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• GOSH_Childhood_Leukemia_Behjati_WellcomeCore_RNA

  Paediatric single-cell archival project This project is taking sequential samples of children with B-cell acute lymphoblastic leukaemia (B-ALL) treated at Great Ormond Street Hospital (GOSH) between 2011 and the present day. Patients selected, are those that have done badly (e.g. had to escalate treatment, relapsed or died) despite having apparently ""good"" or ""intermediate"" risk cytogenetics, and often also had immunotherapy (e.g., the bi-specific T-cell engager blinatumomab) because of this. Using samples from key treatment-points, we hope to better characterise their disease and treatment response, to re-define understandings of risk for patients who did unexpectedly poorly. Additionally, we hope to define the disease/immunological factors that determine response to increasingly used immunotherapies within the field of paediatric B-ALL. Each of the samples will be viably frozen bone marrow-derived mononuclear cells. They will come in Eppendorf's, frozen and de-anonymised, from GOSH. We plan to primarily do single cell analysis (GEX, BCR +/-TCR), and then tumour-only WGS and bulk-RNA expression on left-over

  Study EGAS00001007616

• Pharmacogenomics of Metformin Dose Response in T2DM Patients

  Type 2 Diabetes Mellitus (T2DM) is a chronic and sometimes debilitating disease that affects millions of adults in the USA. Metformin is commonly prescribed for T2DM; yet some T2DM patients do not tolerate it or respond well. This pharmacogenomics study will examine response, as both change in T2DM patients' weight and hemoglobin A1c (HbA1c) levels, to different doses of metformin, by extracting this data from the electronic health record (EHR), for T2DM subjects taking metformin found by prescription orders in the EHR, and identified as cases with the EHR phenotype algorithm published by Kho et. al. in 2012 (PMID: 22101970 as part of the eMERGE (electronic Medical Records and Genomics; dbGaP phs000237, phs000297, phs000360, phs000368, phs000888, and phs000948) project.

  Study phs000984

• Filtering and Annotation of Variants That Are Rare (FAVR)

  The project developed a suite of new methods (FAVR) designed to assist the shortlisting of genetic variants under a rare variant-phenotype/disease model. The methods were designed to work with commonly used massively parallel sequencing analysis pipelines, such as the GATK or ANNOVAR, and have been made publically available as a suite of software tools (https://github.com/bjpop/favr). The FAVR methods use signatures in comparator sequence alignment files to facilitate the filtering of mapping artifacts and common genetic variants, and annotation of genetic variants based on evidence of co-occurrence in individuals. As relevant, FAVR methods can also be used to filter out artifacts derived from imbalanced paired-end sequencing. Pope et al., BMC Bioinformatics, accepted Dec 2012.

  Study phs000601

• A Comprehensive Catalogue of Somatic Mutations from a Human Cancer Genome

  We previously reported the whole-genome sequencing of the well-studied human malignant melanoma cell line, COLO-829, and a lymphoblastoid cell line from the same individual, providing the first comprehensive catalogue of somatic mutations from an individual cancer (Pleasance et al. (2010) Nature 463, 191). This initial study was carried out using the Illumina Genome Analyzer II, largely using paired-end reads of 75 bases from standard Illumina paired-end libraries. Here we report the results of sequencing the genomes of COLO-829 and its matched lymphoblastoid sample using HiSeq2000 with v3 cluster-generation and sequencing chemistries. This has significantly improved the evenness and completeness of genomic coverage, particularly in areas of extreme sequence composition that were underrepresented in the initial study.

  Study EGAS00001000245

• Association studies using the Metabochip array - Samples analysed by the WTCCC (1958 British Birth Cohort (58BC), Hypertension cohort (HT), Type 2 Diabetes Cohort (T2D) and Coronary Artery Disease (CAD) cohort)

  Genomewide association studies (GWAS) have proven a powerful hypothesis-free method to identify common disease-associated variants. Even quite large GWAS, however, have only at best identified moderate proportions of the genetic variants contributing to disease heritability. To provide cost-effective genotyping of common and rare variants to map the remaining heritability and to fine-map established loci, the Metabochip Consortium has developed a 200,000 SNP chip that has been produced in very large numbers for a fraction of the cost of GWAS chips. This chip provides a powerful tool for genetic studies of metabolic, cardiovascular and anthropometric traits (Voight et al., in press PLoS Genetics).

  Study EGAS00000000115

• Integrative analysis of small cell lung cancer

  Small-cell lung cancer (SCLC) is an aggressive lung tumor subtype. We conducted integrated analysis of genome sequencing, transcriptome, and copy number analysis and found an extremely high mutation rate of 7.4±1 protein-changing mutations per million basepairs. Evidence for inactivation of TP53 and RB1 was found in all sequenced cases. Furthermore, we identified recurrent mutations in CREBBP, EP300, and MLL, observed mutations in PTEN, in SLIT2, and EPHA7, as well as focal amplifications of the FGFR1 locus. As a major feature of SCLC we found mutation in histone modifying genes, observed genome alterations that are therapeutically tractable, and provide a framework for identifying biologically relevant genes in the context of a high mutation rate. Peifer et al., 2012, Nature Genetics.

  Study EGAS00001000299

• DPY30_ChIP_seq

  H9 human embryonic stem cells (hESCs) were cultured in feeder-free chemically-defined conditions in medium containing 10ng/ml Activin A and 12ng/ml FGF2 (Vallier L. 2011, Methods in Molecular Biology, 690: 57–66). Chromatin immunoprecipitation was performed as described in Brown S. et al. 2011. Stem Cells 29: 1176–85 by using 5ug of anti-DPY30 antibody (Sigma, cat. number HPA043761). This protocol was performed in control hESCs (expressing a scrambled shRNA) and in hESCs stably expressing an shRNA against DPY30 (Sigma, clone n. TRCN0000131112).This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001001132

• Integrated Genomic Analysis of Chronic Lymphocytic Leukaemia

  Chronic lymphocytic leukaemia (CLL) is a frequent and heterogeneous disease whose genetic alterations determining the clinicobiological behaviour are not fully understood. Here, we describe a comprehensive evaluation of the genomic landscape of 452 CLLs and 54 monoclonal B-lymphocytosis (MBL), a precursor disorder. This study provides an integrated portrait of the genomic landscape of CLL, identifies new recurrent mutations acting as drivers of the disease, and suggests clinical interventions which may improve the management of patients with this neoplasia. The data here included was described in the ICGC-CLL manuscript published by Puente et al (Nature 2015). Note that complementary RNA-seq data generated within the IGCG-CLL can be found in EGAS00001000374.

  Study EGAS00001001306

• RNA sequencing from patient-derived intestinal organoids

  uman duodenal tissues for establishing organoid cultures used in this study were sourced from de-identified surgical specimens (n = 3) of the duodenum obtained from patients who had undergone biopsy procedures unrelated to CeD at Tampere University Hospital. The protocol was approved by the Ethics Committee of Tampere University Hospital, Tampere, Finland (ETL code R18082). RNA from the duodenal organoids was isolated using an RNeasy Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. RNA purity and concentration were measured using a NanoDrop One spectrophotometer (NanoDrop Technologies, Wilmington, Delaware, USA). Preparation of the RNA library and transcriptome sequencing was conducted by Novogene Co., LTD (Cambridge, UK). Messenger RNA was purified from total RNA using polyA selection and subjected to library construction. Sequencing was performed on an Illumina platform, and 150 bp paired-end reads were generated.

  Dataset EGAD50000000492

• Profiling the genomic landscape and evolutionary history of polyploid giant cancer cells in undifferentiated pleomorphic sarcomas

  In this study, we characterise PGCCs in ten pleomorphic sarcomas and use topographic single-cell DNA sequencing (scDNA-seq) to investigate their genomic landscape. We selected PGCCs based on their nuclear morphology, including mononucleated or multinucleated bizarre, misshapen nuclei, and analysed them at single-cell resolution. Histopathological analysis showed that PGCCs were often randomly distributed throughout the tumour and did not appear in clusters, suggesting that they arise de novo rather than through clonal expansion. scDNA-seq revealed that PGCCs originate from the dominant tumour population and exhibit extensive copy number heterogeneity, either due to subsequent or ongoing chromosomal instability. Both clonal and subclonal chromothripsis-like events were identified in PGCCs, indicating that chromothripsis is a key driver of heterogeneity in these cells and is linked to multinucleation rather than mononuclear PGCC formation.

  Dataset EGAD50000002084

• Genomic Evolution of Breast Cancer Metastasis and Relapse

  Recurrent breast cancer is almost universally fatal. We characterize 170 patients locally relapsed or distant metastatic cancers using massively parallel sequencing. We identify that the relapse-seeding clone disseminates late from the primary tumor. TP53 and AKT1 appear to be enriched in ER-positive cancers predisposed to relapse. Mutation acquisition continues at relapse as the same mutation signatures continue to operate and new signatures, such as that caused by radiotherapy appear de novo. In 49% of cases we identify drivers mutations private to the relapse and these are sampled from a wider range of cancer genes, including SWI-SNF complex and JAK-STAT signaling.

  Dataset EGAD00001002696

• Exploring the driver events of eccrine poromas and porocarcinomas: A retrospective, cross-institutional study of 90 cases

  Eccrine poroma (EP) and porocarcinoma (EPC) are rare benign and malignant adnexal neoplasms of the terminal sweat gland duct, respectively. Both can arise de novo, however, EPCs can also arise from a pre-existing EP. To-date, genetic investigation of these tumors has involved studies with small sample sizes and/or limited analyses. To comprehensively compare the driver events and mutational landscape of these tumors, we performed a retrospective multi-institutional whole-exome sequencing and RNA sequencing study on the largest cohort of EPs and EPCs to-date (n=90). We uncovered novel events and delineated different pathways of tumorigenesis underlying these tumors, with EPs driven largely by fusion genes, and EPCs driven largely by somatic mutations, with rare YAP1 and frequent PAK gene novel fusions.

  Dataset EGAD00001015378

• Exploring the driver events of eccrine poromas and porocarcinomas: A retrospective, cross-institutional study of 90 cases

  Eccrine poroma (EP) and porocarcinoma (EPC) are rare benign and malignant adnexal neoplasms of the terminal sweat gland duct, respectively. Both can arise de novo, however, EPCs can also arise from a pre-existing EP. To-date, genetic investigation of these tumors has involved studies with small sample sizes and/or limited analyses. To comprehensively compare the driver events and mutational landscape of these tumors, we performed a retrospective multi-institutional whole-exome sequencing and RNA sequencing study on the largest cohort of EPs and EPCs to-date (n=90). We uncovered novel events and delineated different pathways of tumorigenesis underlying these tumors, with EPs driven largely by fusion genes, and EPCs driven largely by somatic mutations, with rare YAP1 and frequent PAK gene novel fusions.

  Dataset EGAD00001015376

• Exploring the driver events of eccrine poromas and porocarcinomas: A retrospective, cross-institutional study of 90 cases

  Eccrine poroma (EP) and porocarcinoma (EPC) are rare benign and malignant adnexal neoplasms of the terminal sweat gland duct, respectively. Both can arise de novo, however, EPCs can also arise from a pre-existing EP. To-date, genetic investigation of these tumors has involved studies with small sample sizes and/or limited analyses. To comprehensively compare the driver events and mutational landscape of these tumors, we performed a retrospective multi-institutional whole-exome sequencing and RNA sequencing study on the largest cohort of EPs and EPCs to-date (n=90). We uncovered novel events and delineated different pathways of tumorigenesis underlying these tumors, with EPs driven largely by fusion genes, and EPCs driven largely by somatic mutations, with rare YAP1 and frequent PAK gene novel fusions.

  Dataset EGAD00001015377

• Exploring the driver events of eccrine poromas and porocarcinomas: A retrospective, cross-institutional study of 90 cases

  Eccrine poroma (EP) and porocarcinoma (EPC) are rare benign and malignant adnexal neoplasms of the terminal sweat gland duct, respectively. Both can arise de novo, however, EPCs can also arise from a pre-existing EP. To-date, genetic investigation of these tumors has involved studies with small sample sizes and/or limited analyses. To comprehensively compare the driver events and mutational landscape of these tumors, we performed a retrospective multi-institutional whole-exome sequencing and RNA sequencing study on the largest cohort of EPs and EPCs to-date (n=90). We uncovered novel events and delineated different pathways of tumorigenesis underlying these tumors, with EPs driven largely by fusion genes, and EPCs driven largely by somatic mutations, with rare YAP1 and frequent PAK gene novel fusions.

  Dataset EGAD00001015379

• Complete DNA/RNA sequencing dataset for Australian ICGC ovarian cancer sequencing project 2014-07-07

  Complete WGS and RNA-Seq dataset for Australian ICGC ovarian cancer sequencing project 2014-07-07, representing 93 donors. Sequencing was performed on Illumina HiSeq. Alignment of the lane-level fastq data was performed with bwa (WGS data) and RSEM (transcriptome data). For this dataset lane-level .bam files have been merged and de-duplicated to create a single bam file for each sample type (tumour/normal) for each donor. This dataset supersedes all previous datasets for this study. 2016-08-08 updated with 14 outstanding RNA-seq samples & corresponding RSEM bams 2016-12-07 updated with 7 outstanding RNA-seq controls and corresponding RSEM bams

  Dataset EGAD00001000877

• SC_DDD-G-5

  Post-QC (pre-imputation) genotype data for N=2,166, a subset of trios described in EGAD00010001602. These data form N=722 complete trios in which the proband has a neurodevelopmental phenotype (Niemi et al. Nature 2018). Includes HPO phenotype terms for patients. Samples were genotyped on the Illumina HumanOmniExpress BeadChip

  Dataset EGAD00010001606

• ETMR_Nanostring

  Total RNA (100ng) from 21 ETMRs with C19MC structural alterations and 28 other PBTs was prepared with nCounter miRNA Sample Prep Kit according to standard protocol. miRNA expression profiling was conducted with human v1, v2, or v3 miRNA panel on nCounter miRNA expression platform (NanoString Technologies, Seattle, WA) according to manufacturer’s protocol. Signal normalization was done using nSolver Analysis and batch corrected using ComBat (Johnson et al. Biostatistics. 2007). 565 miRNAs overlapped between all three versions and was used for further analyses. Fold change and supervised t-test with FDR correction was calculated between the ETMRs and other PBTs.

  Dataset EGAD00010001701

• MassArray1-80

  234 samples genotyped at 40 loci using the MassArrayiPLEX genotyping assay using the iPLEX Gold genotyping kit (Agena Biosciences, cat. 10148-2) Gabriel et al. (2009) [Gabriel, S. , Ziaugra, L. and Tabbaa, D. (2009), SNP Genotyping Using the Sequenom MassARRAY iPLEX Platform. Current Protocols in Human Genetics, 60: 2.12.1-2.12.18. doi:10.1002/0471142905.hg0212s60]). Products were detected on a MassArray mass spectrophotometer and data were acquired in real time with MassArray RT software 4.0.0.2 (Agena Biosciences). SNP clustering and validation was carried out with Typer 4.0.26.75 software (Agena Biosciences).

  Dataset EGAD00010001906

• Nurminen et al ("GP2Men") Study Primary and Metastatic Prostate Cancer Whole Genome Sequence Data

  We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones.

  Dataset EGAD50000000005

• Homologous recombination repair status in metastatic prostate cancer by next-generation sequencing and functional tissue-based immunofluorescence assays

  - WES files from primary or metastatic prostate cancer biopsies, from patients with metastatic prostate cancer. - This dataset includes those cases profiled by WES and included in the manuscript Homologous recombination repair status in metastatic prostate cancer by next-generation sequencing and functional tissue-based immunofluorescence assays, Arce-Gallego et al, Cell Rep Med 2024 - The dataset includes WES for 80 tumor biopsies from 65 different patients, including paired tumor/normal samples.

  Dataset EGAD50000001018

• Anthropological dataset 1 for The admixture histories of Cabo Verde

  Datasets used in the article "The genetic and linguistic admixture histories of the islands of Cabo Verde" by Laurent R et al. eLife 2023 (DOI: https://doi.org/10.7554/eLife.79827 - URL: https://elifesciences.org/articles/79827) File name "eGAdeposit_233CaboVerde_SampleInfo_FINAL_01062022.txt" Column 1 corresponds to individual alphanumeric codes as in the "eGAdeposit_233CaboVerde_GenotypeFile_FINAL_01062022.vcf" genotype file Column 2 corresponds to individual's biological sex as per genetic inference Column 3 corresponds to individual's self-reported age in years Column 4 corresponds to individual's self-reported cumulated number of years spent in academic or professional education

  Dataset EGAD00001008976

• Candidate diagnostic variants reported into DECIPHER, Wright NEJM 2023

  Candidate diagnostic variants reported into DECIPHER by 4 April 2022, annotated with clinical and automated pathogenicity assertions (see DOI: 10.1056/NEJMoa2209046). Genomic Diagnosis of Rare Pediatric Disease in the United Kingdom and Ireland, Wright et al, NEJM 2023.

  Dataset EGAD00001010137

• RNA sequencing of human intra- and extracranial endothelial cells

  This data set contains the raw .fastq files from one RNA-sequencing experiment. Endothelial cells of the basilar artery and endothelial cells of the carotid artery were post-mortem derived with laser microdissection and sequenced. For this analysis both the basilar- and the carotid artery endothelial cells of eleven individuals were sequenced. For more details please see: DMA Hermkens et al. "Profiling the Unique Protective Properties of Intracranial Arterial Endothelial Cells" Acta Neuropathol Commun. 2019 Oct 14; PMID: 31610812.

  Dataset EGAD00001006203

• Non-ADT METs ICGC Prostate UK

  To characterize the subclonal genomic architecture of non-androgen-deprived metastatic prostate cancer, we performed whole-genome sequencing (WGS) of pelvic lymph node metastases and matching noncancerous blood from 10 patients to an average sequencing depth of 55x. The patients are part of PELICAN (Project to ELIminate Lethal Cancer) study led by G. Steven Bova at Johns Hopkins University (USA) and Tampere University (Finland). As of September 2020, study using these data is: Wedge et al, Nature Genetics 2018 (PMID: 29662167)

  Dataset EGAD00001002002