBC0001176, plasma 3 month sample

  Dataset EGAD00001010711

• Targeted capture ctDNA Library CRCQV42Run045-12

  Targeted capture ctDNA Library CRCQV42Run045-12 from patient PBC0001068, plasma 3 month sample

  Dataset EGAD00001010683

• Targeted capture ctDNA Library CRCQV42Run041-24

  Targeted capture ctDNA Library CRCQV42Run041-24 from patient PBC0005116, plasma 3 month sample

  Dataset EGAD00001010633

• Targeted capture ctDNA Library CRCQV42Run041-12

  Targeted capture ctDNA Library CRCQV42Run041-12 from patient PBC0001413, plasma 3 month sample

  Dataset EGAD00001010626

• Targeted capture ctDNA Library CRCQV42Run046-16

  Targeted capture ctDNA Library CRCQV42Run046-16 from patient PBC0001042, plasma 3 month sample

  Dataset EGAD00001010707

• Targeted capture ctDNA Library CRCQV42Run044-7

  Targeted capture ctDNA Library CRCQV42Run044-7 from patient PBC0001314, plasma 3 month sample

  Dataset EGAD00001010678

• Targeted capture ctDNA Library CRCQV42Run041-4

  Targeted capture ctDNA Library CRCQV42Run041-4 from patient PBC0001375, plasma 3 month sample

  Dataset EGAD00001010634

• Targeted capture ctDNA Library CRCQV42Run043-23

  Targeted capture ctDNA Library CRCQV42Run043-23 from patient PBC0002480, plasma 3 month sample

  Dataset EGAD00001010652

• Targeted capture ctDNA Library CRCQV42Run043-24

  Targeted capture ctDNA Library CRCQV42Run043-24 from patient PBC0002329, plasma 3 month sample

  Dataset EGAD00001010653

• Targeted capture ctDNA Library CRCQV42Run046-6

  Targeted capture ctDNA Library CRCQV42Run046-6 from patient PBC0001666, plasma 3 month sample

  Dataset EGAD00001010718

• Follicular T cell lymphoma

  Paired-end RNA-seq of follicular T cell lymphoma for the discovery of fusion transcripts

  Dataset EGAD00001006379

• Targeted capture ctDNA Library CRCQV42Run041-21

  Targeted capture ctDNA Library CRCQV42Run041-21 from patient PBC0001335, plasma 3 month sample

  Dataset EGAD00001010630

• Targeted capture ctDNA Library CRCQV42Run043-19

  Targeted capture ctDNA Library CRCQV42Run043-19 from patient PBC0004537, plasma 3 month sample

  Dataset EGAD00001010648

• Targeted capture ctDNA Library CRCQV42Run043-20

  Targeted capture ctDNA Library CRCQV42Run043-20 from patient PBC0003244, plasma 3 month sample

  Dataset EGAD00001010649

• Targeted capture ctDNA Library CRCQV42Run043-21

  Targeted capture ctDNA Library CRCQV42Run043-21 from patient PBC0005013, plasma 3 month sample

  Dataset EGAD00001010650

• Targeted capture ctDNA Library CRCQV42Run044-15

  Targeted capture ctDNA Library CRCQV42Run044-15 from patient PBC0001818, plasma 3 month sample

  Dataset EGAD00001010665

• Targeted capture ctDNA Library CRCQV42Run045-16

  Targeted capture ctDNA Library CRCQV42Run045-16 from patient PBC0001782, plasma 3 month sample

  Dataset EGAD00001010687

• Targeted capture ctDNA Library CRCQV42Run035-18

  Targeted capture ctDNA Library CRCQV42Run035-18 from patient PBC0002853, plasma 3 month sample

  Dataset EGAD00001010583

• Targeted capture ctDNA Library CRCQV42Run036-6

  Targeted capture ctDNA Library CRCQV42Run036-6 from patient PBC0005064, plasma 3 month sample

  Dataset EGAD00001010604

• Targeted capture ctDNA Library CRCQV42Run043-17

  Targeted capture ctDNA Library CRCQV42Run043-17 from patient PBC0004350, plasma 3 month sample

  Dataset EGAD00001010646

• Targeted capture ctDNA Library CRCQV42Run029-24

  Targeted capture ctDNA Library CRCQV42Run029-24 from patient PBC0003334, plasma 3 month sample

  Dataset EGAD00001010517

• Targeted capture ctDNA Library CRCQV42Run035-21

  Targeted capture ctDNA Library CRCQV42Run035-21 from patient PBC0003595, plasma 3 month sample

  Dataset EGAD00001010586

• Targeted capture ctDNA Library CRCQV42Run035-6

  Targeted capture ctDNA Library CRCQV42Run035-6 from patient PBC0002406, plasma 3 month sample

  Dataset EGAD00001010589

• Targeted capture ctDNA Library CRCQV42Run043-15

  Targeted capture ctDNA Library CRCQV42Run043-15 from patient PBC0001470, plasma 3 month sample

  Dataset EGAD00001010644

• Targeted capture ctDNA Library CRCQV42Run043-16

  Targeted capture ctDNA Library CRCQV42Run043-16 from patient PBC0002255, plasma 3 month sample

  Dataset EGAD00001010645

• Targeted capture ctDNA Library CRCQV42Run044-11

  Targeted capture ctDNA Library CRCQV42Run044-11 from patient PBC0001515, plasma 3 month sample

  Dataset EGAD00001010661

• Targeted capture ctDNA Library CRCQV42Run044-12

  Targeted capture ctDNA Library CRCQV42Run044-12 from patient PBC0001550, plasma 3 month sample

  Dataset EGAD00001010662

• Targeted capture ctDNA Library CRCQV42Run044-13

  Targeted capture ctDNA Library CRCQV42Run044-13 from patient PBC0001665, plasma 3 month sample

  Dataset EGAD00001010663

• Targeted capture ctDNA Library CRCQV42Run044-14

  Targeted capture ctDNA Library CRCQV42Run044-14 from patient PBC0001667, plasma 3 month sample

  Dataset EGAD00001010664

• Targeted capture ctDNA Library CRCQV42Run044-23

  Targeted capture ctDNA Library CRCQV42Run044-23 from patient PBC0005498, plasma 3 month sample

  Dataset EGAD00001010673

• Targeted capture ctDNA Library CRCQV42Run045-19

  Targeted capture ctDNA Library CRCQV42Run045-19 from patient PBC0001311, plasma 3 month sample

  Dataset EGAD00001010690

• Targeted capture ctDNA Library CRCQV42Run045-15

  Targeted capture ctDNA Library CRCQV42Run045-15 from patient PBC0001714, plasma 3 month sample

  Dataset EGAD00001010686

• Targeted capture ctDNA Library CRCQV42Run045-21

  Targeted capture ctDNA Library CRCQV42Run045-21 from patient PBC0002651, plasma 3 month sample

  Dataset EGAD00001010692

• Targeted capture ctDNA Library CRCQV42Run045-23

  Targeted capture ctDNA Library CRCQV42Run045-23 from patient PBC0002481, plasma 3 month sample

  Dataset EGAD00001010694

• Targeted capture ctDNA Library CRCQV42Run045-24

  Targeted capture ctDNA Library CRCQV42Run045-24 from patient PBC0001409, plasma 3 month sample

  Dataset EGAD00001010695

• Targeted capture ctDNA Library CRCQV42Run046-24

  Targeted capture ctDNA Library CRCQV42Run046-24 from patient PBC0002494, plasma 3 month sample

  Dataset EGAD00001010715

• Targeted capture ctDNA Library CRCQV42Run046-4

  Targeted capture ctDNA Library CRCQV42Run046-4 from patient PBC0001776, plasma 3 month sample

  Dataset EGAD00001010716

• Targeted capture ctDNA Library CRCQV42Run046-5

  Targeted capture ctDNA Library CRCQV42Run046-5 from patient PBC0001242, plasma 3 month sample

  Dataset EGAD00001010717

• Short-read scRNA-seq

  10x Chromium Single Cell 3′ single-cell cDNA sequencing data for adrenal tissue sampleA and sampleB.

  Dataset EGAD50000002210

• Exome Sequencing

  Exome sequencing of for 10 patients: 10 tumors, 10 cell lines and 7 blood samples (for 3 patients blood was not available)

  Dataset EGAD00001002690

• Single-cell DNA-seq

  Single-cell DNA sequencing (scDNA-seq) data.

  Dataset EGAD00001008779

• Dataset for "Intratumoral Heterogeneity and Clonal Evolution Induced by HPV Integration" (ONT)

  Dataset for "Intratumoral Heterogeneity and Clonal Evolution Induced by HPV Integration" (ONT)

  Dataset EGAD00001009632

• Dataset for "Intratumoral Heterogeneity and Clonal Evolution Induced by HPV Integration" (pacBio)

  Dataset for "Intratumoral Heterogeneity and Clonal Evolution Induced by HPV Integration" (pacBio)

  Dataset EGAD00001009631

• Exome sequences of three individuals from the EXCEED study

  Exome sequences of three unrelated individuals of south Asian ancestry from the EXCEED study

  Dataset EGAD00001007649

• Genome-wide analysis of genetic risk factors for rheumatic heart disease in Aboriginal Australians provides support for pathogenic molecular mimicry

  Background. Rheumatic heart disease (RHD) following Group A Streptococcus (GAS) infections is heritable and prevalent in Indigenous populations. Molecular mimicry between human and GAS proteins triggers pro-inflammatory cardiac valve-reactive T-cells. Methods. Genome-wide genetic analysis was undertaken in 1263 Aboriginal Australians (398 RHD cases; 865 controls). Single nucleotide polymorphisms (SNPs) were genotyped using Illumina HumanCoreExome BeadChips. Direct typing and imputation was used to fine-map the human leukocyte antigen (HLA) region. Epitope binding affinities were mapped for human cross-reactive GAS proteins, including M5 and M6. Results. The strongest genetic association was intronic to HLA-DQA1 (rs9272622; P=1.86x10-7). Conditional analyses showed rs9272622 and/or DQA1*AA16 account for the HLA signal. HLA-DQA1*0101_DQB1*0503 (OR 1.44, 95%CI 1.09-1.90, P=9.56x10-3) and HLA-DQA1*0103_DQB1*0601 (OR 1.27, 95%CI 1.07-1.52, P=7.15x10-3) were risk haplotypes; HLA_DQA1*0301-DQB1*0402 (OR 0.30, 95%CI 0.14-0.65, P=2.36x10-3) was protective. Human myosin cross-reactive N-terminal and B repeat epitopes of GAS M5/M6 bind with higher affinity to DQA1/DQB1 alpha/beta dimers for the two risk haplotypes than the protective haplotype. Conclusions. Variation at HLA_DQA1-DQB1 is the major genetic risk factor for RHD in Aboriginal Australians studied here. Cross-reactive epitopes bind with higher affinity to alpha/beta dimers formed by risk haplotypes, supporting molecular mimicry as the key mechanism of RHD pathogenesis.

  Study EGAS00001002678

• Defining the metastasome in colorectal cancer: Implications for hypotheses on metastasis evolution and personalized therapy (HIPO-032)

  Personalized cancer therapy aims at individual genetic changes characterizing primary cancers. Still, however, metastasis is the major clinically unmet problem, also due to an incomplete understanding of its molecular evolution.This study is the first to define the metastasis-specific whole genome landscape of human colorectal primary vs. matched metastatic lesions.Protein coding, ncRNA genes and 3’UTRs harbored about a third each of single nucleotide variations (SNVs)/indels, 19% of them being metastasis-specific. We found novel mutational hills among ncRNAs and 3’UTRs, copy number changes able to explain changes in microRNA expression in metastases, and novel metastasis-specific mutations in the 3’UTRs on important cancer signalling genes. Some metastatic lesions showed targetable mutations not detected in the primaries. Analysis of mutual exclusivity patterns supported a model that has progressive alterations in important colorectal cancer genes and revealed new partners. Metastatic lesions were enriched in SNVs, specific structural variations, and alterations in genes affecting cell adhesion and extracellular matrix interaction.Furthermore, a hepatic stellate activation cascade was enriched in metastases, suggesting genetic programs for site-specific colonization. Allele frequency distribution and mutational signature analysis suggests a common ancestor clone to both the primary tumor and the metastasis, with additional mutagenesis ongoing in both sites independently. Taken together, our results significantly add to hypothesis generation on metastasis evolution, and suggest novel metastasis-specific genomic changes which would be missed by current personalized therapy concepts. (HIPO-032)

  Study EGAS00001002717

• Sequencing data for oesophageal and related samples - BOs release 2 (RNA)

  Dataset EGAD00001003840

• WGS of constitutional MLH1 epimutation carriers and non-carrier relatives

  This dataset contains chromosome 3 alignments. WGS was performed on genomic DNA isolated from peripheral blood cells.

  Dataset EGAD50000000710

• MD Anderson OPMD DAC

  Datasets and agreement used for the OPMD dataset access of the publication Reconstructing oral cavity tumor evolution through brush biopsy, Springer Nature, DOI: 10.1038/s41598-024-72946-3

  Dac EGAC50000000384

• HipSci - Bardet-Biedl Syndrome - RNA Sequencing - October 2016

  HipSci - Bardet-Biedl Syndrome - RNA Sequencing - October 2016

  Dataset EGAD00001003181

• HipSci - Bardet-Biedl Syndrome - Exome Sequencing - July 2017

  HipSci - Bardet-Biedl Syndrome - Exome Sequencing - July 2017

  Dataset EGAD00001003515

• WES HCC-neuro

  WES data of a HCC with neuroendocrine differentiation (HCC-NED), normal and organoid from a 74-year-old man.

  Dataset EGAD00001008430

• Finnish - THL Health2000 Dataset

  The dataset encompasses 208 Runs from the WGSPD Project 3 - Genomic Strategies to Identify High-impact Psychiatric Risk Variants Project

  Dataset EGAD00001008684

• Illumina HiSeq 2000

  Three files of patients 20, 23 and 25 with WGS done on Illumina HiSeq 2000. For research purpose and authorised user only.

  Dataset EGAD00001003438

• CP-NET Cram files

  WGS Cram files from the Childhood Cerebral Palsy Integrated Neuroscience Discovery Network "CP-NET" - Clinical Database Platforms - Phase 3 project.

  Dataset EGAD00001010014

• HipSci - Bardet-Biedl Syndrome - RNA Sequencing - July 2017

  HipSci - Bardet-Biedl Syndrome - RNA Sequencing - July 2017

  Dataset EGAD00001003531

• HipSci - Bardet-Biedl Syndrome - Exome Sequencing - October 2016

  HipSci - Bardet-Biedl Syndrome - Exome Sequencing - October 2016

  Dataset EGAD00001003161

• KiCS cancer panel academic only data

  This data set contains KiCS cancer panel data for academic and for-profit use.

  Dataset EGAD00001009733

• ParityImmune

  Blood transcriptome from women participating in the Norwegian Women and Cancer study (NOWAC)

  Dataset EGAD00010001412

• Additional Methylation files for Roussel-ATRT-TM

  Additional Methylation files for Roussel-ATRT-TM paper titled "Atypical teratoid/ rhabdoid tumoroids reveal subgroup-specific drug vulnerabilities"

  Dataset EGAD00010002495

• Whole Exome Sequencing

  Whole Exome Sequencing for a cohort of 20 B-ALL samples : 5 Down syndrome (DS), 7 Hyperdiploid (HeH), 3 iAMP21 and 5 others.

  Dataset EGAD00001005425

• Tumor-derived exosomes modulate PD-L1 expression in monocytes

  Small RNA expression profiles of the blood plasma-derived exosomes from B-cell chronic lymphocytic leukemia patients

  Dataset EGAD00001003230

• RNA data for MMML (EGAS00001002422)

  RNA sequencing data for MMML (3 tumor samples and 1 gcbcell)

  Dataset EGAD00001003285

• Organoid Derivation Project TGS: Release 3

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005958