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• GIS-LUNGTCR1-2016_VAL-FASTQ

  This dataset contains FASTQ files of targeted Amplicon deep-sequencing data, for validation of the mutations found in WES. There are 16 patients and 95 samples in total, including 16 controls and 79 tumors. 140 fastq in total, multiple runs for some of the samples. Please refer to the sample-ID from filename for merging.

  Dataset EGAD00001001981

• ChIP-seq fastq and alignment files

  ChIP-seq experiments: fastq files; both ChIP and Input for each sample. Illumina HiSeq 2500. ChIP-seq alignment files for trimmed, mapping q20 and nonredundant reads; both ChIP and Input for each sample. Software: Trim Galore v0.3.7; Bowtie 2 v2.1.0; samtools v1.7

  Dataset EGAD00001006279

• FASTQ file for paper titled "Discovery of immunotherapy targets for pediatric solid and brain tumors by exon-level expression"

  This data set includes 27 full-length transcript sequence generated from PacBio IsoSeq that were used for verify the cancer-specific exons identified in three genes: FN1, COL6A3 and TNC. The data were generated from PDX models of osteosarcoma patients.

  Dataset EGAD00001015356

• DAC for hepatoblastoma sequencing data

  Data Access Committee for sequencing data of hepatoblastoma PDX cell models sequenced under University of Helsinki.

  Dac EGAC50000000548

• RNA-seq data for NRF2 study

  RNA-seq data for clinical samples

  Dataset EGAD00001002243

• TTN gene targeted sequencing for AMC cohort

  TTN gene targeted sequencing for AMC cohort (n=24)

  Dataset EGAD00001005497

• 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

• National Cancer Institute (NCI) Prostate Cancer Genome-Wide Association Study for Uncommon Susceptibility Loci (PEGASUS)

  This genome-wide association study was funded by the National Cancer Institute (NCI) to identify uncommon susceptibility loci for prostate cancer. A total of 4,600 prostate cancer cases and 2,840 controls of European ancestry from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial were genotyped using the Illumina HumanOmni2.5 and passed rigorous quality control filters. Additional genotype data (available in dbGap under other accession numbers) from 101 independent controls of European ancestry scanned with the HumanOmni2.5 were also included, resulting in a total of 4,600 cases and 2,941 controls for the published analysis. SNPs from the most promising regions, as determined by rank p-value, under multiple different models as well as select candidate genes were taken forward for replication using a custom Iselect chip in 6,575 cases and 6,392 controls of European ancestry. Results from the primary scan after imputation were then meta-analyzed with the Iselect results as well as results from previous GWAS. In a combined meta-analysis of the primary scan together with the custom Iselect replication and a previous GWAS, thirteen loci reached genome-wide significance (P < 5 x 10-8) for prostate cancer overall; however, each of them confirmed a previously reported locus. Although they did not reach genome-wide significance, we found evidence for two new suggestive loci at chromosome 16q22.2 (PKD1L3, rs12597458, P = 9.67 x 10-8) and 6p22.3 (CDKAL1, rs12198220, P = 2.13 x 10-7). In a combined case-only analysis of 12,518 prostate cancer cases, we identified two loci associated with Gleason score, a pathological measure of disease aggressiveness: rs35148638 at 5q14.3 (RASA1, P=6.49x10-9) and rs78943174 at 3q26.31 (NAALADL2, P=4.18x10-8). In a stratified case-control analysis, the SNP at 5q14.3 appears specific for aggressive prostate cancer (P=8.85x10-5) with no association for non-aggressive prostate cancer compared to controls (P=0.57). Only the cases and controls genotyped on the HumanOmni2.5 specifically for this study are included under this accession number. Controls (n=101) genotyped with the HumanOmni2.5 for another study are posted under a different accession number. Please note that the majority of prostate cancer cases and controls genotyped in CGEMS (and posted under a different accession number) are included in this study.

  Study phs000882

• Genetic Components of Knee Osteoarthritis (GeCKO) Study: The Osteoarthritis Initiative

  The Osteoarthritis Initiative (OAI) is a publicly and privately funded prospective longitudinal cohort with a primary objective of identifying risk factors for incidence and progression of tibiofemoral knee OA. The OAI utilized a focused population-based recruitment to enroll 4,674 men and women between the ages of 45-79 years who either had radiographic symptomatic knee OA or who were without radiographic symptomatic OA in both knees but were considered high risk for OA because they had two or more known risk factors for knee OA. Subjects were recruited into the baseline phase of the OAI at multiple sites throughout the US between 2004 and 2006. All subjects were invited back for follow-up examinations to assess incidence or progression of OA annually, for up to 5 years. Phenotype data from the baseline and follow-up examinations are available for public access from the Osteoarthritis Initiative (OAI) database at http://www.oai.ucsf.edu/. The Genetic Components of Knee Osteoarthritis (GeCKO) Study was initiated in 2009 as a genetic ancillary study to perform a genome-wide association study to identify genetic variants associated with radiographic osteoarthritis. This study included 4,482 individuals participating in the parent OAI study. Following sample cleaning (e.g., removal of duplicates, sex mismatches, poor sample quality, etc.), the final sample set included 4,129 individuals.

  Study phs000955

• Symptom Clusters in Oncology Patients Receiving Chemotherapy

  The parent longitudinal study evaluated the symptom experience of oncology outpatients receiving chemotherapy. Patients completed questionnaires in their homes a total of six times (assessments 1 through 6). The questionnaires were timed to evaluate symptoms associated with the receipt of two cycles of chemotherapy that were administered approximately a month apart. Assessments 1 and 4 evaluated symptoms prior to the receipt of chemotherapy. Assessments 2 and 5 evaluated symptoms in the week following the administration of chemotherapy. Assessments 3 and 6 evaluated symptoms two weeks following the administration of chemotherapy. For this analysis, morning and evening fatigue data from the enrollment assessment (i.e., prior to the second or third cycle of chemotherapy) and the assessment that was done approximately 1 week after administration of chemotherapy were evaluated. Patients completed the Lee Fatigue Scale to obtain information on morning and evening fatigue severity. For the morning fatigue assessment, patients were asked to describe how they “felt over the past week when they woke up in the morning”. For the evening fatigue assessment, they were asked to describe how they felt over the past week “before they went to bed at night”. Medical records were reviewed for disease and treatment information. Epigenome assays (microarray) were completed for 1170 patients. Whole transcriptome sequencing (bulk RNA-Seq) was completed for 651 patients.

  Study phs003863

• Targeted de-methylation of the FOXP3-TSDR

  CD4+ regulatory T cells (Tregs) are key mediators of immunological tolerance and promising effector cells for immuno-suppressive adoptive cellular therapy to fight autoimmunity and chronic inflammation. Their functional stability is critical for their clinical utility and has been correlated to the demethylated state of the TSDR/CNS2 enhancer element in the Treg lineage transcription factor FOXP3. However, proof for a causal contribution of the TSDR de-methylation to FOXP3 stability and Treg induction is so far lacking. We here established a powerful transient-transfection CRISPR-Cas9-based epigenetic-editing method for the selective de-methylation of the TSDR within the endogenous chromatin environment of a living cell. The induced de-methylated state was stable over weeks in clonal T cell proliferation cultures even after expression of the editing complex had ceased. Epigenetic editing of the TSDR resulted in FOXP3 expression, even in its physiological isoform distribution, proving a causal role for the de-methylated TSDR in FOXP3 regulation. However, successful FOXP3 induction was not associated with a switch towards a functional Treg phenotype, in contrast to what has been reported from FOXP3 overexpression approaches. Thus, TSDR de-methylation is required, but not sufficient for a stable Treg phenotype induction. Therefore, targeted demethylation of the TSDR may be a critical addition to published in vitro Treg induction protocols which so far lack FOXP3 stability.

  Study EGAS00001004867

• Center for Cancer Genomics (CCG) Cancers of Unknown Primary Project (CUPP)

  The Cancers of Unknown Primary Project (CUPP) aims to characterize difficult tumor types with the goal of determining future best treatment practices. NCI will utilize whole exome sequencing, copy-number variation, DNA methylation in conjunction with transcriptome sequencing to provide a comprehensive genomic landscape of Cancers of Unknown Primary (CUP). The samples will be processed and submitted for genomic characterization using pipelines and procedures established within the Center for Cancer Genomics (CCG).

  Study phs001801

• Susceptibility loci for tanning ability in Japanese population identified by genome-wide association study

  We performed genome-wide association studies of Japanese Skin Type for Japanese individuals from a cohort study by the Tohoku Medical Megabank Project in order to identify variants related to tanning ability and skin pigmentation. We used imputed genotype data of 9,187 Japanese individuals, of which 4,475 individuals are from Miyagi prefecture and 4,712 individuals are from Iwate prefecture. Skin Type information was obtained from the questionnaire for cohort participants.

  Study JGAS000160

• Whole Genome Sequencing from patients with multiple myeloma treated with BCL2 inhibitor based treatment

  Samples was collected at the Mayo Clinic. MM tumor samples were enriched using the StemCell EasySep Human CD138 positive selection kit II (StemCell, Cambridge, MA). DNA from CD138+ MM cell (tumor) or peripheral blood samples (germline control) at Mayo Clinic (Rochester or Arizona) underwent WGS. For samples collected at Mayo Clinic Rochester, library preparation was completed using the NEB Ultra II (New England Biolabs, Ipswich, MA) and the Nextera Flex systems (Illumina, San Diego, CA) and sequenced at the Mayo Clinic Genome Analysis Core with an approximate depth of 30X for tumor samples and 60X for the germline control. For samples collected at Mayo Clinic Arizona, Novogene was used for WGS following random shearing of the DNA, end-repairs, A-tailed and ligated with Illumina adapters. Sequencing was performed on the Illumina Novaseq 6000 sequencer Reference Genome: GRCh38.

  Dataset EGAD50000002132

• Mutational landscape of the transcriptome offers putative targets for immunotherapy of myeloproliferative neoplasms

  In this study, we aimed to comprehensively characterize in 113 myeloproliferative neoplasms (MPN) patients the mutational landscape of the granulocyte transcriptome using RNA sequencing data. We implemented workflows for fusion and variant calling, and differential splicing analysis for patients with hotspot SF3B1 mutations. Finally, we characterized the neoantigen landscape on individualized level taking each patient's HLA genotype (derived from the same RNA-seq dataset) into account.

  Study EGAS00001003486

• Whole exome sequencing for clarification of rare causes of axonal Charcot-Marie-Tooth disease (2017-08-16)

  The project is focused on the axonal forms of Charcot-Marie-Tooth (CMT) disease. We have selected 13 families (7 from Spain and 6 from Czech Republic) that have been indepth clinically assessed and previously tested for mutations in known CMT genes without causal variants characterised. In these patients we expect to discover several CMT2 genes. Thus, we requested for exome sequencing of 45 DNAs:27 exomes in families from Spain and 18 exomes in the families from Czech Republic. 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/ This dataset contains all the data available for this study on 2017-08-16.

  Dataset EGAD00001003565

• Genome-wide DNA Methylation Data from Illumina HumanMethylationEPIC arrays for whole blood samples from 403 healthy individuals

  Genome-wide DNA Methylation Data from Illumina HumanMethylationEPIC arrays for whole blood samples from 403 healthy individuals. Additional associated phenotype information is available for all individuals included in this study directly from CIBMTR. Data are available under controlled access release upon reasonable request and execution of a data use agreement. Requests should be submitted to CIBMTR at info-request@mcw.edu and include the study reference IB17-04.

  Study EGAS00001006033

• Saliba Lab Data Access Committee

  DAC for data generated by Saliba Lab at Helmholtz institute for RNA-based Infection Research, Würzburg, Germany

  Dac EGAC50000000152

• Screening for abnormal CGI methylation in primary colorectal tumours

  Screening for abnormal CGI methylation in primary colorectal tumours

  Dataset EGAD00001000213

• RNA-Seq data for Juvenile Pilocytic Astrocytomas

  RNA-Seq data for 9 JPA samples.

  Dataset EGAD00001009043

• Whole exome sequencing of ATCWGS42 primary tumour and PDX

  Genomic DNA was isolated from snap-frozen tissue and subjected to whole genome sequencing at The Center for Applied Genomics (Hospital for Sick Children, Toronto, ON) or at Beijing Genomic Institute (BGI). High-coverage whole exome-sequencing was performed at GeneSeeq Inc. (Toronto, Ontario, Canada) using IDT xGen exome panel.

  Study EGAS50000001490

• RNA Sequencing of AD OM cells exposed to traffic-related air pollutants

  Total RNA sequencing of olfactory mucosa (OM) cells derived from individuals diagnosed with Alzheimer's disease exposed to traffic-related ultrafine particles (UFPs) for 24-h and 72-h in submerged cultures. The UFPs used for exposures were: A0, A20 and Euro6. Exposures were compared to the corresponding blank samples.

  Study EGAS50000000448

• WES for 45 patients with pleural mesothelioma (Matched)

  The dataset contains WES data (bam files) for 45 pleural mesothelioma samples. Nextera Flex for Enrichment solution (Illumina, San Diego, CA) in combination with SureSelect Human All Exon V7 probes (Agilent, Santa Clara, CA) was used for library preparation and generated libraries were sequenced on NovaSeq 6000 (Illumina, San Diego, CA) in 150 pair-end mode. Quality control of the raw data was conducted using fastQC (v. 0.11.8) (https://www.bioinformatics. babraham.ac.uk/projects/fastqc/). WES data were then analyzed with the DRAGEN Bio IT platform 49 (version 4.2.4) and the human GRCh38 as reference genome.

  Dataset EGAD50000002128

• WES for 42 patients with pleural mesothelioma (Not matched)

  The dataset contains WES data (bam files) for 42 pleural mesothelioma samples. Nextera Flex for Enrichment solution (Illumina, San Diego, CA) in combination with SureSelect Human All Exon V7 probes (Agilent, Santa Clara, CA) was used for library preparation and generated libraries were sequenced on NovaSeq 6000 (Illumina, San Diego, CA) in 150 pair-end mode. Quality control of the raw data was conducted using fastQC (v. 0.11.8) (https://www.bioinformatics. babraham.ac.uk/projects/fastqc/). WES data were then analyzed with the DRAGEN Bio IT platform 49 (version 4.2.4) and the human GRCh38 as reference genome.

  Dataset EGAD50000002127

• Comprehensive molecular phenotyping of ARID1A-deficient gastric cancer reveals pervasive epigenomic reprogramming and therapeutic opportunities

  This dataset includes paired tumor-blood whole exome sequencing data for 209 gastric cancer (GC) patients, along with whole transcriptome sequencing data for 125 GC samples. Whole exome sequencing was conducted using Agilent SureSelect Human All Exon V6 kits. For RNA sequencing, total RNA was isolated using the RNeasy Mini Kit, and libraries were prepared with the TruSeq Stranded Total RNA with Ribo-Zero Gold protocol (Illumina). Aligned BAM files for both exome and RNAseq data are included in this dataset.

  Dataset EGAD50000000660