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• WGS samples for multiple myeloma (hipo-067 and hipo-K08K)

  Four PAIRED WGS samples, tumor and control, were sequenced on a HiSeq X Ten and the library preparation kit used was Illumina TruSeq Nano DNA. The tumor was multiple myeloma from bone marrow.

  Dataset EGAD00001008150

• SNF_CyTOFF_20

  Matrix of normal values from 39 marker CyTOF assay performed on baseline BMMC. FCS files from the CyTOF assay were normalized and concatenated using Fluidigm's CyTOF software and then de-multiplexed using Astrolabe Diagnostics, Inc., a commercial, cloud-based platform for single-cell analysis.

  Dataset EGAD00001011146

• Dataset for MCPlus_WGS

  WES/WGS sequencing data of 44 chromothriptic tumor and control runs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001009277

• Dataset for liposarcoma-EXON

  Rare cancer sequencing data of 29 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008886

• Fecal 16S UC sequencing data

  Fecal 16S-V4 rRNA gene sequence data from NCT02749630 ulcerative colitis patients. Stool samples were collected at screening as well as on days 29, 43, 64, 85, and 134 processed for 16SV4 rRNA gene sequencing

  Dataset EGAD00001008818

• Dataset for gynecologic_cancer-EXON

  Rare cancer sequencing data of 55 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008877

• Targeted RNA Expression Profiling via scTAMARA-seq

  This dataset comprises targeted single-cell RNA expression profiles derived from the same CD34+ bone marrow sample (patient X.1) used for scTAMARA_DNA. 120 RNA targets were selected using LASSO regression to capture key transcripts predictive of cell state within the hematopoietic compartment.

  Dataset EGAD00001015495

• ART-NKI II HNSCC RNA-Seq

  RNA-Seq data of 25 HNSCC specimens from patients treated at The Netherlands Cancer Institute, Amsterdam and enrolled in the ARTFORCE trial. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for polyA mRNA sequencing.

  Dataset EGAD00001005721

• Targeted sequencing of breast cancer susceptibility genes for 1,995 Japanese breast cancer patients

  This dataset consists of 106 bam files. Each sample from 10-20 consecutive patient extractions were combined into one DNA pool, generating a total of 106 DNA pools. We sequenced 11 genes implicated in hereditary breast cancer using the SureSelect Custom kit.

  Dataset EGAD00001006368

• TXT_Cytof_15B

  Matrix of normal values from 39 marker CyTOF assay performed on longitudinal BMMC. FCS files from the CyTOF assay were normalized and concatenated using Fluidigm's CyTOF software and then de-multiplexed using Astrolabe Diagnostics, Inc., a commercial, cloud-based platform for single-cell analysis.

  Dataset EGAD00001011143

• Tam-seq of germline samples for HGSOC copy-number signatures study

  This dataset contains targeted amplicon sequencing of Germline DNA extracted from 56 blood samples. They were sequenced on Illumina HiSeq 2500 and aligned to human genome assembly GRCh37 (hg19)to produce 127 bam files (2-3 technical replicates per sample).

  Dataset EGAD00001004172

• DAC for Genomic landscapes of endometrioid and mucinous ovarian cancers and morphologically similar tumor types

  While endometrioid and mucinous ovarian cancers represent nearly a quarter of ovarian cancers, their molecular characteristics and pathologic origins are poorly understood. To characterize the genomic and epigenomic alterations of these ovarian cancer subtypes and evaluate links to morphologically similar tumors from other sites, we performed sequence, copy number, mutation signature, rearrangement, and methylation analyses from tumor samples and matched normal tissues of 133 patients. These analyses included patients with ovarian endometrioid (n=44), ovarian mucinous (n=43), uterine endometrial (n=15), and gastrointestinal mucinous cancers (n=31). In addition to identifying genes previously known to be involved in these tumors, we identified alterations in RAD51C, NOTCH4, SMARCA1/4 and JAK1 in ovarian endometrioid, ESR1 in uterine endometrioid, and SMARCA4 in ovarian mucinous cancers. Whole genome sequencing revealed rearrangements involving PTEN, NF1, and NF2 in ovarian endometrioid tumors, and NF1 and MED1 in ovarian mucinous cancers. The number of alterations and affected genes in ovarian and uterine endometrioid cancers were similar as were genome-wide methylation profiles, suggesting that these tumors may have a similar tissue of origin. By contrast, methylation patterns in ovarian mucinous cancers were more similar to other gastrointestinal mucinous cancers, including mucinous cancers of the stomach, colon, and pancreas. These analyses provide insights into the genomic landscapes and origins of mucinous and endometrioid ovarian carcinomas, providing new avenues for early clinical intervention and management of patients with these cancers.

  Dac EGAC50000000341

• UK10K NEURO IOP COLLIER

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The Genetics and Psychosis (GAP) set consists of samples from subjects with schizophrenia, ascertained as a new-onset sample. This set is of UK origin, with data on cognition, brain imaging and other endophenotypes. The Maudsley twin series consists of probands ascertained from the Maudsley Twin Register, defined as patients of multiple birth who had suffered psychotic symptoms. This set is of UK origin, with data on cognition, brain imaging and other endophenotypes, with DNA available from an MZ or DZ affected or unaffected co-twin. The Maudsley family study (MFS) consists of over 250 families who have a history of schizophrenia or bipolar disorder Within the Maudsley Family Study, biological markers of psychosis include neuropsychological tests, Evoked Response Potentials Tests (ERPs), MRI scans, dermatoglyphics and eye tracking. Early risk factors for psychosis and clinical symptoms are also investigated. This set is of UK origin, with DNA available from both affected and unaffected relatives in many of the probands. For further information on this study please contact David Collier (david.collier@kcl.ac.uk).

  Study EGAS00001000121

• Integrative genomic profiling of hepatocellular adenomas reveals recurrent FRK activating mutations and mutational processes of malignant transformation

  Hepatocellular adenomas (HCA) are benign liver tumors predominantly developed in women using oral contraception. Here, exome sequencing identified novel recurrent somatic FRK mutations that induce constitutive kinase activity, STAT3 activation and cell proliferation sensitive to Src inhibitors. We also found new recurrent mutations activating JAK1, gp130 or ß-catenin. Chromosome copy-number and methylation profiling revealed patterns that correlated with specific gene mutations and tumor phenotypes. Finally, integrative analysis of HCA transformed to hepatocellular carcinoma revealed ß-catenin mutation as an early alteration and TERT promoter mutations as associated with the last step of the adenoma-carcinoma transition. In conclusion, we identified the genomic diversity in benign hepatocyte proliferation, new therapeutic targets and the key genomic determinants of the adenoma-carcinoma transformation sequence. Significance: Malignant transformation of benign adenoma into malignant carcinoma is frequently observed in several epithelial tumor types. Adenoma-carcinoma transition is crucial for the patient prognosis, however, little is known on the molecular mechanisms involved. Here, we performed an integrated genomic analysis of hepatocellular adenomas (HCA). Among several novel gene mutations, we identified recurrent somatic mutation activating FRK, a Src-like kinase. FRK activating mutations induce STAT3 activation and cell proliferation that can be targeted by Src inhibitors. Focusing on malignant transformation of HCA in hepatocellular carcinoma, we identified CTNNB1 and TERT promoter mutation as early and late genomic events involved in adenoma-carcinoma transition. In conclusion, we propose new therapeutic targets and an identification of adenomas with the highest risk of malignant transformation. Highlights: - FRK is activated by somatic recurrent mutations. - FRK mutants promote cell proliferation sensitive to src inhibitors. - CTNNB1 activation is an early event in HCA transformation. - TERT promoter mutations are associated with adenoma-carcinoma transition.

  Study EGAS00001000679

• Regenerative effects of MSC treatment on busulfan-induced small intestine damaged organoids.

  Allogenic hematopoietic stem cell transplantation (HSCT) is a curative treatment for leukemia and a range of non-malignant disorders. The success of the therapy is hampered by occurrence of acute graft-versus-host disease (aGvHD); an inflammatory response damaging recipient organs, with gut, liver, and skin being the most susceptible. Intestinal GvHD injury is often a life-threatening complication in patients unresponsive to steroid treatment. Second-line available therapies are immunosuppressants or mesenchymal stromal/stem cell (MSC) infusions. Data from our institution and others demonstrate rescue of approximately 40-50% of patients suffering from aGvHD with mesenchymal stromal/stem cells (MSCs) and minor side effects. Although promising, the better understanding of MSC mode of action and patient response to MSC-based therapy is essential to improve this lifesaving treatment. Here, we developed a novel 3D co-culture model of human small intestinal organoids and MSCs, which allows to study the regenerative effects of MSCs on intestinal epithelium in a more physiologically relevant setting than existing in vitro systems. Using this model we mimicked chemotherapy-mediated damage of the intestinal epithelium. The treatment with busulfan, the chemotherapeutic commonly used as conditioning regiment before the HSCT, affected pathways regulating EMT, proliferation, and apoptosis in small intestinal organoids, as shown by transcriptomic and proteomic analysis. The co-culture of busulfan treated intestinal organoids with MSCs reversed the effects of busulfan on the transcriptome and proteome of intestinal epithelium, which we also confirmed by functional evaluation of proliferation and apoptosis. Collectively, we demonstrate that our novel in vitro co-culture system is a new valuable tool to facilitate the investigation of the molecular mechanisms behind the therapeutic effects of MSCs on damaged intestinal epithelium.

  Study EGAS00001007432

• The CHOP Pediatric Genetic Sequencing (PediSeq) Project : Applying Genomic Sequencing in Pediatrics

  The purpose of this research study is to learn more about newer types of genetic testing called whole exome sequencing and whole genome sequencing. Current genetic testing usually examines one gene or a few genes at a time. Exome and genome sequencing allow us to test most of a person's genes at one time with a single test. This testing might identify the reason for a person's unexplained condition or diagnosis. We hope this study will help us identify the best methods for: Educating patients and families about exome and genome sequencing Analyzing exome and genome sequencing data to identify results relevant to patients Giving results to families in a clear, appropriate, and informative manner The current data only includes WES for 159 samples. The WGS samples for 20 samples will be uploaded in the near future.

  Study phs000935

• Childhood Cancer Data Initiative (CCDI): Pediatric In Vivo Testing Program - Neuroblastoma

  The goal of NCI's Pediatric In Vivo Testing Consortium (PIVOT) is to advance the development of effective treatments for pediatric cancers through preclinical in vivo testing of novel therapeutic agents. To design impactful experiments that can be rapidly translated to the clinic, PIVOT investigators require comprehensive genomic characterization of the patient-derived xenograft (PDX) models used across the consortium. This robust experimental design enhances collaboration with industry partners by providing proof-of-concept data for drugs in development. As part of this effort, neuroblastoma PDX models are being molecularly characterized to identify promising therapeutic candidates. The molecular profiling includes whole exome sequencing (WES), RNA-seq, MethylEPIC array, CytoSNP array, and DNA fingerprinting for quality control. Additionally, demographic and clinical data (e.g., diagnosis, disease site, disease status) are shared. These characterized PDX models, matched with patient tumors, guide model selection for preclinical drug testing.

  Study phs003163

• How to request data

  Request Data Access If you're looking to access data from the EGA for your research, you are in the right documentation page! In this page you will find a step by step guide on how to request data archived at the EGA, as well as some frequently asked questions you might find useful. When requesting data, you'll need to provide information about your research project and intended use of the data. Keep in mind that your data access request may require additional approvals or agreements depending on the datasets and data providers involved. Step by step guide Register yourself as an EGA user. Validate your account. A validation link will be sent to your email to activate your EGA user account. Log into the EGA page. Search the dataset of interest from the dataset catalogue or search bar. Click on “Request access” Add a comment requesting these files and send your request Your request is successfully registered, and the DAC will receive a notification about your request In addition to submitting your request, you should also contact the DAC directly via e-mail. You can access the DAC's contact information from the relevant dataset page. Frequently Asked Questions (FAQ) Which is the role of the EGA in the data access process? The European Genome-phenome Archive (EGA) facilitates the secure distribution under controlled access of personally identifiable genetic and phenotypic data. Data Access Committee (DAC) is a body of one or more individuals who are responsible for data release to external requestors, based on participant consent and/or National Research Ethics terms. How do I request access to multiple datasets? To request access for multiple datasets managed by the same Data Access Committee (DAC), please start by visiting the DAC catalogue to locate the DAC of your choice. Once you've found the relevant DAC, proceed to its dedicated page where you'll find a "Request access" button. After logging in to the EGA website, you'll be presented with a comprehensive list of datasets under the chosen DAC. Select the datasets you're interested in, and then compose a message indicating your access request. Click the "Send" button to submit your request, which will include the chosen datasets. Please be sure to review the data usage policies for the requested datasets, as policies can vary among datasets managed by the same DAC. I am a co-applicant. Will I be given a joint access account? Each applicant needs to create an EGA account themselves, with unique login details. User log-in details are for individual use only and should never be shared, under any circumstances. EGA services are only provided to EGA account holders. Therefore, the co-applicant will send a formal request to the DAC. What happens if a co-applicant leaves or a new member needs to be added to an existing DAC application? The appropriate Data Access Committee (DAC) should be contacted and informed about the required changes so that the application can be updated. I have changed my place of work since my EGA account was created. Can I still have access to data? You must contact the relevant Data Access Committee (DAC). The DAC did not reply to my data access request. How should I proceed? There is no estimated time for DAC response. Please allow up to four weeks for a response. In the meantime, please send them a weekly reminder of the status of your request by emailing them directly.If the DAC becomes unresponsive, meaning that they fail to communicate with a requestor within the specified time frame, please contact the EGA Helpdesk team. I have an account with the EGA and would like access to further datasets. How should I proceed? You need to reapply to the relevant Data Access Committee (DAC). Once the DAC has approved your application your account will be updated and an email will be sent to notify you of a change made to your account. How do I download files of the datasets I have been granted access to? The preferred method for data download and decryption is the EGA Secure Download Client. Please carefully review the provided documentation. How do I download metadata? Metadata is retrieved from the EGA webpage; you will not need to use any download client. Be informed that you will only be able to download metadata of your approved datasets. For such download, you should first log in the EGA webpage with your credentials. Then browse the metadata's dataset you have gained access and you will find a metadata download button. You may also want to have a look our metadata schema. I have forgotten or misplaced my user account password; how do I get a new one? Click on “Log in”, located in the top right corner of the EGA homepage. Then click “Forgot password”, type in your registered institutional email address, and instructions for resetting your password will be sent to you. Please check both regular and junk/spam folders. I was sent a link to set a password for my download account, but it seems like the link has expired. What should I do? The procedure is the same as the one for a forgotten password, explained in the previous question.

  Documentation access/request-data/how-to-request-data

• Short and long-read genome sequencing methodologies for somatic variant detection; genomic analysis of a patient with diffuse large B-cell lymphoma

  Recent advances in throughput and accuracy mean that the Oxford Nanopore Technologies (ONT) PromethION platform is a now a viable solution for WGS. New bioinformatic methods have been developed to take advantage of this long read data, however much of the validation of these tools has focussed on calling germline variants (both SNVs and structural variants). Somatic variants are outnumbered many-fold by germline variants and their detection is further complicated due to their frequency varying depending on tumour purity/subclonality. Here, we evaluate the extent to which Nanopore WGS enables genome-wide detection and analysis of somatic variation. We do this through sequencing tumour and germline genomes for a patient with diffuse B-cell lymphoma. We examine the capability of currently available tools for calling somatic variants in ONT data by comparing the data with results from 150bp short-read sequencing of the same samples. We then conduct a detailed analysis of the performance of multiple long-read mappers and structural variant callers for calling large, somatic structural variants (SVs) in ONT data. Our protocol achieved yields of up to 96 mapped Gb per PromethION flow cell with average read lengths of ~5kb. Calling germline SNVs from these data achieved good specificity and sensitivity. However, results of somatic SNV calling highlight the need for the development of specialized joint calling algorithms. Our analysis of structural variants shows that the comparative performance of different tools varies significantly between SV types, and suggest long reads are especially advantageous for calling large somatic deletions and duplications. Finally, we highlight the utility of long reads for phasing clinically relevant variants by using the ONT data to confirm that a somatic 1.6Mb deletion and a p.(Arg249Met) mutation involving TP53 are oriented in trans.

  Study EGAS00001004266

• dic(9;20) pediatric ALL with DNMT3B rearrangement - arrayCGH experiment

  Array CGH derived copy number variations from dicentric chromosome dic(9;20) positive pediatric Acute lymphocytic leukemia B-lymphocyte samples, by utilizing an Agilent 400K SurePrint G3 Custom CGH Human Genome Microarray (e-Array design 84704)

  Dataset EGAD00010002564

• RNA-seq bulk

  This dataset gather all RNA-sequencing data in MM.1S cell line in control and Dex condition; both in 3 biological replicates

  Dataset EGAD00001011137

• Independent development of lymphoid and histiocytic malignancies from a shared early precursor

  Whole Exome Sequencing data from the germline of the patient as well as the tumors in bone marrow (T-ALL), Liver (Histiocytic Sarcoma) and ileum (non-Langerhans Cell Histiocytosis).

  Dataset EGAD00001001596

• Genomic and Transcriptomic sequencing of neuroblastoma cell lines

  Genomic and Transcriptomic sequencing of neuroblastoma HSR and ecDNA cell lines. Data represents five 96-well plates that were processed with G&T sequencing, resulting in genomic and transcriptomic data from the same single cells. Dataset contains 95 bam files containing the DNA sequencing data and 95 bam files containing the RNA sequencing data of CHP212 cells, 380 bam files (190 DNA and 190 RNA) for TR14 cells, 188 bam files (94 DNA and 94 RNA) for Kelly cells and 192 bam files (96 DNA and 96 RNA) for IMR5/75 cells.

  Dataset EGAD50000000729

• RNA sequencing data for Molecular Characterization of ETMRs

  The dataset contains two samples from one patient. As a representative FFPE tissue sample, ET174 was histologically iden- tified, targeted and microdissected with a puncher for nucleic acid extraction. RNA was extracted using the automated Maxwell system with the Maxwell 16 LEV RNA FFPE Kit (Promega), according to the manufacturer’s instructions. To evaluate FFPE RNA quality, we used the percentage of RNA fragments >200 nt fragment determination value (DV200). Only RNA samples with DV200 > 70% were included for sequencing on a NextSeq 500 (Illumina). Eight lanes have been sequenced resulting in 16 Fastq files (paired end).

  Dataset EGAD00001006209

• Functional Significance of Prostate Cancer Risk-SNPs

  Prostate Cancer (PrCa), the most frequently diagnosed solid tumor in men in the U.S., results in ~192,000 new cases and ~27,000 deaths per year. Although the variation of PrCa incidence is likely to be the result of several factors, there is a large body of literature that strongly implicates a genetic etiology. Genome-wide association studies (GWAS) have emerged as the most widely used contemporary approach to identify genetic variants (in particular SNPs) that are associated with increased risk of human disease. For PrCa, at least five GWAS have now been performed yielding a substantial number of well-validated SNPs that are associated with an increased risk of PrCa, and that number continues to grow. A significant problem for many of the PrCa risk-SNPs identified so far, however, is that they do not lie within or near a known gene and they have no obvious functional properties. These findings suggest that many (if not most) of these risk-SNPs will be located in regulatory regions that control gene expression rather than in coding regions that may directly affect protein function. Therefore, in order to define the functional role of currently known risk-SNPs, the target genes must first be identified. A promising strategy to address this problem involves the use of expression quantitative trait loci (eQTL) analysis. Unfortunately, most of the publically available SNP-Transcript eQTL datasets utilize lymphoblastoid cells with only a handful using tissue from target organs. Although useful, these datasets alone are unlikely to be sufficient. Recent studies have demonstrated that gene expression and gene regulation occur in both a tissue-specific and tissue independent fashion and suggest that a complete repertoire of regulatory SNPs can only be uncovered in the context of cell type specificity. To date, such a tissue-specific dataset for normal prostate tissue does not exist. In this study, we have constructed a normal prostate tissue specific eQTL data set.

  Study phs000985