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• Lung Cancer Genetic Study Among Asian Never Smokers

  The Lung Cancer Genetic Study Among Asian Never Smokers includes data from four population-based studies conducted among Asian women: the Shanghai Women's Health Study (SWHS), the Hwasun Cancer Epidemiology Study (HCES1 and HCES2), the Female Lung Cancer Consortium in Asia (FLCCA, including sFLCA), and the Guangzhou Lung Cancer Study (GLCS). Shanghai Women's Health Study (SWHS): The SWHS is a population-based prospective cohort study of approximately 75,000 adult women who were recruited between 1997 and 2000. The cohort has been followed by a combination of record linkage and active follow-ups. Lung cancer patients identified in the SWHS and non-cases were included in the current study.Hwasun Cancer Epidemiology Study-Lung Cancer (HCES1 and HCES2): The HCES is a hospital-based case-control study whose goal is to identify factors of the cancer development and clinical progression in a Korean population. Included in this project were 299 female lung cancer cases from HCES2, who were diagnosed between April 2004 and February 2013 at Chonnam National University Hwasun Hospital, a cancer specified hospital in Jeollanam-do province, South Korea. Controls were randomly selected from women with no previous cancer diagnosis at enrollment in the Namwon Study and the Dong-gu study, ongoing community-based cohort studies in South Korea. A total of 1,103 controls (273 from HCES1 and 830 from HCES2) were genotyped by MEGA. Guangzhou Lung Cancer Study (GLCS): The GLCS is a case-control study including never-smoking women lung cancer cases and controls from Guangzhou, China.In this study, the DNA samples of 1,195 subjects from SWHS and GLCS were genotyped using whole exome sequencing (WES), and the DNA samples of 7,174 subjects from SWHS, HCES1, HCES2, FLCCA, and sFLCA were genotyped using the Multi-Ethnic Global Array (MEGA).

  Study phs002366

• Tracing the origin of disseminated tumor cells in breast cancer using single-cell sequencing

  Background Single-cell micro-metastases of solid tumors often occur in the bone marrow. These disseminated tumor cells (DTCs) may resist therapy and lay dormant or progress to cause overt bone and visceral metastases. The molecular nature of DTCs remains elusive, as well as when and from where in the tumor they originate. Here, we apply single-cell sequencing to identify and trace the origin of DTCs in breast cancer. Results We sequence the genomes of 63 single cells isolated from six non-metastatic breast cancer patients. By comparing the cells DNA copy number aberration (CNA) landscapes with those of the primary tumors and lymph node metastasis, we establish that 53% of the single cells morphologically classified as tumor cells are DTCs disseminating from the observed tumor. The remaining cells represent either non-aberrant ‘normal’ cells or aberrant cells of unknown origin that have CNA landscapes discordant from the tumor. Further analyses suggest that the prevalence of aberrant cells of unknown origin is age-dependent, and that at least a subset is hematopoietic in origin. Evolutionary reconstruction analysis of bulk tumor and DTC genomes enables ordering of CNA events in molecular pseudo-time and traced the origin of the DTCs to either the main tumor clone, primary tumor subclones, or subclones in an axillary lymph node metastasis. Conclusions Single-cell sequencing of bone marrow epithelial-like cells, in parallel with intra-tumor genetic heterogeneity profiling from bulk DNA, is a powerful approach to identify and study DTCs, yielding insight into metastatic processes. A heterogeneous population of CNA-positive cells is present in the bone marrow of non-metastatic breast cancer patients, only part of which are derived from the observed tumor lineages.

  Study EGAS00001002102

• Multi-omic analysis of Down Syndrome in thyroid

  Background: Down syndrome (DS) is a chromosomal disorder characterized by trisomy of chromosome 21 and is associated with various health problems. Congenital thyroid dysfunction is a common endocrine abnormality in DS, but its exact cause remains unknown. This work aimed at studying the etiology of congenital thyroid dysfunction in DS. Methods: Thyroid tissue of fetuses with DS (n=4) and fetuses without a genetic/developmental abnormality (n=5) were analyzed using histopathological evaluation, RNA sequencing (RNA-seq), and DNA methylation (DNAm) profiling. Results: Histological examination showed abnormally developed DS thyroid tissue compared to non-DS/healthy tissue. RNA-seq analysis showed numerous differences in gene expression between DS and healthy thyroid tissue, including significant downregulation of thyroid genes FOXE1, IYD and DIO2. Gene set enrichment analysis showed disruption of vital cellular processes and functions in DS fetal thyroid tissue. Analysis of DNAm and expression quantitative trait methylation (eQTM) analysis identified functional and biologically relevant associations, that can be linked to plausible disrupted pathways in DS. Conclusions: Based on these observations, we conclude that congenital non-autoimmune thyroid dysfunction in DS is probably a DS-specific form of thyroid dysfunction, characterized by abnormal thyroid development, altered expression of genes that regulate thyroid development and hormone production, and altered expression and DNAm of genes pertaining to numerous vital pathways leading to significant metabolic disturbances. The disturbances as investigated by multi-omics analyses (RNA-seq, DNAm and eQTM analyses) are widely spread over the genome, and presumably caused by the direct and downstream effects of overexpressed chromosome 21 genes (dosage effect), and (epi)genomic disturbances.

  Study EGAS00001007677

• DAC of the STIC project

  Ovarian high-grade serous carcinoma (HGSC) originates in the fallopian tube, with secretory cells carrying a TP53 mutation, known as ‘p53 signatures’, identified as potential precursors. p53 signatures evolve into serous tubal intraepithelial carcinomas (STIC) lesions, which, in turn, progress into invasive HGSC that readily spread to the ovary and disseminate around the peritoneal cavity. We recently investigated the genomic landscape of early- and late-stage HGSC and found higher ploidy in late-stage (median 3.1) than early-stage (median 2.0) samples. Here, to explore whether the high ploidy and possible whole genome duplication observed in late-stage disease are determined early in the evolution of HGSC, we analysed archival formalin-fixed paraffin-embedded samples (FFPE) from five HGSC patients. p53 signatures and STIC lesions were laser-capture microdissected and sequenced using shallow whole genome sequencing (sWGS), while invasive ovarian/fallopian tube and metastatic carcinoma samples underwent macrodissection and were profiled using both sWGS and targeted next generation sequencing. Results showed highly similar patterns of global copy number change between STIC lesions and invasive carcinoma samples within each patient. Ploidy changes were evident in STIC lesions, but not p53 signatures, and there was strong correlation between ploidy in STIC lesions and invasive ovarian/fallopian tube and metastatic samples in each patient. The reconstruction of sample phylogeny for each patient from relative copy number indicated that high ploidy, when present, occurred early in the evolution of HGSC, which was further validated by copy number signatures in ovarian and metastatic tumours. These findings suggest that aberrant ploidy, suggestive of whole genome duplication, arises early in HGSC, and is detected in STIC lesions, implying that the trajectory of HGSC may be determined at the earliest stages of tumour development.

  Dac EGAC50000000227

• Alterations in the gut microbiome implicate key taxa and metabolic pathways across inflammatory arthritis phenotypes

  Musculoskeletal diseases affect up to 20% of adults worldwide. The gut microbiome has been implicated in inflammatory conditions, but large-scale metagenomic evaluations have not yet traced the routes by which immunity in the gut affects inflammatory arthritis. To characterize the community structure and associated functional processes driving gut microbial involvement in arthritis, the Inflammatory Arthritis Microbiome Consortium investigated 440 stool shotgun metagenomes comprising 221 adults diagnosed with rheumatoid arthritis, ankylosing spondylitis, or psoriatic arthritis and 219 healthy controls and individuals with joint pain without an underlying inflammatory cause. Diagnosis explained about 2% of gut taxonomic variability, which is comparable in magnitude to inflammatory bowel disease. We identified several candidate microbes with differential carriage patterns in patients with elevated blood markers for inflammation. Our results confirm and extend previous findings of increased carriage of typically oral and inflammatory taxa and decreased abundance and prevalence of typical gut clades, indicating that distal inflammatory conditions, as well as local conditions, correspond to alterations to the gut microbial composition. We identified several differentially encoded pathways in the gut microbiome of patients with inflammatory arthritis, including changes in vitamin B salvage and biosynthesis and enrichment of iron sequestration. Although several of these changes characteristic of inflammation could have causal roles, we hypothesize that they are mainly positive feedback responses to changes in host physiology and immune homeostasis. By connecting taxonomic alternations to functional alterations, this work expands our understanding of the shifts in the gut ecosystem that occur in response to systemic inflammation during arthritis.

  Dataset EGAD50000000567

• META-PRISM

  A pan-cancer cohort of 1031 patients resistant to systemic therapies or with no approved therapeutic options. It includes whole-exome sequencing of 571 tumor and matched-normal samples, and transcriptome sequencing of 947 tumor samples. Biopsies were taken at entry into precision medicine trials, often after diagnosed resistance. Comprehensive clinical information is available for all patients and include patient age at biospy, tumor primary site and histological subtype, biopsy site, treatments received prior to biopsy, blood assessment results at biopsy, metastatic sites at biopsy, and survival time from the biopsy date.

  Dataset EGAD00001009684

• TRACERx NSCLC, multiregion sequencing of the first 100 tumors

  BACKGROUND 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). METHODS Multiregion high-depth whole-exome sequencing (M-seq) was performed on 100 early stage NSCLC tumors resected prior to systemic therapy. A total of 327 tumor regions were sequenced and analyzed to define evolutionary histories, obtain a census of clonal and subclonal events, and assess the relationship between ITH and recurrence-free survival (RFS). RESULTS Widespread ITH was observed for both somatic copy number alterations (median 48% [0.03-88%]) and mutations (median 30% [0.5-93%]). Driver mutations in EGFR, MET, BRAF and TP53 were almost always clonal. However, heterogeneous driver alterations occurring later in evolution were found in over 75% of tumors and were common in PIK3CA, NF1 and genes involved in chromatin modification and DNA response and repair. Genome doubling and ongoing dynamic chromosomal instability (CIN), illustrated by mirrored subclonal allelic imbalance, were identified as causes of ITH resulting in parallel evolution of driver copy number events, including amplifications of CDK4, FOXA1, and BCL11A. Elevated copy number heterogeneity was associated with shorter RFS (HR=4.9, P=0.00044), which remained significant in a multivariate analysis. CONCLUSIONS ITH mediated through CIN, rather than point mutational heterogeneity, was associated with increased risk of relapse, supporting its value as a prognostic predictor, and the need to target this high-risk phenotype.

  Dataset EGAD00001003206

• Integrated clinical, whole genome, and transcriptome analysis of multisampled lethal metastatic prostate cancer

  We report the first combined analysis of whole genome sequence, detailed clinical history, and transcriptome sequence of multiple prostate cancer metastases in a single patient (A21). Whole genome and transcriptome sequence was obtained from 9 anatomically separate metastases, and targeted DNA sequencing was performed in cancerous and noncancerous foci within the primary tumor specimen removed 5 years prior to death. Transcriptome analysis revealed increased expression of AR-regulated genes in liver metastases that harbored an AR p.L702H mutation, suggesting a dominant effect by the mutation despite being present in only 1 of an estimated 16 copies per cell. The metastases harbored several alterations to the PI3K/AKT pathway, including a clonal truncal mutation in PIK3CG and present in all metastatic sites studied. The list of truncal genomic alterations shared by all metastases included homozygous deletion of TP53, hemizygous deletion of RB1 and CHD1, and amplification of FGFR1. If the patient were treated today given this knowledge, use of second-generation androgen-directed therapies, cessation of glucocorticoid administration, and therapeutic inhibition of the PI3K/AKT pathway or FGFR1 receptor could provide personalized benefit. Three previously unreported truncal clonal missense mutations (ABCC4 p.R891L, ALDH9A1 p.W89R, and ASNA1 p.P75R) were expressed at the RNA level and assessed as druggable. The truncal status of mutations is critical for actionability, and can only be determined through analysis of multiple sites of metastasis. Our findings suggest that a large set of deeply analyzed cases could serve as powerful guide to more effective prostate cancer basic science and personalized cancer medicine clinical trials.

  Dataset EGAD00001001869

• Study of Melanoma Risk in Australia and the United Kingdom

  The results from a small number of melanoma GWAS have been published. Initial studies identified several pigmentation- and nevus-associated loci that mediate an effect on melanoma risk, however, these studies were somewhat limited in that sample number (hence power) was low, they used first-generation low density SNP arrays, or used pools of DNA samples. Subsequently, additional melanoma GWAS have been completed, including a "Phase 2" study by the International Melanoma Genetics Consortium (GenoMEL), a full GWAS of the Australian sample resource previously used in a pooled-DNA GWAS, and study conducted through the MD Anderson Cancer Center and the Brigham and Women's Hospital. The groups who performed these studies have recently shared data and replicated a number of new melanoma susceptibility loci. Aside from the identification of novel loci, there are two additional notable outcomes of these studies. Firstly, an examination of the observed versus expected test statistics (Q-Q plot) from the Australian study, even after removing data from SNPs within known replicated susceptibility loci, reveals that there remains an excess of positive results. Similarly, the latest GenoMEL study finds almost three times as many SNPs reaching p-values between 10-4 and 10-5 as would be expected by chance. In the light of other studies of complex traits [1], these data suggest that studies based on additional melanoma samples from the same population are likely to identify an even larger "polygenic tail" and should prove invaluable in identifying and characterizing the underlying loci. A second observation, is that the effect size for replicated associations differs very little between GWAS. Given the strong effect of ultraviolet radiation (UVR) on risk of melanoma this might be expected to affect penetrance, particularly in the Australian and English samples, which are ethnically similar, but have very different sun exposures. We hypothesize that those SNPs whose effects are strongly mediated by UVR exposure may have smaller marginal effects on risk and are therefore harder to detect initially, suggesting a whole category of genetic effects that are unexplored.Indeed, most melanoma GWAS performed to date used samples from both European and Australian populations in discovery and or replication, and thus, existing studies have been poorly powered to detect associations with a wide difference in effect between these low- and high-UVR populations. Here, we wish to pursue this hypothesis further and plan to genotype additional cases drawn from the English and Australian populations. This will serve two complementary objectives: firstly, it will greatly increase our collective power to detect additional new melanoma risk alleles; secondly, it will highlight associations that are stronger specifically within the context of either low-UVR or high-UVR exposure environments. We posit that these data will be invaluable, not just for identifying novel melanoma-associated loci, but also for addressing gene-environment interactions. We have available a collection of valuable resources to help fulfill our aims. These include a considerable number of well-characterized and ungenotyped Australian and UK melanoma cases, as well as existing genotype data from sets of Australian and UK cases and population-based controls. We propose the following Specific Aims: To extend our current collective melanoma GWAS by genotyping a large number of additional UK and Australian cases. To jointly analyze these data and identify new melanoma risk loci. To determine whether pre-existing and new melanoma risk alleles show different degrees of association between the English and Australian populations, and assess whether these differences might be due to differences in sunlight exposure.

  Study phs000519

• TIGIT is the central player in T-cell suppression associated with CAR T-cell relapse in mantle cell lymphoma

  Study EGAS00001007113

• Elevated cfDNA after exercise is derived primarily from mature polymorphonuclear neutrophils, with a minor contribution of cardiomyocytes

  Study EGAS00001007157

• Effect of ETS2 modulation on chromatin accessibility and enhancer activity in inflammatory (TPP) macrophages

  This dataset consists of H3K27ac ChIP-seq and ATAC-seq in primary human macrophages following modulation of ETS2 expression. There are three datasets: H3K27ac ChIP-seq in ETS2-edited and unedited inflammatory (TPP) macrophages, H3K27ac ChIP-seq in ETS2-overexpressing and control M0 macrophages, and ATAC-seq in ETS2-edited and unedited inflammatory macrophages. For editing experiments, monocytes were transfected with CRISPR-Cas9 ribonucleoproteins containing a gRNA that was validated to edit ETS2, or a non-targeting control gRNA. Inflammatory macrophage differentiation was performed using conditions that model chronic inflammation (TPP): 3 days GM-CSF (50ng/mL) followed by 3 days GM-CSF, TNFa (50ng/mL), PGE2 (1mg/mL), and Pam3CSK4 (1mg/mL). For overexpression experiments, controlled overexpression of ETS2 mRNA or control mRNA (an equivalent amount of mRNA encoding the reverse complement of ETS2 – thereby controlling for the quantity, length and purine/pyrimidine composition of the transfected RNA but with a transcript that would not be translated) was induced in resting, non-activated (M0) macrophages by transfecting 500ng mRNA. To minimise non-specific activation due to the transfected RNA, in vitro transcription was performed using co-transcriptional capping (to minimise uncapped products), and incorporating modified, minimally immunogenic nucleotides (replacing uridine with N1-methyl-pseudouridine and cytidine with methylcytidine). After 18 hours, transfected cells were were activated with low dose LPS and harvested 6 hours later. For H3K27ac ChIP-seq, cells were harvested, cross-linked, quenched, lysed, and sheared. Immunoprecipitation of histone-DNA complexes was performed overnight at 4C with rotation using an anti-H3K27ac antibody and the SimpleChIP Plus Sonication ChIP kit (Cell Signaling Technology). Following reverse cross-linking, 50ng of immunoprecipitated DNA or input DNA were used to prepare sequencing libraries using the iDeal Library Preparation kit (Diagenode), according to manufacturer instructions. 10 PCR cycles were used for the amplification step and size selection was not performed. The quality and molarity of all libraries was assessed using a BioAnalyzer 2100 (Agilent) and the libraries were sequenced on an Illumina NovaSeq 6000. Raw data are provided as raw and aligned sequencing reads from H3K27ac-bound DNA and the input chromatin. ATAC-seq in TPP macrophages was performed using the Omni-ATAC protocol (Corces et al 2017) with the following modifications: cell number was increased to 75,000 cells, cell lysis time was increased to 5 minutes; volume of Tn5 transposase in the transposition mixture was doubled; duration of the transposition step was extended to 40 minutes. Sequencing libraries were prepared by PCR with the NEBNext Ultra II Q5Master Mix (NEB) as described previously (Corces et al 2017). Amplified libraries were cleaned using AMPure XP beads (Beckman Coulter). Sequencing reads were trimmed using TrimGalore! (Phred score 24), filtered to remove reads < 20 bp and aligned to the human genome (hg19) using Burrows-Wheeler Aligner (BWA). Aligned reads were converted to BAM files, sorted and indexed using SAMtools (Li et al, 2009). PCR duplicates and unmapped reads were removed, and the resulting BAM files were re-sorted and indexed. Raw data are provided as 100 bp paired-end Illumina reads from n = 3 donors for each experiment. BAM files are also provided.

  Study EGAS50000000109

• HIV exome pilot, exome data part 2 GRCh37_53

  exome sequence data for 25 HIV elite long term non-progressors and rapid progressors. Partial dataset (overlap with EGAD00001000047) of raw BAMs mapped to GRCh37_53.

  Dataset EGAD00001000087

• Oncotrack_450K_metastatic

  Oncotrack metastatic samples using 450K. The shared AF analysis files oncotrackDNAmAnalysis.R and oncotrackDNAmBetaScores.txt which were applied for both Oncotrack_450K_tumor (EGAD00010001162) and Oncotrack_450K_metastatic (EGAD00010001161) datasets are included on Oncotrack_450K_tumor (EGAD00010001162) dataset.

  Dataset EGAD00010001161

• iPSC Samples for EBiSC

  EBiSC Whole Genome Sequencing raw FASTQ

  Dataset EGAD00001003882

• iPSC alignment file (CRAM) for EBiSC

  EBiSC Whole Genome Sequencing processed CRAM

  Dataset EGAD00001003942

• WTCCC2 bacteraemia susceptibility replication samples

  A WTCCC2 project - replication study for bacteraemia susceptibility in 2518 individuals from Kenya, genotyped on the Illumina Immunochip chip.

  Dataset EGAD00010001509

• GCAT| SNParray coreSpain V1

  4988 samples issued from GCAT cohort, genotyped with MEGAex-Infinium Array, with data for Cr1-22. Plink files with QC but not imputed.

  Dataset EGAD00010001665

• GCAT| SNParray coreSpain V2

  4988 samples issued from GCAT cohort, genotyped with MEGAex-Infinium Array, with data for Cr1-22. Plink files with QC and imputed (SHAPEIT+IMPUTE).

  Dataset EGAD00010001664

• Richter Syndrome Methylation dataset

  Raw idat files for 90 RS + DLBCL + CLL samples.

  Dataset EGAD00010002194

• P50_P76_P763_DAC_DMSO_24h_48h_72h_Illumina_HT12v4_Gene_Expression.xlsx

  Expression dataset for DAC treated CD34 sorted primary CML bone marrow samples

  Dataset EGAD00010002211

• DNA-Methylation data for atypical teratoid/rhabdoid tumoroids study

  We demonstrate that ATRT tumoroids retain subgroup-specific epigenetic and gene expression profiles

  Dataset EGAD00010002400

• Hostage_2_genotype

  Cohort: Raw genotype files for Hostage2 cohort. Genotype Chip: Illumina’s (Illumina Inc., San Diego, U.S.) Global Screening Array-24 Multi Disease (GSA) Version 2.0 B1 genomic build: b37

  Dataset EGAD00010002171

• Hostage_3_genotype

  Cohort: Raw genotype files for Hostage3 cohort. Genotype Chip: Illumina’s (Illumina Inc., San Diego, U.S.) Global Screening Array-24 Multi Disease (GSA) Version 2.0 B1 genomic build: b37

  Dataset EGAD00010002173

• Hostage_4_genotype

  Cohort: Raw genotype files for Hostage4 cohort. Genotype Chip: Illumina’s (Illumina Inc., San Diego, U.S.) Global Screening Array-24 Multi Disease (GSA) Version 2.0 B1 genomic build: b37

  Dataset EGAD00010002178

• Hostage_1_genotype

  Cohort: Raw genotype files for Hostage1 cohort. Genotype Chip: Illumina’s (Illumina Inc., San Diego, U.S.) Global Screening Array-24 Multi Disease (GSA) Version 2.0 B1 genomic build: b37

  Dataset EGAD00010002180

• OncoScan SNP data set for systemic follicular lymphoma (sFL)

  122 unpaired initial tumor samples (122 FFPE samples) of sFL patients measured with OncoScan SNP microarrays, Affymetrix CEL intensity data file types (Thermo Fisher Scientific, Waltham, Massachusetts, USA)

  Dataset EGAD00010002592

• T and NK cell PBMC metadata

  Cell annotation for single T and NK cells of PBMC collected from 113 patients, including UMAP coordinates and annotation.

  Dataset EGAD50000000140

• WGS and WXS for mismatch repair-deficient human colon organoids

  Whole genome/exome sequencing to detect spontaneous acquired mutations in mismatch repair-deficient human colon organoids.

  Dataset EGAD50000000159

• Genotyping data of 81 patients with multiple sclerosis and other neurological diseases

  This dataset consists of raw unimputed genotype data for 81 individuals with multiple sclerosis (n=33) and other neurological disease (n=48).

  Dataset EGAD50000000183

• GSA reference

  reference whole exome sequence serving as a reference of individuals. Includes the raw GSa files and the called variants in vcf format merge for all individuals (S1-S5).

  Dataset EGAD50000000481

• bfast CohortD OAPL

  ctDNA mutation calls (one mutation per sample per line) for BFAST Cohort D including mutation-level data like coding change, allele frequency, etc.

  Dataset EGAD50000000148

• Dataset for NSCLC samples

  This dataset contains 10x scRNA sequencing data of 16 NSCLC samples. Sequencing was performed on Illumina HiSeq 4000. The sequencing was always paired.

  Dataset EGAD50000000185

• Reconstruction oral cavity tumor evolution through brush biopsy

  A dataset of samples analyzed for the publication "Reconstructing oral cavity tumor evolution through brush biopsy", Springer Nature, DOI: 10.1038/s41598-024-72946-3

  Dataset EGAD50000000856

• RNAseq data

  Bulk RNAseq data for IMbrave151 (N = 96), including raw counts, transcript per million (TPM), and voom transformed values.

  Dataset EGAD50000000568

• Altered Blood and Keratinocyte microRNA/transfer RNA Fragment Profiles Related to Fibromyalgia Syndrome and its Severity

  We show dysregulated microRNA and tRNA fragment profiles related to FMS pathopyhsiology and opening new perspectives for FMS diagnostics and symptom monitoring.

  Dac EGAC50000000247

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0187_002

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0167_003 for Diffuse large B-cell lymphoma patient sample TFRIPAIR10

  Dataset EGAD50000001372

• ctDNA sample level analysis

  This table summarizes Guardant 360 ctDNA variant summary statistics including frequency of variant types for each sample included in this analysis.

  Dataset EGAD50000001341

• Raw RNA sequencing of hepatoblastoma PDX cell line HB-303-LEF

  This dataset contains raw RNA sequencing data for hepatoblastoma PDX cell line HB-303-LEF

  Dataset EGAD50000001358

• Sequencing data for oesophageal and related samples - cell-lines (WGS)

  Dataset EGAD00001015467

• McGill Reproductive Genetics Data Access Committee

  Committee at RI-MUHC (Slim Lab) that reviews requests for controlled human genetic/phenotypic data generated in reproductive genetics/infertility studies. Ensures requests comply with participant consent, ethics approvals, and institutional policies.

  Dac EGAC50000000775

• NICOLA Epigenetic Data Wave 1 - Beta values

  Beta values generated from Illumina EPIC methylation array data for a subset of NICOLA Wave 1 samples.

  Dataset EGAD50000001965

• Transcriptomics for UPST-SCCHN3 cohort

  Transcriptomics of Tumor samples on UPST-SCCHN3 cohort

  Dataset EGAD50000002179

• ATAC_TALL_t_14_16_translocation

  ATAC-sequence for patients with t(14;16) translocated T-ALL and control T-ALL patients

  Dataset EGAD50000001784

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0169_001

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0169_001 for Follicular lymphoma patient sample TFRIPAIR4_FL

  Dataset EGAD00001010217

• NICHE - DNA-seq of MMR proficient and MMR deficient early stage colon cancers

  The dataset includes 174 FASTQ files from paired-end WXS sequencing on Illumina HiSeq2500 for 39 patients.

  Dataset EGAD00001006041

• RnaSeq samples for 25 UPS samples

  Whole Transcriptiome Rnaseq of 25 UPS samples - raw FastQ sequences, 125x2 nc Paired End Reads, min 30M PE, HiSEq technology

  Dataset EGAD00001006355

• HV31 - MGI standard short-read sequencing

  MGI standard short-read sequencing data for individual HV31 generated using DNA from peripheral blood mononuclear cells, to a sequencing depth of ~57×.

  Dataset EGAD00001007044

• CBD-RAW-CLINVAR: Clinical metadata

  Anonymised patient metadata and associated data dictionary. For further information regarding this dataset, please contact Alexander Mentzer at contact@combat.ox.ac.uk.

  Dataset EGAD00001007931

• RNA-seq data for de-methylation of FOXP3-TSDR study

  22 RNA-seq samples of ex-vivo (TN and Treg), cultured Treg, TET1 and untreated mCherry-MOCK

  Dataset EGAD00001006865