9182 results for "cancer rna-seq"

in 40.45 milliseconds.

• Thoracic Patient-Derived Xenografts

  Targeted capture sequencing of 341 cancer associated genes in thoracic patient-derived xenografts.

  Study phs001192

• PanCuRx Translational Research Initiative

  The goal of the PanCuRx TRI is to seek solutions to the high fatality rate of pancreatic adenocarcinoma (PDAC), the most common type of pancreatic cancer, by generating new knowledge about the genetics and biology of the disease, mechanisms of how tumours grow and tailored treatment options. PDAC is the fourth leading cause of cancer death in Canada and is expected to become the second-leading cause of cancer death within the next decade. The current five-year survival rate of eight per cent is the lowest of all epithelial cancers. There are major clinical challenges to treating the disease, including the fact that PDAC spreads to other parts of the body early so surgical removal of the tumour benefits few patients, that PDAC has proven relatively resistant to systemic therapies such as chemotherapy and that there has been limited benefit from the use of newer molecular targeted agents. The PanCuRx team has created the world’s first resource of tumour-purified PDAC whole genomes; more than 500 PDAC samples, with combined clinical annotation, have been successfully sequenced and analyzed at the Ontario Institute for Cancer research in Toronto. The resulting clinical genomic resource created from this data has led to multiple new findings that have established a framework to better understand PDAC biology and lays the foundation for new genomic approaches to better understanding the disease. The team also launched a clinical trial called COMPASS (Comprehensive Molecular Characterization of Advanced Ductal Pancreas Adenocarcinoma for Better Treatment Selection: A Prospective Study), which is recruiting patients with advanced PDAC (both locally advanced and metastatic) at The Princess Margaret Cancer Centre (PM) in Toronto. The study uses the molecular results from individual patient tumours to guide selection of better second line treatment on a case-by-case basis. The primary objective of the initial study was to assess the feasibility of collecting tumour materials from patients with advanced PDAC and delivering results within 56 days of biopsy. Having achieved this objective, the trial has expanded to other cancer centres across Canada. Pancreatic cancer is one of the most deadly forms of cancer and outcomes have not improved in four decades. The PanCuRx initiative is providing a better understanding of the disease and actively applying new knowledge to cancer patients through clinical trials. This knowledge will help to improve quality of life and prolong survival for pancreatic cancer patients worldwide.

  Study EGAS00001002543

• Identification of drug resistance genes in cancer cell lines by insertional mutagenesis

  Our aim is to identify genes involved in resistance to anti-cancer therapies. In order to do this we have taken advantage of a lentiviral vector (LV)-based insertional mutagen to mutagenize cancer cell lines. LV-transduced cell lines were then treated with anti-cancer therapies and the emergence of resistant clones scored. DNA from pools of resistant clones was collected, subjected to custom capture by baits designed against the LV sequence, and then sequenced to identify the LV-genomic junction. We hope that the identification of recurrently targeted genes in resistant cell population will allow us to identify genes that mediate drug resistance.

  Dataset EGAD00001002207

• AVENIO Expanded ctDNA panel sequence alignments (BAMs) from plasma DNA of BC, CRC and NSCLC patients

  The AVENIO ctDNA Expanded Kit is a next-generation sequencing (NGS) liquid biopsy assay with a 77 gene panel (192 kb) containing genes in U.S. National Comprehensive Cancer Network (NCCN) Guidelines and emerging cancer biomarkers. This pan-cancer assay was applied to 48 plasma samples from patients with breast, colorectal and non-small cell lung cancer. After sequencing 150bp paired-end, reads were aligned to the hg38 genome with the AVENIO Oncology Analysis Software (version 2.0). These files are the deduplicated alignments generated by the analysis software used for subsequent variant, indel and CNV calling.

  Dataset EGAD00001006301

• CALGB 80303:Genome-Wide Association Study of Advanced Pancreatic Cancer Patients - A Randomized Phase III trial of Gemcitabine Plus Bevacizumab versus Gemcitabine Plus Placebo in Patients with Advanced Pancreatic Cancer

  CALGB 80303 was a randomized, phase III study in advanced pancreatic cancer patients treated with Gemcitabine plus either Bevacizumab or placebo. No difference in overall survival (OS) was observed between the two arms. We prospectively collected germline DNA and conducted a genome-wide association study (GWAS).

  Study phs000250

• Integrative Analysis of Lung Adenocarcinoma in Never Smokers

  We performed whole genome sequencing of lung cancers from never smoker subjects from Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval (IUCPQ-UL) (N=125), Université Côte d'Azur (Nice) (N=47), the Environment And Genetics in Lung cancer Etiology (EAGLE) study (N=27), Yale University (N=22), and Moffitt Cancer Cente (N=11).

  Study phs001697

• Whole Genome Scan for Pancreatic Cancer Risk in the Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case-Control Consortium (PanScan)

  Within the framework of the NCI-sponsored Cohort Consortium, investigators from 12 prospective epidemiologic cohorts formed the Pancreatic Cancer Cohort Consortium in 2006. This study, also known as "PanScan", is funded by the National Cancer Institute (NCI) and involves conducting a genome-wide association study (GWAS) of common genetic variants to identify markers of susceptibility to pancreatic cancer. In 2007, the study was expanded to include 8 case-control studies. The study team includes scientists from the cohorts comprising the Consortium, the NCI and the Pancreatic Cancer Case Control Consortium (PanC4). PanScan I and II were conducted in 12 cohort studies and 8 case-control studies, leading to the discovery of four novel regions in the genome associated with risk for pancreatic adenocarcinoma. The third phase of PanScan (PanScan III) was conducted using recently identified incident pancreatic cancer cases drawn from fourteen cohorts from the cohort consortium, including nine prospective cohorts who participated in PanScan I, and five newly joined cohorts. The nine cohort studies that participated in PanScan I and had new genotyping of cases in PanScan III include ATBC, CPS-II, EPIC, HPFS, NHS, PHS, PLCO, SMWHS, and WHI; the five newly joined cohort studies include the Agricultural Health Study (AHS), the Multiethnic Cohort Study (MEC), the Melbourne Collaborative Cohort Study (MCCS), the Vitamins and Lifestyle Study (VITAL), and Selenium and Vitamin E Cancer Prevention Trial (SELECT). In addition to the cases from cohorts, we also included cases from the Gastrointestinal Cancer Clinic of Dana-Farber Cancer Institute Study (DFCI-GCC); from the University Hospital in Heidelberg, Germany, which is part of a larger European clinical case-control study (PANDoRA); and from clinic-based cases from eastern Spain (PANKRAS-II). The dbGaP datasets available include all subjects previously made available from PanScan I and II, plus 1,582 new incident pancreatic cancer cases of European descent from prospective cohorts, case-control studies or case series (genotyped as part of PanScan III). Also included are 61 pancreatic cancer cases and 67 control subjects from PanScan I as well as 173 pancreatic cancer cases from PanScan III of Asian ancestry from the Shanghai Men's and Women's Health Study (Supplemental Table 10, Wolpin et al. (Nat Genet, 2014)). The control population used in the analysis for the Wolpin et al. manuscript included cancer-free individuals from the prospective cohorts that contributed pancreatic cancer cases to PanScan III and controls from the Spanish Bladder Cancer SBC/EPICURO study that were previously genotyped using the OmniExpress, Omni 1M or Omni 2.5M SNP arrays. The data from these control subjects were posted to dbGaP under the GWAS in which they were initially genotyped and will not be made available in duplicate under this dbGaP study.The summary statistics for PanScan I-III were generated as detailed in Wolpin BM. et al., Genome-wide association study identifies multiple susceptibility loci for pancreatic cancer, Nature Genetics 2014; 46(9):994-1000 (https://www.nature.com/articles/ng.3052), and Klein, A. et al., Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer, Nature Communications, 2018;9(1):556 (https://www.nature.com/articles/s41467-018-02942-5). The dataset includes results from an association study of 5,117 individuals diagnosed with pancreatic ductal adenocarcinoma (PDAC) and 8,845 control individuals, or a total of 13,962 subjects of European ancestry (as compared to the genotype and phenotype information under this project that includes 9,437 individuals (PanScan I and II PDAC case and control individuals and PanScan III PDAC cases only). This is due to the fact that PanScan III “borrowed” GWAS data from control individuals genotyped separately from the PanScan GWAS project and are therefore not included as raw genotypes in phs000206.v5.p3. Association analysis was performed separately for PanScan I-II and PanScan III, followed by a meta-analysis of the two datasets. Results were filtered based on a minor allele frequency (MAF) < 0.01, an imputation INFO score < 0.3 and a heterogeneity P-value < 1x10-10 leaving a total of 9,758,390 variants. Columns in the summary statistics dataset are as follows: ID: variant rsIDChr: chromosome numberPosition: position in the chromosome, genome build GRCh37/hg19MarkerName: variant identifierAllele1: reference alleleAllele2: alternative alleleFreq1: allele frequency for allele2FreqSE: standard error of the allele frequencyMinFreq: the minimal allele frequency across studiesMaxFreq: the max allele frequency across studiesEffect: effect size for allele2StdErr: standard errorP-value: meta-analysis p-valueDirection: summary of effect direction for each studyHetISq: I^2 statistic which measures heterogeneity on scale of 0-100%HetChiSq: chi-squared statistic in simple test of heterogeneityHetDf: degrees of freedom for heterogeneity statisticHetPVal: P-value for heterogeneity statistic

  Study phs000206

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: pancreas (2019-04-03)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute (Illumina HiSeqX, 40X and 20X depth respectively). Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004893

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Colorectal_LCM (2020-02-20)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2020-02-20.

  Dataset EGAD00001005994

• Research on the identification of cancer stem cells for peidatric and adult malignancies.

  In recent years, "cancer stem cells" have been reported to exist in many cancers. Many reports have shown that they are the main cause of cancer recurrence. The aim of this study is to test whether cancer stem cells can be identified using patient samples. We will perform omics analysis of rare pediatric cancers by integrating genomics, transcriptomics, proteomics and metabolomics, including somatic and germline mutations, to deepen our understanding of the biological characteristics of the target disease. Based on the knowledge gained and clinical information from patients, we will conduct preclinical pharmacological studies using mouse models and in vitro models to investigate the therapeutic effects of novel therapeutic agents and novel therapeutic strategies such as cellular therapies.

  Study JGAS000623

• "Single nucleotide variant detection in multiple foci of three prostate cancer tumors"

  "Single nucleotide variant detection in multiple foci of three prostate cancer tumors"

  Dataset EGAD00001000035

• "Copy number variant detection in multiple foci of three prostate cancer tumors"

  "Copy number variant detection in multiple foci of three prostate cancer tumors"

  Dataset EGAD00001000036

• ALK inhibitors in the context of ALK-dependent cancer cell lines

  ALK inhibitors in the context of ALK-dependent cancer cell lines

  Dataset EGAD00001000078

• Matched breast cancer fusion gene study

  Matched breast cancer fusion gene study

  Dataset EGAD00001000097

• Cancer-associated genome-wide hypomethylation and copy number aberrations

  Noninvasive detection of cancer-associated genome-wide hypomethylation and copy number aberrations by plasma DNA bisulfite sequencing

  Dataset EGAD00001001093

• Amplicon sequencing data from organoids of colorectal cancer patients.

  The dataset includes 13 bam files. Each bam file is a different colorectal cancer patient organoid.

  Dataset EGAD00001004313

• Ovarian cancer organoid biobank - followup

  WGS of more samples in the ovarian cancer organoid biobank dataset.

  Dataset EGAD00001005707

• Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring

  Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring

  Dataset EGAD00001006237

• KiCS cancer panel academic only data

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

  Dataset EGAD00001009733

• Geographic variation of mutagenic exposures in kidney cancer genomes – filtered vcf files (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – filtered vcf files (Mutographs)

  Dataset EGAD00001012222

• MutWP4__CRUK_Grand_Challenge_Mutographs_of_Cancer__Pancreatic_Organoids

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001003520

• PDAC organoid genomic heterogeneity

  The establishment of patient-derived pancreatic cancer organoid culture in recent years creates an exciting opportunity for researchers to perform a wide range of in vitro studies on a model that closely recapitulates the tumor. Among the outstanding questions in pancreatic cancer biology are the causes and consequences of genomic heterogeneity observed in the disease. However, to use pancreatic cancer organoids as a model to study genomic variations, we need to first understand the degree of genomic heterogeneity and its stability within organoids. Here, we used single-cell whole-genome sequencing to investigate the genomic heterogeneity of two independent pancreatic cancer organoids, as well as their genomic stability with extended culture. Clonal populations with similar copy number profiles were observed within the organoids, and the proportion of these clones was shifted with extended culture, suggesting the growth advantage of some clones. However, sub-clonal genomic heterogeneity was also observed within each clonal population, indicating the genomic instability of the pancreatic cancer cells themselves. Furthermore, our transcriptomic analysis also revealed a positive correlation between copy number alterations and gene expression regulation, suggesting the “gene dosage” effect of these copy number alterations that translates to gene expression regulation.

  Study EGAS00001006782

• NCI Expanded Genome-Wide Association Study of Renal Cell Carcinoma

  The National Cancer Institute (NCI) expanded genome-wide association study (GWAS) of renal cell carcinoma (RCC) was a 2nd GWAS of this cancer performed by NCI to further investigate common genetic variants associated with RCC risk. The GWAS includes 2,781 cases and 2,940 controls of European background from 13 studies conducted in the US and Finland (10 cohorts, 2 case series, 1 hospital biorepository), scanned using the Illumina OmniExpress and Omni2.5 SNP arrays. SNP-level results from this GWAS were combined by meta-analysis with results from the earlier NCI RCC GWAS (phs000351.v1.p1; 1,311 cases and 3,424 controls) and others conducted by the International Agency for Research on Cancer (scan 1: 2,438 cases and 5,071 controls; scan 2: 2,781 cases and 2,940 controls), The University of Texas MD Anderson Cancer Center (893 cases and 556 controls) and the Institute for Cancer Research, UK (944 cases and 4,024 controls). Only data from the second NCI scan are included in this dbGaP submission. Data from 239 cases and 229 controls from this scan will be deposited in dbGaP separately under the Women's Health Initiative (phs000200).

  Study phs001736

• Dataset for neuroendocrine_adrenal_tumor-RNA

  Rare cancer sequencing data of 145 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008862

• Single cell epigenomic study of H3-K27M mutant diffuse midline glioma across age and location

  Histone 3 lysine27-to-methionine (H3-K27M) mutations most frequently occur in childhood diffuse midline gliomas (DMGs) of the pons, but are also increasingly recognized in adult DMGs. Their potential heterogeneity at different ages and midline locations are vastly understudied. Here, through dissecting the single-cell epigenomic architectures of a comprehensive cohort of patient H3-K27M DMGs, we delineate how age and anatomical location shape glioma cell-intrinsic and -extrinsic features in light of the shared driver mutation. We analyzed the open chromatin profiles of 8 tumors utilizing the single-cell/nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq)

  Study EGAS00001007035

• panel data of ALK-positiv non-small cell lung cancer patients

  270 samples with ALK-positiv non-small cell lung cancer, targeted sequencing (198 kb panel size)

  Dataset EGAD00001006297

• Sequence of breast cancer bone metastases PDX from 2 targeted panels

  Sequence of breast cancer bone metastases PDX obtained by 2 targeted panels

  Dataset EGAD00001010856

• Geographic variation of mutagenic exposures in kidney cancer genomes – patient metadata files (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – patient metadata files (Mutographs)

  Dataset EGAD00001012223

• Geographic variation of mutagenic exposures in kidney cancer genomes – copy number variants (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – copy number variants (Mutographs)

  Dataset EGAD00001013727

• Defining structural variation associated with breast cancer susceptibility by long-read genome sequencing

  Defining structural variation associated with breast cancer susceptibility by long-read genome sequencing

  Dataset EGAD50000000283

• Primary Lung Cancer whole genome study

  Genomic libraries (500 bps) will be generated from total genomic DNA derived from lung cancer patients and subjected to short paired end sequencing on the llumina platform. Paired reads will be mapped to build 37 of the human reference genome to facilitate the generation of genome wide copy number information, and the identification of novel rearranged cancer genes and gene fusions.

  Dataset EGAD00001000388

• Whole-genome sequencing of paired tumor and blood samples from 65 bladder cancer patients

  65 paired tumor and normal whole-genome sequencing samples from urothelial bladder carcinomas (UBC, the most common type of bladder cancer) are used to uncover the whole-genome mutational landscape of UBC. Recurrent mutations in noncoding regions affecting gene regulatory elements and structural variations leading to gene disruptions are prevalent in this type of cancer.

  Dataset EGAD00001004545

• Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer

  The dataset for Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer includes 106 bam files from whole exome next-generation sequencing on the Illumina HiSeq2500. The samples analyzed include matched tumor/normal samples from non-small cell lung cancer patients treated with immunotherapy.

  Dataset EGAD00001005796

• Whole exome and targeted sequencing of non-muscle-invasive bladder cancer

  Tumor-blood paired whole-exome sequencing of 58 pairs of non-muscle-invasive bladder cancer samples (stageT1). Targeted sequencing of 112 non-muscle-invasive bladder cancer samples (34 stage T1; 78 stage Ta) Please note the following files have been removed: EGAR00003025153, EGAR00003025435, EGAR00003025294, EGAR00003025262, EGAR00003025224.

  Dataset EGAD00001008332

• Y_chromosome_mis_segregation_in_the_DLD_1_cell_line

  Study of the well-established DLD-1 colorectal cancer cell line, engineered with an inactivatable Y centromere that leads to mis-segregation. The goal is to gain insights into the mechanism of chromothripsis due to chromosome mis-segration.

  Study EGAS00001002551

• Transcriptome_Sequencing_of_Cancer_Cell_Lines

  In this study we will sequence the transcriptome of Verified Cancer Cell lines. This will be married up to whole exome and whole genome sequencing data to establish a full catalog of the variations and mutations found.

  Study EGAS00001000261

• Breast_Cancer_Matched_Pair_Cell_Line_Whole_Genomes

  We propose to definitively characterise the somatic genetics of matched pair breast cancer cell lines through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Study EGAS00001000166

• TRACERx Reduced-representation bisulfite sequencing (RRBS)

  RRBS data from TRACERx non-small cell lung cancer (NSCLC) tumours and matched normal adjacent tissue. 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).

  Dataset EGAD00001009707

• Colorectal cancer transcriptomics with primary and metastatic samples

  We performed RNAseq analysis of metastatic colorectal cancer from 16 patients. Normal adjacent tissue, primary tissue, and metastatic tissue were analyzed to uncover metastatic progression and difference between primary and distant metastatic lesions.

  Study EGAS00001006464

• Colorectal cancer genomics with primary and metastatic samples

  We performed whole genome sequencing of metastatic colorectal cancer from 16 patients. Normal adjacent tissue, primary tissue, and metastatic tissue were analyzed to uncover metastatic progression and difference between primary and distant metastatic lesions.

  Study EGAS00001006465

• Breast Cancer PDTX Encyclopaedia

  Molecular data from the collection of PDTX breast cancer models described in Bruna et al, 2016. Cell. It includes whole exome sequencing, shallow whole genome sequencing, expression arrays and reduced bisulfite representation sequencing (RRBS).

  Study EGAS00001001913

• Triple_Negative_Breast_Cancer_Whole_Genomes

  We propose to definitively characterise the somatic genetics of Triple negative breast cancer through generation of comprehensive catalogues of somatic mutations in 500 cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Study EGAS00001000092

• Paired-end Whole Exome-seq analysis of multi-centric lower grade glioma

  This dataset includes Nimblegen SeqCap EZ Exome v3 data for each lesion of three patients with multicentric glioma. For two patients, each lesion was sequenced along with whole blood. For a third patient, 3 pieces from the right lesion and 4 pieces from the left were sequenced along with whole blood. In each case BAM files that have been aligned with BWA mem alignment are available.

  Dataset EGAD00001003795

• Dataset GBM 2022

  The cellular origin and differentiation status of glioblastoma by scRNA-seq

  Dataset EGAD00001008745

• Epigenomic analysis of human dopaminergic neuron differentiation reveals LBX1, NHLH1 and NR2F1/2 as necessary for lineage specification

  Sample information: The 56 samples produced in this project come from the human iPSC line GM17602 (Coriell) where a tyrosine hydroxylase-T2A-mCherry reporter was inserted. A combination of epigenetic analysis (ATAC/ChIP) along transcriptomics.

  Dataset EGAD00001009288

• Standard epigenome mapping in human epithelial cells of the digestive and urogenital organs [ ChIP-Seq ]

  The aim of this study is to participate in International Human Epigenome Consortium (http://ihec-epigenomes.org/), through disclosure of quality reference epigenome profiles in normal and diseased cells obtained from multiple Japanese people.

  Study JGAS000079

• Standard epigenome mapping in human epithelial cells of the digestive and urogenital organs [BS-Seq ]

  The aim of this study is to participate in International Human Epigenome Consortium (http://ihec-epigenomes.org/), through disclosure of quality reference epigenome profiles in normal and diseased cells obtained from multiple Japanese people.

  Study JGAS000078

• Standard epigenome mapping in human epithelial cells of the digestive and urogenital organs [ ChIP-Seq ]

  The aim of this study is to participate in International Human Epigenome Consortium (http://ihec-epigenomes.org/), through disclosure of quality reference epigenome profiles in normal and diseased cells obtained from multiple Japanese people.

  Study JGAS000165

• Standard epigenome mapping in human epithelial cells of the digestive and urogenital organs [BS-Seq ]

  The aim of this study is to participate in International Human Epigenome Consortium (http://ihec-epigenomes.org/), through disclosure of quality reference epigenome profiles in normal and diseased cells obtained from multiple Japanese people.

  Study JGAS000082

• sn-RNAseq profiling of the impact of a cytokine storm model in human cardiac organoids

  We developed conditions to model conditions of diastolic dysfunction caused by inflammatory factors in human cardiac organoids. Our organoid model is based on conditions promoting maturation (Mills et al., PNAS, 2017 and Voges et al., In Revision) and incorporating human pluripotent stem cell derived cardiomyocytes, epicardial cells, fibroblasts, endothelial cells and pericytes. Following 15 days of differentiation (Voges et al., Development, 2017) we formed human cardiac organoids incorporating both our multicellular differentiation (Mills et al., PNAS, 2017) and additional endothelial cells (Voges et al., In Revision). After another 12 days of maturation in 5 days maturation medium and 5 days weaning medium (Voges et al., In Revision) human cardiac organoids were either treated with weaning medium or “cardiac cytokine storm” comprised of 100 ng/ml IFNg, 10 ng/ml IL-1 β and 10 µg/ml poly(I:C). After 48 hours human cardiac organoids were harvested and snap frozen in -80C before nuclei isolation and single nuclei RNA sequencing.Pooled hCO (~40) were homogenized in 4 mL lysis buffer (300 mM sucrose, 10 mM Tris-HCl (pH = 8), 5 mM CaCl2, 5 mM magnesium acetate, 2 mM EDTA, 0.5 mM EGTA, 1 mM DTT)(all Sigma-Aldrich) with 30 strokes of a dounce tissue grinder (Wheaton). Large pieces of hCO were allowed to settle and homogenate was passed through pre-wetted 40 µm cell strainers (Becton Dickinson). Remaining hCO material in the douncer was resuspended in 4 mL and the douncing and filtering steps were repeated twice. All steps of the homogenization were performed on ice. The filtered homogenate was centrifuged at 1500 x g for 5 min at 4oC. Nuclei pellets were then re-suspended in PBS. A fraction of resuspended nuclei were then stained with hoechst nuclear stain (1:500 dilution) and counted on a haemocytometer under fluorescent microscope.The nuclei were then re-centrifuged (1500 x g for 5 min at 4°C) and resuspended at a density to load ~5,000 nuclei per sample. Cell were loaded into the Chromium Controller (10X Genomics) for gel bead emulsion (GEM) formation. Library preparation was conducted according to the manufacturer’s recommended protocol using the Chromium Next GEM Single Cell 3’ GEM, Library & Gel Bead Kit v3.1. Libraries were sequenced on the NextSeq 500/550 v2 (Illumina) with 150 bp reads.In the snRNA-seq we note the lower than expected percentage of non-myocytes and the loss of endothelial cells (Mills et al., PNAS, 2017 and Voges et al., In Revision), indicating the protocol requires further optimization for hCO samples.

  Study EGAS00001005174