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• DAC for pediatric AML genomic sequences

  DAC for dataset containing matched tumor-healthy genomic sequences from patients with pediatric AML

  Dac EGAC50000000666

• DAC for DLBCL dataset

  Data Access Committee for cfDNA sequencing dataset from 290 samples from patients with DLBCL and HGBL.

  Dac EGAC50000000257

• DAC for the access to IMMUcan data

  This DAC will assess the requests for the access to IMMUcan data

  Dac EGAC50000000829

• Nrf2 transcript alterations

  WGS and RNA-seq data for Nrf2 study

  Study EGAS00001001740

• Metastatic_Prostate_Follow_Up_2

  Validation/deeper sequencing for metastatic prostate cancer samples

  Study EGAS00001000756

• Metastatic_Prostate_Follow_Up

  Validation/deeper sequencing for metastatic prostate cancer samples

  Study EGAS00001000730

• Targeted Gene Panel for 171 PTCLs

  Targeted Gene Panel for 171 PTCLs

  Dataset EGAD00001003967

• WGS data for NRF2 study

  WGS data for cell lines and clinical samples

  Dataset EGAD00001002244

• Inter and intra - tumor heterogeneity in Colorectal Cancer

  The dataset consists of two main sample groups. 1) The inter-tumour sample group contains a total of 97 samples from 27 patients. Each patient has a single normal and primary sample as well as one or more metastases. All samples were sequenced using IonTorrent PGM and a custom colorectal cancer (CRC) panel. 2) The intra-tumour sample group contains a total of 68 samples from a single tumour as well as a normal tissue sample. All 68 samples were sequenced using IonTorrent PGM and a custom CRC panel. Shallow whole genome sequencing was additionally applied to 10 of the samples using Illumina HiSeq 4000.

  Dataset EGAD00001003131

• Genome and transcriptome sequence data from a non small cell lung cancer patient

  Genome and transcriptome sequence data from a non small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001963

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001964

• Genome and transcriptome sequence data from a non-small cell lung carcinoma patient

  Genome and transcriptome sequence data from a non-small cell lung carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001966

• Genome and transcriptome sequence data from an adenocarcinoma of right lung patient

  Genome and transcriptome sequence data from an adenocarcinoma of right lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001967

• Genome and transcriptome sequence data from a non-small cell lung carcinoma patient

  Genome and transcriptome sequence data from a non-small cell lung carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001968

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001969

• Genome and transcriptome sequence data from a melanoma skin cancer - squamous cell carcinoma patient

  Genome and transcriptome sequence data from a melanoma skin cancer - squamous cell carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002018

• Genome and transcriptome sequence data from a metastatic small cell carcinoma of unknown primary cancer patient

  Genome and transcriptome sequence data from a metastatic small cell carcinoma of unknown primary cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002641

• Genome and transcriptome sequence data from a metastatic clear cell carcinoma of the ovary patient

  Genome and transcriptome sequence data from a metastatic clear cell carcinoma of the ovary patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005906

• Mutation and Microsatellite Burden Predict Response to PD-1 Inhibition in Children with Germline DNA Replication Repair Deficiency: An Observational Registry Study

  This dataset contains raw data from polyA RNAseq, hybrid capture target TCR panel data, and bam files from whole exome sequencing on 39 tumors with matched germline blood.

  Dataset EGAD00001008036

• COVID-19 progression and resolution in common variable immunodeficiency patients

  116,958 single-cell transcriptomes from samples of peripheral blood mononuclear cells (PBMCs) from five CVID patients at three distinct stages of the SARS-CoV-2 infection: 1) baseline, before viral infection, 2) progression, during viral infection, and 3) convalescence, once the viral infection had been resolved and the patient was PCR negative. CVID patients were under regular immunoglobulin replacement therapy and displayed only mild symptoms during SARS-CoV-2 infection.

  Dataset EGAD00001009773

• Population-based analysis of POT1 variants in a cutaneous melanoma case-control cohort

  Pathogenic germline variants in the protection of telomeres 1 gene (POT1) have been associated with predisposition to a range of tumor types, including melanoma, glioma, leukemia and cardioangiosarcoma. We sequenced all coding exons of the POT1 gene in 2,929 European-descent melanoma cases and 3,298 controls, identifying 43 protein-changing genetic variants. We performed functional studies on each of these variants and explored their possible contribution to disease risk.

  Dataset EGAD00001009848

• Spinocerebellar ataxia 15 (SCA15) derived iPSC WGS

  A collection of four induced pluripotent stem cell models (iPSC) derived from patients diagnosed with Spinocerebellar ataxia 15 (SCA15) . Spinocerebellar ataxia 15 (SCA15) is a neurological condition characterised by progressive gait and limb ataxia as well as abnormalities in eye movement and difficulties with balance, speech and swallowing (Synofzik et al., 2011). Whole Genome Sequencing was performed to confirm the presence of a heterozygous deletions in the inositol 1,4,5-triphosphate receptor gene (ITPR1), characteristic of the disease. Cell models names: HPSI0216i-vieg_5 or Vieg_5 (WTSIi472-A), HPSI0216i-vieg_3 or Vieg_3 (WTSIi472-B), HPSI0216i-dacv_6 or Dacv_6 (WTSIi554-A), HPSI0216i-boho_3 or Boho_3 (WTSIi502-A). All iPSC models are available via ECACC-Culture Collections.

  Dataset EGAD00001009851

• RNA-seq dataset of Characterization of UV DNA damage in B-cell precursor acute lymphoblastic leukemia

  Extensive characterization of mutational signature SBS7a in B-cell precursor acute lymphoblastic leukemia (BCP-ALL) samples. We aimed to describe the presentation of SBS7a in BCP-ALL, identify other pediatric cancers presenting with SBS7a, look into the link of SBS7a and UV-light in the context of BCP-ALL and cancers with provable UV exposure, and pinpoint when SBS7a was acquired.

  Dataset EGAD00001015598

• The data access committee for Single molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer

  Dac EGAC00001003253

• Engineering Immune Cells for Therapy, Institute for Medical Microbiology, Immunology and Hygiene, Technical University Munich

  CRISPR engineering of human T cells

  Dac EGAC50000000405

• Screening for human epigenetic variation at CpG islands

  Screening for human epigenetic variation at CpG islands

  Dataset EGAD00001000059

• SNP data for Ovarian cancer PRS

  SNP data for Ovarian cancer PRS (cases)

  Dataset EGAD00001008143

• iRHOM2 Deficiency Causes Environmentally Directed Immunodysregulatory Disease

  We have conducted a study of two kindreds each with a unique mutation in the gene RHBDF2 that encodes the protein iRHOM2. Whole exome (kindred 1) and whole genome sequencing (kindred 2) have uncovered the first human loss-of-function mutations of RHBDF2 in 4 individuals. The affected patients are all children, 8 years old or younger, and were brought to our attention initially due to their suffering recurrent infections. Sequencing of their family members revealed parents with heterozygous RHBDF2 mutations, and heterozygous and wild type siblings – all of which were unaffected by this disease. A comprehensive investigation of this gene mutation through in vivo and in vitro experiments revealed that a loss of the protein iRHOM2 impairs the activity of the protease ADAM17 and subsequently compromises immune responses. This condition leads to the afflicted patients becoming vulnerable to different infections based on their unique environment and presenting with diverse clinical phenotypes. 

  Study phs002478

• Somatic Mutations and Cell Lineage in the Human Brain

  Genetic mutations causing human disease are conventionally thought to be inherited from one's parents and present in all somatic (body) cells. Increasingly however, somatic mutations are implicated in neurological diseases. Somatic mutations that arise during the cell divisions of prenatal brain development are inherited in clonal fashion and can cause neurodevelopmental diseases, even when present at low levels of mosaicism. In this study we use whole genome, RNA, and ATAC sequencing of single cells and bulk tissue to identify somatic mutations in control, and some disease, brains to: 1) identify and catalogue the mutations which shape the somatic neuronal genome; 2) perform a cell lineage analysis of the human brain using clonal somatic mutations in cortical neurons; 3) determine patterns of somatic mutations at different ages and in aging related disease phenotypes; and 4) relate cell lineage patterns to cell phenotype in the human brain through cell type-specific analyses.

  Study phs001485

• Metastatic Colorectal Adenocarcinoma Tumor Purity Assessment from Whole Exome Sequencing Data

  As molecular medicine wields greater and greater influence on cancer patient care, including therapeutic selection, it becomes increasingly important to understand the power and deficiencies of computational tools used in decision making processes. Here we use a set of unselected metastatic colorectal tumor samples in a head-to-head comparison between the calculation of stromal content by a board certified pathologist, versus that derived from whole exome sequencing and purity assessment by a variety of common computational methods. We find that in about 1/2 of the samples the computational methods each do a poor job of assessing the percent stromal contamination, and as a result, tumor variant allele frequencies (VAFs) are low or undetected, whereas analysis of organoids derived from the same tumors yielded much higher VAFs in keeping with their lower stromal content. The whole exome sequence of our samples is available via dbGaP, accession phs003059.v1.p1.

  Study phs003059

• Kbtbd13 knock-down restores muscle function in a human-based mouse model of nemaline myopathy type 6

  Nemaline myopathy type 6 (NEM6) is caused by pathogenic variants in KBTBD13. Patients experience muscle weakness, excessive fatigue and impaired muscle relaxation which significantly impact their daily activities. NEM6 pathophysiology consist of abnormal protein aggregation in muscle cells and a predominance of slow-twitch myofibers, and most patients carry the Dutch founder variant in KBTBD13 (NP_001094832.1: p.Arg408Cys, referred to as KBTBD13R408C). Currently, no specific therapy is available. Here, we used a KBTBD13R408C knock-in mouse model and aimed to identify the molecular changes driving disease pathogenesis and to define a therapeutic window to test a therapeutic intervention. We revealed that the mouse model closely phenocopies the human phenotype at a morphological, functional, and transcriptional level and that the p.Arg408Cys variant causes mis-localization of KBTBD13 within the muscle sarcomere. Our natural history data in the Kbtbd13(R408C) mouse model showed that disease onset occurs between 1-3 months and plateaus by 9 months with little progression at 18 months.

  Study EGAS50000001678

• Dual inhibition of FLT3 and BCL-2 is effective in preclinical models of BCL11B-activated lineage ambiguous leukemia

  Aberrant activation of BCL11B (“BCL11B-a”) defines a subtype of lineage ambiguous leukemias with T-lymphoid and myeloid features, co-occurring activating FLT3 mutations, and a stem/progenitor immunophenotype and gene expression profile. As with other lineage ambiguous leukemias, optimal treatment is unclear and there are limited targeted therapeutic options. Here, we investigated the efficacy of BCL-2 and FLT3 inhibition with venetoclax and gilteritinib, respectively, in preclinical models of BCL11B-a leukemia. Despite variation in response to single agent therapies, the combination of venetoclax plus gilteritinib was highly effective in all models evaluated. BH3 profiling and single cell RNA-seq analysis suggests that resistance to venetoclax and/or gilteritinib monotherapies is due to the tumor-intrinsic activity of BCL-xL and/or MCL-1 prior to drug treatment. These data support clinical evaluation of venetoclax in combination with gilteritinib in BCL11B-a lineage ambiguous leukemias.

  Study EGAS50000000978

• Heritable pulmonary arterial hypertension in a large Iberian family

  Data from a study of a large Iberian family (n=65 subjects, 5 generations) affected by pulmonary arterial hypertension (PAH) and segregating with the BMPR2 missense mutation p.Arg491Gln (rs137852749, c.1472G>A). PAH is a rare disease characterized by an abnormal rise in mean pulmonary arterial pressure (> or equal to 25 mmHg at rest), which, in turn, leads to a progressive increase in pulmonary vascular resistance and ultimately to death, due to right ventricular failure. Heritable PAH has an overall prevalence below 1 case per million adults and is defined by either the presence of a known genetic defect linked to the disease or a positive family history. Heritable PAH is inherited as an autosomal dominant disease. However, not all BMPR2 mutation carriers develop the disease, highlighting the presence of reduced penetrance. In this family, there are 22 mutation carriers from which 8 were diagnosed with heritable PAH and the other 14 were healthy at the time of examination.

  Study EGAS00001003123

• Integrated genomic characterization of IDH1 mutant Glioma malignant progression

  Gliomas represent approximately 30% of all central nervous system tumors, and 80% of malignant brain tumors. To understand the molecular mechanisms underlying the malignant progression of low-grade Isocitrate Dehydrogenase 1 (IDH1) mutant gliomas, we studied paired tumor samples from 41 patients, comparing higher-grade, progressed samples to their lower-grade counterparts. Integrated genomic analyses, including whole-exome sequencing, copy number, gene expression and DNA methylation profiling, demonstrated non-linear clonal expansion of the original tumors and identified oncogenic pathways driving progression. These include activation of the MYC and RTK-RAS-PI3K pathways, up-regulation of the FOXM1- and E2F2-mediated cell cycle transition genes, as well as epigenetic silencing of developmental transcriptional factors bound by Polycomb repressive complex 2 in human embryonic stem cells. Our results not only provide mechanistic insight into the genetic and epigenetic mechanisms driving glioma progression but also identify inhibition of the bromodomain and extra-terminal (BET) family as a potential therapeutic approach.

  Study EGAS00001001588

• Dissecting features of epigenetic variants underlying cardiometabolic risk using full-resolution epigenome profiling in regulatory elements

  Sparse profiling of CpG methylation in blood by microarrays have identified epigenetic links to common diseases. We apply methylC-capture sequencing (MCC-Seq) in a clinical population of ~200 adipose tissue and matched blood samples (Ntotal ~400), providing high-resolution methylation profiling (>1.3M CpGs) at regulatory elements. We link methylation to cardiometabolic risk through associations to circulating plasma lipid levels and identify lipid-associated CpGs with unique localization patterns in regulatory elements. We show distinct features of tissue-specific versus tissue-independent lipid-linked regulatory regions by contrasting with parallel assessments in ~800 independent adipose tissue and blood samples from the general population. We follow-up on adipose-specific regulatory regions under (1) genetic and (2) epigenetic (environmental) regulation via integrational studies. Overall, the comprehensive sequencing of regulatory element methylomes reveals a rich landscape of functional variants linked genetically as well as epigenetically to plasma lipid traits.

  Study EGAS00001003415

• A Comprehensive Platform for Analyzing Longitudinal Multi-Omics Data

  PALMO (Platform for Analyzing Longitudinal Multi-omics data) is a platform for analyzing longitudinal data from bulk as well as single cell. It allows to identify inter-, intra-donor variations in genes over longitudinal time points. The analysis can be done on bulk expression dataset without known cell type information or single cell with cell type or user defined groups. It allows to infer stable and variable features in a given donor and each cell type or a user defined group. The outlier analysis can be performed to identify technical/biological perturbed samples in a donor or a participant. Further, differential analysis can be performed to decipher time-wise changes in gene expression in a cell type. The data that is available in the dbGaP is the demo longitudinal samples used in the study, which includes hashed raw fastq files for single-cell RNA-sequencing (scRNA-seq) and non-hashed fastq files for single-cell ATAC-sequencing (scATAC) experiment.

  Study phs003203

• Loss of Epigenetic Barrier is Required for Enhancer Hijacking-Mediated Oncogenic Transcription

  Structural variants (SVs) involving enhancer hijacking can disrupt chromatin topologies to cause oncogene activation in cancer genomes, yet the molecular determinants for the transcriptional output of enhancer hijacking remain largely unknown. We developed a multimodal approach to integrate genome sequencing, chromosome conformation, and sequence-based deep learning for quantitative analysis of transcriptional effects and structural reorganization imposed by SVs in leukemic genomes. We identified candidate pathogenic SVs including recurrent t(5;14) translocations that cause the hijacking of BCL11B enhancers for oncogenic activation of TLX3-dependent transcriptional programs. By engineering patient-associated t(5;14) in isogenic leukemia cells, we uncovered an uncharacterized mechanism whereby DNA methylation serves as an epigenetic barrier to enhancer hijacking and loss of epigenetic barrier is a molecular determinant for the transcriptional output of pathogenic SVs. Hence, leveraging the epigenetic barriers of SV-mediated oncogenic programs may provide new opportunities to reprogram gene regulation as epigenetic therapies in human disease.

  Study EGAS00001006140

• Northern Ireland COhort for the Longitudinal study of Ageing

  The Northern Ireland COhort for the Longitudinal study of Ageing (NICOLA) is a representative sample of ~8,500 people from across Northern Ireland. The study, which was set up in 2012, aims to understand what it is like to grow older in Northern Ireland. • NICOLA has a strong focus on molecular biomarkers so there is complementary genetic, epigenetic and transcriptomic data available for a subset of individuals. • We inherit much of our DNA from our parents while a small amount of this material changes as we get older. In NICOLA we have 551,839 directly genotyped and 18,148,478 imputed Single Nucleotide Polymorphisms (SNPs) currently available for 2969 participants. • Summary statistics for the association of these gene polymorphisms with ~30 phenotypes were generated. • Epigenetics provides a link between our inherited DNA and environmental influences from a person’s diet, medication and lifestyle. NICOLA has the epigenetic quality controlled profiles of 1984 individuals arising from variations in DNA methylation at 862,927 genetic sites.

  Study EGAS00001007915

• The Genetic Basis of Aggressive Prostate Cancer, The Role of Rare Variation

  In this study, we will apply a multi-staged approach to reveal genes harboring rare variants that are associated with aggressive PCa. Whole-exome sequencing (Aim 1a) of 2,774 aggressive cases and 2,776 non-aggressive cases of European ancestry will be conducted followed by rare variant analysis of single sites and gene burden testing to identify novel susceptibility loci/genes for aggressive disease. We will validate the most significantly associated genes (~500) through targeted sequencing in an additional 6,415 aggressive and 5,586 non-aggressive cases and 1,890 controls (Aim 1b). Next, we will investigate the clinical predictive utility of the genes/variants identified in 2,291 cases in the STHM3 trial who are undergoing biopsy based on PSA and genetic risk score stratification (Aim 2). Through this tiered approach we expect to significantly advance knowledge of aggressive PCa etiology and health disparities as well as guide the development of early detection and prognostic strategies for the subset of men who are most susceptible to this fatal form of disease. In this case-case study of aggressive vs non aggressive prostate cancer, aggressive cases are defined as prostate cancer as cause of death, (T4 disease or T3 disease) and Gleason 8+. Non-aggressive cases are men with T1/2 disease and Gleason ACKNOWLEDGMENTS and CONTRIBUTING SITES CAPS, PROCAP, STHM1, STHM2: Swedish Cancer Society (CAN 2016/818), Swedish Research Council (2014/2269).STHM3: Stockholm County Council (Stockholms Läns Landsting).MEC: Funding provided by the National Cancer Institute: Understanding Ethnic Differences in Cancer, 2U01CA164973 and The Genetic Basis of Aggressive Prostate Cancer, The Role of Rare Variation, 5R01CA196931-02.ATBC: The ATBC Study is supported by the Intramural Research Program of the U.S. National Cancer Institute, National Institutes of Health, and by U.S. Public Health Service contract HHSN261201500005C from the National Cancer Institute, Department of Health and Human Services.COSM: The Swedish Research Council/National Research Infrastructure Grant (VR 2014/6397; VR 2015/5997) The Swedish Cancer Foundation (CAN 2013/456; CAN 2016/727)CPSII: The authors express sincere appreciation to all Cancer Prevention Study II participants and to each member of the study and biospecimen management group. The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II cohort.MCCS/APCS/PCFS: The Melbourne Collaborative Cohort Study (MCCS) recruitment was funded by VicHealth and Cancer Council Victoria and further supported by Australian National Health and Medical Research Council (NHMRC) grants 209057 and 396414. The Aggressive Prostate Cancer Case-Control Study (APCS) was funded by NHMRC grant 623204. The Prostate Cancer Family Study (PCFS) was fully funded by Cancer Council Victoria. Cancer Council Victoria funds the continuing maintenance and updating of the MCCS, APCS and PCFS. Cases and their vital status are ascertained and followed up through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database.PLCO: The Prostate Lung Colorectal Ovarian Cancer Screening Trial (PLCO) was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and by contracts from the Division of Cancer Prevention, National Cancer Institute, US National Institutes of Health, Department of Health and Human Services. EPIC: The coordination of EPIC is financially supported by International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam- Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology - ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A19170 and C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford). (United Kingdom). DFCI: Linda and Arthur Gelb and Rebecca and Nathan Milikowsky. HPFS and PHS: The Health Professionals Follow-up Study was supported by U01 167552 and P01 CA228696 from the National Cancer Institute, and with support from the Prostate Cancer Foundation. The Physicians' Health Study was supported by grants CA34944, CA40360, CA097193, HL26490 and HL34595.Northwestern: P50CA180995 (Catalona) 08/01/15 – 07/31/20 NIH/NCI SPORE in Prostate Cancer; The Urological Research FoundationPROMPT: MRC UK - Project reference G0500966, Cambridge BRC infrastructure funding, Cambridge Biomedical Research Campus (BRC-1215-20014), CRUK Cambridge Cancer Centre infrastructure funding (they are requesting this statement is written in blue for publications).ICR: This work was supported by the NIH R01 grant 5R01CA196931-02. The samples from the UK were from UKGPCS and PrompT. The UKGPCS study was supported by Cancer Research UK (grant numbers C5047/A7357, C1287/A10118, C1287/A5260, C5047/A3354, C5047/A10692, C16913/A6135 and C16913/A6835). We would like to acknowledge the NCRN nurses and Consultants for their work in the UKGPCS study. We thank all the patients who took part in this study. We also acknowledge The Institute of Cancer Research, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK for their ongoing support. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust.Funding:CIDR grant X01HG008336

  Study phs001524

• How to use EGA Webin?

  EGA Webin EGA Webin serves as a platform for registering metadata for array based submissions, large scale sequence submission as well as for legacy EGA submission accounts (ega-box-XXXX). For large scale submitters of sequence data you have also the option to submit metadata via XMLS programmatic submission, while new submitters are advised to utilise the Submitter Portal for their submissions. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. WEBIN actions: Register metadata for a sequence submission Register study, samples, experiments, runs, DAC, policy and dataset/s after file upload. Register components for your array-based metadata submission Register study, samples, DAC or policy before uploading files. Edit existing submission metadata Change or update previously submitted metadata. Register metadata for a sequence submission Ensure that all sequence files have been encrypted before uploading them to your submission account using the EgaCryptor. Go to the EGA Webin and log in using your submission account name (ega-box-XXX) and password. Register components of your metadata submission Study Samples Data Access Committee (DAC) Data access policy Dataset For array-base submissions: Study, Samples, Data Access Committee (DAC) and Data access policy may all be registered BEFORE file upload and dataset registration through the array-base template. Register your Study Go to the “Studies (Projects)” box Click on “Register Study” and fill in the information related to your study. Click on “Submit”, this will save the information and generate an EGA study ID. To use the study accession number in a publication, the study has to be previously released on the EGA website, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX.Further information about EGA can be found on https://ega-archive.org and “The European Genome-phenome Archive in 202 "(10.1093/nar/gkab1059)" Register your Samples Go to the “Samples” box Click on “Register Samples” Select “Download spreadsheet to register samples” and customise your template, there is a default EGA template (EGA default checklist) but more attributes can be added if required. For the EGA default checklist, there are mandatory,recommended and optional attributes. As well custom fields which can be added if required. Mandatory attributes Field Name Description tax_id Taxonomy ID of the organism as in the NCBI Taxonomy database. Entries in the NCBI Taxonomy database have integer taxon IDs. See our tips for sample taxonomy here scientific_name Scientific name of the organism as in the NCBI Taxonomy database. Scientific names typically follow the binomial nomenclature. For example, the scientific name for humans is Homo sapiens. sample_alias Unique name of the sample. If not selected system will auto generate an unique alias sample_title Title of the sample sample_description Description of the sample phenotype *** Where possible, please use the Experimental Factor Ontology (EFO) to describe your phenotypes. Recommended attributes Field Name Description subject_id Identifier for the subject where the sample has been derived from gender * Sex Optional attributes Field Name Description sex sex of the organism from which the sample was obtained disease_site Affected organ sample type Affected organ donor_id ** Identifier of the donor where the sample has been derived from *Gender should be described as 'male', 'female' or 'unknown'. If 'unknown' due to a known sex chromosome aneuploidy, please create a user defined attribute called 'Sex chromosome karyotype' and add the appropriate value, for example, 'XXY'. **Donor id (Subject id) should be a de-identified subject handle. If unknown, please add 'unknown' to the field. ***Phenotypes should, where possible, be an Experimental Factor Ontology accession. If a term cannot be found to describe your phenotype please use free text. All sample phenotypes considered important for further analysis of the data should be provided (for example, tumour type), additional phenotype attributes can be created by defining your own attributes; use the notion 'phenotype2', 'phenotype3', etc. After you have customised the fields for the sample submission, download the template and fill in the information. Example of the sample template: Finally upload the sample template to get the EGA accession IDs for the samples. Register your Data Access Committee (DAC) Further information on the role of your DAC Go to the “Data Access” box Click on “Register Dacs” Input the information about the DAC and register at least one main DAC contact. Register your Data Access Policy Your Data Access Policy provides the terms and conditions of data use, this is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAC). Go to the “Data Access” box Click on “Register Policies” Select the DAC to which this policy will be linked to and fill in the policy information. Submitting your Runs and Analyses This section is only for sequence data submission, for array-based submission it can be skipped. Please refer to our Submitting array based metadata Runs Registration Go to the “Raw Reads (Experiments and Runs)” box Click on “Submit Reads” Select “Download spreadsheet template for Read submission” Select the template corresponding to your submission type For the templates you have the option to customise the optional fields. To check their description click on “Show Description “ Download the template and fill in the required information. Example of the runs template: We recommend that Fastq, BAM, and CRAM read files are submitted using Webin-CLI When using this interface instead of Webin-CLI, raw sequences must be uploaded in one of the supported data formats before they can be submitted. The files can be uploaded using FTP or Aspera. The study and the sequenced samples must be pre-registered before the raw reads are submitted. Please note that each individual study and sample should be registered only once. You will be asked to provide information about the sequencing libraries and instruments. Submitting your Dataset This section is only for sequence data submissions, for array based submissions it can be skipped. Please refer to our Submitting array based metadata The dataset describes the data files, defined by the run (EGARXXXXXXXXXXX) and analysis (EGAZXXXXXXXXXXX) accessions that make up the dataset and links the collection of data files to a specified Data Access Committee and Data Access Policy. As a result, you must have registered your Reads and experiments, Data Access Committee (DAC) and Data access policy before submitting your Dataset. Please consider the number of datasets that your submission consists of, for example, a case control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Please contact EGA Helpdesk for further assistance. Go to the “Data Access” box Click on “Register Dataset” Select the Data Access Committee (DAC) and Data access policy Register your dataset After submitting your dataset you should contact the EGA Helpdesk to provide a release date for your dataset. Datasets are automatically held (i.e. not released) unless they are affiliated to a study that has already been released. Edit/update existing submission metadata Go to the “Report” section of the object you would like to edit. Locate the object and click on the arrow under action. An option menu will be displayed. Objects can be edited through their XML or with the WEBIN menu. After an object has been edited, changes would be available on the website until the submission is released again. Please contact the EGA Helpdesk if you require further assistance.

  Documentation submission/metadata/submission/EGA_webin

• International Consortium for Blood Pressure (ICBP)

  The ICBP consortium is an international effort to investigate blood-pressure genetics. The consortium was formed by two parent consortia, the CHARGE-BP consortium (Cohorts for Heart and Aging Research in Genomic Epidemiology - blood pressure) and the GBPGEN consortium (Global Blood Pressure Genetics Consortium). In 2011 we performed genome-wide association analyses based on genome-wide SNPs imputed to HapMap for systolic and diastolic blood pressure (SBP and DBP) and mean arterial pressure and pulse pressure (MAP and PP). In 2016 we performed an analysis based on the Cardio-MetaboChip for SBP and DBP. All these datasets are available here, however, full association statistics including effect size directions, only under controlled access by dbGaP.

  Study phs000585

• Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS)

  Advances in antiretroviral therapies (ART) and patient management have improved survival of people living with HIV (PLWH). However, with exposure to ART and associated side effects and toxicities as well as to the virus itself, PLWH are at an increased risk of several chronic diseases. The Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS) research network integrates longitudinal clinical data and treatment management of PLWH and provides research infrastructure to support HIV-associated clinical, social, and behavioral outcomes and comparative effectiveness research using data collected from 8 Centers for AIDS Research clinical sites across United States. For detailed study description, see Kitahata et al. (2008, PMID:18263650).

  Study phs001788

• A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy

  The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and non-canonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in-silico tools for the identification, prediction, and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens and high-confidence tumor-specific antigens.

  Study EGAS50000000228

• Star2xml: metadata converter into XML

  Star2xml: metadata converter into XML Welcome to the landing page for star2xml - a tool developed by the European Genome-phenome Archive (EGA) that enables users to convert metadata into XML format that is compliant with EGA's programmatic submission system. For starters, if you are not sure what metadata is in this context, you may want to explore our brief metadata introduction. . Star2xml is useful for users who have already submitted their data to the EGA and want to also submit their metadata programmatically without having to create the XML files manually. The need for these XMLs containing metadata is explained in the EGA's metadata schemas and programmatic submission guidelines. In summary, star2xml allows for users to deposit metadata in a tabular format (e.g. excel spreadsheet) and transform it into a compliant XML format. This saves time in large submissions, where the sheer size of metadata makes it difficult to manage. For smaller submissions, it is recommended to use EGA's Submitter Portal (SP) instead. The installation of the tool is straightforward, and it contains detailed documentation on how to use it in its GitHub repository . While star2xml can help users convert metadata formats, it does not validate the resulting metadata against EGA's schemas. Therefore, after creating your XMLs, you should still validate your metadata before submitting it to EGA, as described in the documentation for programmatic metadata validation.

  Documentation tools/star2xml

• Identifying causative mutations for Thrombocytopenia with Absent Radii

  Thrombocytopenia with Absent Radii is an inherited disorder that manifests itself with major limb skeletal abnormalities and low platelet count (and therefore a bleeding diathesis). The syndrome is well-characterised and defined phenotypically and there is a well-established database of UK-based families affected with ths disorder. The causative mutation for the disorder is yet to be identified. If known, it would allow for pre-natal screening and counselling avoiding life-long care for patients who are affected and are therefore severely disabled. We postulate that exon sequencing of 4 unrelated affected individuals would give enough power to narrow down potential candidate mutations which would thereafter be confirmed using DNA from other affected families.

  Study EGAS00001000054

• A standardised framework for robust fragmentomic feature extraction from cell-free DNA sequencing data

  Fragmentomic features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. However, a lack of systematic evaluation of biases in feature quantification has hindered the adoption of such applications. We compared features derived from whole-genome sequencing of ten healthy donors using nine library kits and ten data-processing routes, and validated them in 1,182 plasma samples from published studies. Our results clarify the variations resulting from library preparation and feature quantification methods. We designed the Trim Align Pipeline and the cfDNAPro R package as unified interfaces for data pre-processing, feature extraction, and visualisation, aiming to standardise multimodal feature engineering and integration for machine learning.

  Study EGAS00001008051

• Next Generation Sequencing platform for targeted Personalized Therapy of Leukemia - Acute myeloid leukemia

  The advent of high-throughput next generation sequencing (NGS) technologies that are revolutionizing genomics and transcriptomics by providing a single base resolution tool for a unified deep analysis of diseases complexity allows a fast and cost-efficient fine-scale assessment of the genetic variability hidden within cohorts of patients affected by the same leukemia. That being so, by potentially highlighting inter-individual differences that may play a role in the differential success of diverse therapeutic interventions, they promise to be crucial for selecting the most appropriate medical treatments. The project aims at the identification of novel prognostic biomarkers for acute myeloid leukemia (AML) and studied the molecular differences between aneuploid and euploid AML.

  Study EGAS00001005464

• Genome-wide analysis for non alcoholic fatty liver disease

  Genome-wide association study for the 902 patients of non alcoholic fatty liver disease (NAFLD) including non alcoholic steato hepatitis (NASH) and hepatocellurar carcinoma and 7672 controls.

  Study JGAS000126

• WTCCC case-control study for Rheumatoid Arthritis

  WTCCC genome-wide case-control association study for Rheumatoid Arthritis (RA) using the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000011

• WTCCC case-control study for Type 2 Diabetes

  WTCCC genome-wide case-control association study for Type 2 Diabetes (T2D) using the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000016