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• 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

• 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

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__pancreas_LCM_

  Samples prepared by LCM - 5 cases for pilot study. Bulk DNA not available.

  Study EGAS00001003197

• Rhabdoid tumor sequencing data

  This study contains sequencing DNA and RNA sequencing data for rhabdoid tumors

  Study EGAS00001006351

• ChIPseq Sequencing data for epigenetic subgroups of meningioma

  Bam files for 16 meningioma tumor samples; ChIPseq performed on Illumina HiSeq 2000

  Dataset EGAD00001005021

• DATA FILES FOR BALL-PAX5

  DATA FILES FOR BALL-PAX5

  Dataset EGAD00001000654

• WGBS files for PCGP NBL_MYCN_ATRX

  WGBS files for PCGP NBL_MYCN_ATRX paper titled "MYCN Amplification and ATRX Mutations are Incompatible in Neuroblastoma"

  Dataset EGAD00001004559

• Dataset for "Genomic landscape of oral cancers" (Illumina RNA)

  Dataset for "Genomic landscape of oral cancers" (Illumina RNA)

  Dataset EGAD00001004366

• HLA sequence data and final calls for VaccGene and 1000Gp3 African populations

  HLA sequence data and final calls for VaccGene and 1000Gp3 African populations

  Dataset EGAD00001011379

• WES data for study of the microenviroment of angioimmunoblastic T-cell lymphoma

  Exome sequencing data for study of the microenviroment of angioimmunoblastic T-cell lymphoma

  Dataset EGAD00001011581

• Genotyping NIGMS Chromosomal Aberration and Inherited Disorder Samples

  As part of an effort to correlate molecular copy number variation determinations with state of the art karyotype analyses, 716 samples derived from 697 individuals from the Chromosomal Aberrations and Inherited Disorders collections of the NIGMS Human Genetic Cell Repository were genotyped and analyzed for CNV determination by the Microarray Center at the Coriell Institute for Medical Research. Karyotyping is performed on all cell cultures in the Repository with reported chromosome abnormalities. The samples chosen for genotyping in this study are intended to represent a diverse set of copy number variants, but the selection was also weighted to over-sample commonly manifested types of aberrations. When available, the ISCN description of the sample based on G-banding and FISH analysis is included in the phenotypic data. Karyotypes for these cells can be viewed in the online Repository catalog (http://ccr.coriell.org/Sections/Collections/NIGMS/?SsId=8).

  Study phs000269

• Validation of Gene Array to Predict Bacterial Co-infection In Adults Hospitalized with Viral Lower Respiratory Tract Infections (LRTI)

  Accurate tests for microbiologic diagnosis of lower respiratory tract infections (LRTI) are needed. Gene expression profiling of whole blood represents a powerful new approach for analysis of the host response during respiratory infection that can be used to supplement pathogen detection testing. Using qPCR, we prospectively validated the differential expression of 10 genes previously shown to discriminate bacterial and non-bacterial LRTI confirming the utility of this approach. In addition, a novel approach using RNAseq analysis identified 141 genes differentially expressed in LRTI subjects with bacterial infection. Using "pathway-informed" dimension reduction, we identified a novel 11 gene set (selected from lymphocyte, α-linoleic acid metabolism, and IGF regulation pathways) and demonstrated a predictive accuracy for bacterial LRTI (nested CV-AUC=0.87). RNAseq represents a new and an unbiased tool to evaluate host gene expression for the diagnosis of LRTI.

  Study phs001248

• 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

• PLCO - Limited Use Pilot Test Data

  This is a sample of Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial data for limited use in a pilot test for remote data access. The PLCO is a randomized, controlled trial of screening tests for prostate, lung, colorectal and ovarian cancers. Approximately 155,000 participants were enrolled between November 1993 and July 2001. Participants were individually randomized to the control arm or intervention arm in equal proportions. Participants assigned to the control arm received usual care, whereas participants assigned to the intervention arm were invited to receive screening exams for prostate, lung, colorectal and ovarian cancers as outlined in the study protocol. The goal of the study was to assess whether these screening exams reduce mortality from prostate, lung, colorectal and ovarian cancers. Data were collected on cancer diagnoses and deaths from all causes that occurred through December 31, 2009. Median follow-up time was 12.4 years.

  Study phs002011

• The Genetic Evolution of Acral Melanoma

  Acral melanoma is a rare melanoma subtype arising on the sole, palm, or nail apparatus. We performed whole-exome sequencing (WES) for a cohort of 35 acral melanoma patients. For each patient, we sequenced multiple tumor areas representing different tumor stages, including the in situ and invasive portions of the primary tumor and available regional or distant metastases. Normal areas were included for all except one patient. We identified distinct evolutional trajectory compared to melanomas arising on the sun-exposed skin. Whole-genome sequencing and targeted sequencing were additionally performed for a subset of the samples. Particularly, in acral melanomas, complex genomic aberrations, characterized by clustered copy number transitions and focal amplifications, and telomerase reverse transcriptase (TERT) activating alterations occur very early during evolution, while MAP-kinase pathway mutations, such as BRAF V600E and NRAS hotspot mutations, can occur late and often result in striking intra-tumor heterogeneity.

  Study phs003451

• 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

• APOL1 Risk Variants Induce Metabolic Reprogramming of Podocytes in Patient-Derived Kidney Organoids

  Apolipoprotein L1 (APOL1) high-risk genotypes G1 and G2 are responsible for the increased risk of chronic kidney disease (CKD) in the African-American population, especially in the presence of interferon gamma (IFN-γ). Here, we applied single cell transcriptomics on kidney organoids generated from patient-derived induced pluripotent stem cells (iPSCs) to model human APOL1 nephropathy. iPSCs were generated from fibroblasts of two patients with APOL1 nephropathy homozygous for G1 or G2 risk variants, and an isogenic control (G0) was created by gene correction. Kidney organoids were generated according to previously published protocol (Takasato M et al. Nat. Protoc. 2016) and treated with IFN-γ for two days to induce APOL1. Twenty-four hours after IFN-γ removal, treated organoids from all three lines that were cultured in parallel were harvested for single cell transcriptomics.

  Study EGAS50000001223

• Exploring Extracellular Vesicle microRNAs in Usher Syndrome Type 1B: Tear-Derived EVs as Indicators of Retinal Health

  The goal of this study was to explore extracellular vesicles (EVs) from human tears and iPSC-derived RPE cells as biomarkers for Usher syndrome type 1B (USH1B), a rare inherited disorder causing deafness and retinitis pigmentosa. Researchers found that tear EVs showed higher levels and diversity of miRNAs, making them a promising non-invasive source for diagnosing USH1B. In contrast to patient-derived RPE cells, which did not express MYO7A protein despite similar expression levels in controls, the study identified specific microRNA signatures associated with retinal degeneration in both tear EVs and RPE-derived EVs. The findings suggest that tear EVs can serve as a useful biomarker for USH1B diagnosis, monitoring, and therapeutic development. Overall, this research aims to contribute to our understanding of disease mechanisms and identify potential targets for treatment in patients with Usher syndrome type 1B.

  Study EGAS50000001200

• Measuring the level of relatedness between NGS datasets

  Sequencing technologies are providing increasingly detailed insight in genetic makeup are paving their way into molecular diagnostics. The field will benefit from rigorous and bias-free measures for the quality of sequence data and for the proper representation of the complexity of the original samples. While current methodologies rely on the availability of a well-characterized reference genome, we propose kMer profiling for alignment-free assessment of the quality, comparability, and complexity of sequencing datasets. We show that kMer detects technical artefacts such as high duplication rates, library chimaeras, and differences in library preparation protocols in whole-genome, whole-exome, and RNA sequencing data. Additionally, it successfully captures the complexity and diversity of microbiomes. Thus, kMer allows for a robust evaluation of the quality and complexity of sequencing data without relying on any prior information and opens the way to a more reliable biological reasoning.

  Study EGAS00001000600

• Determining genome-wide binding of ETS2 by CUT&RUN in primary human macrophages

  This dataset consists of sequencing data from an ETS2 CUT&RUN experiment in primary inflammatory (TPP) macrophages (n = 2). Pre-cultured TPP macrophages were harvested and processed immediately using the CUT&RUN Assay kit (Cell Signaling) according to the manufacturer’s instructions with the following modifications (essentially, avoiding the use of ConA-coated beads). Anti-ETS2 (ThermoFisher) or IgG control (Cell Signaling) antibodies were used for targeted digestion of chromatin. For each donor, 5x10^5 cells were pelleted, washed in Wash Buffer, and resuspended in Antibody Binding buffer. Cells were incubated with antibodies (1:100 dilution for anti-ETS2) for 2h at 4°C. After washing in Digitonin Buffer, cells were incubated with pA/G-MNase for 1h at 4°C. Cells were washed twice in Digitonin Buffer, resuspended in the same buffer and cooled for 5 minutes on ice. Calcium chloride was added to activate pA/G-MNase digestion and cells were incubated for 30 minutes at 4°C before Stop Buffer was added, and cells were incubated for 10 min at 37°C to release cleaved chromatin fragments. Supernatants were collected by centrifugation and DNA extracted using DNA Purification Buffers and Spin Columns (Cell Signaling). Library preparation was performed according to a protocols.IO protocol (dx.doi.org/10.17504/protocols.io.bagaibse) using the NEBNext Ultra II DNA Library Prep Kit. Size selection was performed using AMPure XP beads (Beckman Coulter) and fragment sizes were determined using an Agilent 2100 Bioanalyzer (High Sensitivity DNA kit). Equimolar pools of indexed libraries were sequenced on an Illumina NovaSeq 6000 (100bp PE reads). Raw data are provided as 100 bp paired-end Illumina reads.

  Dataset EGAD00001011349

• Identification_and_functional_validation_of_driver_mutations_in_colorectal_cancer

  Targeted exome sequencing of human colorectal cancers for the identification of novel cancer drivers.

  Study EGAS00001000044

• Carcinoma of the oral tongue (OTSCC) genomic landscape characterisation

  18 Exomes for discovery set and 60 Targeted panel for prevalence set

  Dataset EGAD00001001465

• Array data for oesophageal and related samples - aks_paper_methyl_barretts_release

  Array data for oesophageal and related samples - aks_paper_methyl_barretts_release

  Dataset EGAD00010001972

• Methylation files for Roussel-ATRT-TM

  Methylation files for Roussel-ATRT-TM paper titled "Atypical teratoid/ rhabdoid tumoroids reveal subgroup-specific drug vulnerabilities"

  Dataset EGAD00010002357