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• ChIP-Seq (H3K9me3) assays for reference epigenomes generated by Centre for Epigenome Mapping Technologies at Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, as part of the International Human Epigenome Consortium.

  ChIP-Seq (H3K9me3) assays for reference epigenomes generated by Centre for Epigenome Mapping Technologies at Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001234

• ChIP-Seq (H3K27ac) assays for reference epigenomes generated by Centre for Epigenome Mapping Technologies at Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, as part of the International Human Epigenome Consortium.

  ChIP-Seq (H3K27ac) assays for reference epigenomes generated by Centre for Epigenome Mapping Technologies at Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001229

• March 2016 update of Whole genome bisulfite sequencing assay data (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  March 2016 update of Whole genome bisulfite sequencing assay data (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001957

• ChIP-Seq (H3K27me3) assays for reference epigenomes generated by Centre for Epigenome Mapping Technologies at Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, as part of the International Human Epigenome Consortium.

  ChIP-Seq (H3K27me3) assays for reference epigenomes generated by Centre for Epigenome Mapping Technologies at Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001230

• March 2016 update of whole genome shotgun sequencing data (bam/fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  March 2016 update of whole genome shotgun sequencing data (bam/fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001958

• March 2016 update of smRNA-Seq assays data (bam/fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  March 2016 update of smRNA-Seq assays data (bam/fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001959

• Comparison of protocols for deriving pancreatic progenitors from hPSCs (ATAC-seq)

  This dataset contains 27 mapped bam files. The samples were generated with 3 different protocols for deriving pancreatic progenitors from hPSC. Three parallel differentiations were performed, all done in a hPSC NKX6.1-GFP reporter line. For each protocol there are three cellular populations: total (presort), GFP+ and GFP- . In summary: 3 differentiations x 3 protocols x 3 cellular populations. We prepared ATAC-seq libraries for the 27 samples, and sequenced them on Illumina HiSeq4000.

  Dataset EGAD00001004824

• Compilation of Aggregate Genomic Data for General Research Use

  This study contains all authorized aggregate genomic data from datasets currently in dbGaP that are approved for general research use (GRU) and have no further limitations beyond those outlined in model Data Use Certification Agreement. Access to this study will be granted for one year and any additional authorized aggregate GRU datasets that become available during this one-year period. This study does not include individual-level data.

  Study phs000501

• Meta-analysis of Genome-Wide-Association Sudies for plasma Factor XI

  Coagulation factor XI (FXI) has become increasingly interesting for its role in pathogenesis of thrombosis. We performed a genome-wide association study (GWAS) for plasma FXI levels, using novel data imputed to the 1000 Genomes reference panel. Individual GWAS analyses, including a total of 16,169 European individuals from the ARIC, GHS, MARTHA and PROCARDIS studies, were meta-analyzed, and results are presented here.

  Study EGAS00001002123

• DATA FILES FOR SJOS

  DATA FILES FOR SJOS

  Dataset EGAD00001000159

• DATA FILES FOR SJLGG

  DATA FILES FOR SJLGG

  Dataset EGAD00001000161

• Screening for abnormal CGI methylation in primary colorectal tumours

  Screening for abnormal CGI methylation in primary colorectal tumours

  Dataset EGAD00001000213

• DATA FILES FOR SJLGG

  DATA FILES FOR SJLGG

  Dataset EGAD00001000352

• DATA FILES FOR SJLGG

  DATA FILES FOR SJLGG

  Dataset EGAD00001000353

• Saliba Lab Data Access Committee

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

  Dac EGAC50000000152

• RNA-Seq data for Juvenile Pilocytic Astrocytomas

  RNA-Seq data for 9 JPA samples.

  Dataset EGAD00001009043

• WGS and WXS files for Dyer ATRX study

  WGS and WXS files for Dyer ATRX study

  Dataset EGAD00001003389

• Substantial somatic genomic variation and selection for BCOR mutations in human induced pluripotent stem cells

  Human Induced Pluripotent Stem Cells (hiPSC) are an established patient-specific model system where opportunities are emerging for cell-based therapies. We compared and contrasted hiPSCs derived from different tissues, skin and blood, in the same individual. We show extensive single-nucleotide mutagenesis in all hiPSC lines, although fibroblast-derived hiPSCs (F-hiPSCs) are particularly heavily mutagenized by ultraviolet (UV)-related damage. We utilized genome sequencing data on 454 F-hiPSCs and 44 blood-derived hiPSCs (B-hiPSCs) to gain further insights. Across 324 whole genome sequenced (WGS) F-hiPSCs derived by the Human Induced Pluripotent Stem Cell Initiative (HipSci), UV-related damage is present in ~72% of cell lines, sometimes causing substantial mutagenesis (range 0.25-15 per Mb). Furthermore, we find remarkable genomic heterogeneity between independent F-hiPSC clones derived from the same reprogramming process in the same donor, due to oligoclonal populations within fibroblasts. Combining WGS and exome-sequencing data of 452 HipSci F-hiPSCs, we identify 272 predicted pathogenic mutations in cancer-related genes, of which 21 genes were hit recurrently three or more times, involving 77 (17%) lines. Notably, 151 of 272 mutations were present in starting fibroblast populations suggesting that more than half of putative driver events in F-hiPSCs were acquired in vivo. In contrast, B-hiPSCs reprogrammed from erythroblasts show lower levels of genome-wide mutations (range 0.28-1.4 per Mb), no UV damage, but a strikingly high prevalence of acquired BCOR mutations in ~57% of lines, indicative of strong selection pressure. All hiPSCs had otherwise stable, diploid genomes on karyotypic pre-screening, highlighting how copy-number-based approaches do not have the required resolution to detect widespread nucleotide mutagenesis. This work strongly suggests that models for cell-based therapies require detailed nucleotide-resolution characterization prior to clinical application.

  Dataset EGAD00001007029

• Molecular Biomarkers of Obesity and Metformin Response in Endometrial Cancer: Analysis of GOG-0286B

  The primary endpoint for the phase 2 study was progression free survival (PFS), and the primary endpoint for the phase 3 study was overall survival (OS). Data from phase 2 were included in the phase 3 analysis. Tissue was collected from patients with endometrial cancer who participated in the study (NCT02065687). The tissue will be analyzed for genetic data and combined with the clinical data to see if some gene variants are associated with longer PFS or OS, particularly on the metformin arm of the study.

  Study phs002934

• Whole Exome Sequencing in Familial Parkinson Disease

  We have an ongoing study that has recruited families having at least a sibling pair with Parkinson disease (PD). Families have been screened for mutations in known PD causative genes (LRRK2, parkin, etc). Following review of families without a causative mutation, we selected families for whole exome sequencing that had the strongest history of PD and with the most definitive diagnosis. All families have at least 3 affected family members who were evaluated as part of this study. Only affected family members were included for whole exome sequencing.

  Study phs000376

• Deep Sequencing of 3 Cancer Cell Lines on 2 Sequencing Platforms (Illumina HiSeqX and NovaSeq)

  To test the performance of a new sequencing platform, develop an updated somatic calling pipeline and establish a reference for future benchmarking experiments, we performed whole-genome sequencing of 3 common cancer cell lines (COLO-829, HCC-1143 and HCC-1187) along with their matched normal cell lines to great sequencing depths (up to 278X coverage) on both Illumina HiSeqX and NovaSeq sequencing instruments. This dataset can be used for benchmarking purposes by the genomic community and as a reference call set for scientists using these 3 cell lines.

  Study phs001839

• Reconstitution of Human Brain Cell Diversity in Organoids via Four Protocols [time-course bulkRNAseq]

  Time-course bulk-RNA sequencing of brain organoids grown in four distinct growth protocols designed to generate dorsal (D) and ventral (V) forebrain, midbrain (M) and striatum (S) tissue at days 13 (for dorsal and ventral)/16 (for midbrain and striatum), 25, 40, 80 and 120. Per per cell line-protocol-timepoint combination three organoids (biological replicates) were grown. For reference, also three samples of the pluripotent stem cells across cell lines, not yet exposed to any protocol, were sequenced (day 0, pluripotent).

  Study EGAS50000000663

• ctDNA Quantification in Blood Plasma Using Deep Learning Based Fragle Tool

  The project aims to develop an easy to use software named Fragle that can quantify ctDNA in blood plasma. This can help in cancer screening and progress monitoring from simple blood test without any need for tumor biopsy. We aim to make Fragle usable for both low pass whole genome sequencing and targeted sequencing data of cell free DNA. Fragle can work directly on the raw bam files mapped to hg19/GRCh37. We are uploading the bam files used in this study for Fragle evaluation.

  Study EGAS50000000122

• The_Causes_of_Clonal_Blood_Cell_Disorders_Study___SCOR_Custom

  We took a bone marrow aspirate and peripheral blood samples from a healthy patient aged around 60, and use flow cytometry to isolate 100 HSCs, 50 MEPs, and 50 GMPs. We grew these up into colonies, then whole genome sequenced each colony. Somatic mutations act as a unique barcode for each clone. We have designed a panel for targeted resequencing of the mutations that we find. We are now looking for these mutations in the peripheral blood, to see the dynamics of how HSCs contribute to the peripheral blood in health.

  Study EGAS00001002257

• Osteosarcoma mate-pair and RNA-sequencing

  DNA mate pair and RNA sequencing data of conventional osteosarcomas. Mate pair libraries, with average insert sizes of 2-4 kb, were prepared for sequencing using the Nextera Mate Pair Library Preparation Kit. Paired-end 76 base pair reads were generated using an Illumina NextSeq 500 sequencing instrument. Total RNA was enriched for polyadenylated RNA using magnetic oligo(dT) beads. Enriched RNA was prepared for sequencing using the TruSeq RNA Sample Preparation Kit v2 and paired-end 151 base pair reads were generated from the cDNA libraries using an Illumina NextSeq 500 instrument.

  Study EGAS00001003842

• Haukeland University Hospital Data Access Committee for ParkOme-1 datasets archvied in Federated EGA Norway

  Data Access Committee appointed by Haukeland University Hospital for omics datasets for Parkinson's disease and related disorders, generated in studies under the ParkOme-1 project. Omics datasets of different types are generated from fresh frozen brain tissue from individuals with neurodegenerative parkinsonisms and neurologically healthy controls.

  Dac EGAC50000000193

• Genome-wide analysis for non alcoholic fatty liver disease

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

  Study JGAS000126

• 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

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_LPS_T=24hrs_RPMI_T=5days from venous blood, on Genome GRCh38

  ATAC-seq data for 2 sample(s) for monocyte RPMI_LPS_T=24hrs_RPMI_T=5days from venous blood, on Genome GRCh38. 2 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002901

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_BG_T=24hrs_RPMI_T=5days from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_BG_T=24hrs_RPMI_T=5days from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002904

• BLUEPRINT September 2016, ATAC-seq for unswitched memory B cell from venous blood, on Genome GRCh38

  ATAC-seq data for 3 sample(s) for unswitched memory B cell from venous blood, on Genome GRCh38. 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002905

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_BG_T=1hr from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_BG_T=1hr from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002906

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_BG_T=4hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_BG_T=4hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002910

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_T=24hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_T=24hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002921

• BLUEPRINT September 2016, ATAC-seq for germinal center B cell from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for germinal center B cell from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002911

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_T=1hr from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_T=1hr from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002919

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_LPS_T=24hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_LPS_T=24hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002900

• BLUEPRINT September 2016, ATAC-seq for germinal center B cell from tonsil, on Genome GRCh38

  ATAC-seq data for 2 sample(s) for germinal center B cell from tonsil, on Genome GRCh38. 2 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002917

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_T=6days from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_T=6days from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002914

• BLUEPRINT September 2016, ATAC-seq for naive B cell from venous blood, on Genome GRCh38

  ATAC-seq data for 3 sample(s) for naive B cell from venous blood, on Genome GRCh38. 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002902

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_LPS_T=1hr from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_LPS_T=1hr from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002913

• GIS-LUNGTCR1-2016_WES-FASTQ

  This dataset contains FASTQ files for multi-region exome-sequencing of EGFR-mutant lung adenocarcinomas from Asian patient. There are 16 patients and 95 samples in total, including 16 controls and 79 tumors. Multiple runs for each sample, and 368 fastq in total. Please refer to the sample-ID from filename for merging.

  Dataset EGAD00001001978

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_BG_T=24hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_BG_T=24hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002909

• Molecular discrimination for multicentric occurrence and intrahepatic metastasis by whole genome sequencing of multiple liver cancers

  RIKEN collection of WGS reads for 13 multicentric liver cancers or intrahepatic metastasis and matched blood samples for 12 donors.

  Dataset EGAD00001001996

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_LPS_T=4hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_LPS_T=4hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002922

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_T=4hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_T=4hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002899

• 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

• 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

• Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia

  In this study we generated single-cell whole transcriptome and surface marker expression data for 24 samples of 19 AML patients as well as one healthy donor. We followed the CITEseq protocol with the 3' 10x Genomics scRNAseq kit version 3.1 To increase the coverage of the mitochondrial genome we generated mitochondrial libraries following a protocol termed Optimized 10x. Based on TAPseq, we generated libraries to increase the coverage of selected nuclear SNVs. Exome sequencing was generated for 15 patients to identify nuclear variants. Bulk ATAC was obtained for 9 samples to facilitate the discovery of mitochondrial SNVs. MutaSeq (modified version of SmartsSeq2) was performed on cells from 3 patients. Targeted DNAseq from single-cell derived colonies was generated for 1 patient.

  Study EGAS00001007078

• Evolutionary landscape of clonal hematopoiesis in 3359 individuals from the general population

  This project contains longitudinal data for processed somatic variant calls and data on core phenotypic variables for 3359 community-based individuals ≥60 years, a sub-cohort of the population-based Lifelines cohort (167,729 participants). Cases with peripheral blood count abnormalities and population-based controls were included. Next-generation sequencing data were generated with a median time period of 43 months between baseline and follow-up visit. For 327 individuals a third visit sample was included. Error-corrected sequencing was performed using single-molecule tagged molecular inversion probes targeting 27 myeloid and lymphoid driver genes. The threshold for variant calling was set at ≥1% VAF and ≥10 consensus variant reads. Details on sample selection, sequencing technique, variant calling procedures and data curation are described (van Zeventer et al. 2023).

  Study EGAS00001007087

• SNP Array Data for EGAS00001004666

  SNP Array Data for EGAS00001004666

  Dataset EGAD00010002257

• Heart

  Whole-Exome-Sequencing of 5 pooled patients for SNP-based demultiplexing

  Study EGAS50000000655

• Asian Genome Project(BioBank Japan genotype data)

  Biobank Japan registrants sequenced for Asian Genome Project

  Study JGAS000647

• FFPE_Normal_Panel_V3_Cancer_Panel

  FFPE normal panel generation for use with V3 cancer panel 0618521

  Study EGAS00001000836

• Oxford Nanopore sequencing for APL

  Raw fast5 file of Oxford Nanopore sequencing for an APL patient sample

  Dataset EGAD00001008151

• snv calls for subclonal reconstruction

  single nucleotide variant calls from somatic sniper, vcf format. input for subclonal reconstruction

  Dataset EGAD00001003753

• DATA FILES FOR SJLGG

  DATA FILES FOR SJLGG

  Dataset EGAD00001000695

• NHLBI TOPMed: Whole Genome Sequencing of Venous Thromboembolism (WGS of VTE)

  This study consists of 338 VTE cases from an inception cohort of Olmsted County, MN residents (OC) with a first lifetime objectively-diagnosed idiopathic VTE during the 40-year study period, 1966-2005. All living study subjects were invited to provide a whole blood sample at the Mayo Clinical Research Unit for leukocyte genomic DNA and plasma collection. For living study subjects who did not provide a blood sample, we retrieved any leftover blood ("waste" blood) from samples collected as part of routine clinical diagnostic testing and used this to extract DNA after obtaining patient consent. For deceased cases, with IRB approval, we extracted DNA from any available stored tissue within the Mayo Tissue Archive. This "tissue" DNA has been successfully genotyped in prior studies. Three trained and experienced study nurse abstractors reviewed the complete medical records in the community of all potential cases. Note: WGS sample IDs for the previous GENEVA study cases (phs000289) are included in this dataset. The phenotypes for the GENEVA study are located under the above phs number.

  Study phs001402

• TARGET Trial Study Cohort

  In a randomized controlled clinical trial, investigators will compare the effects on [18F]-fluorodeoxyglucose positron emission tomography-computed tomography (FDG PET/CT) from two treatment regimens in rheumatoid arthritis (RA) patients deemed methotrexate inadequate responders (MTX-IRs). Two common RA treatments will be compared: triple therapy (sulfasalazine, methotrexate, and hydroxychloroquine) versus tumor necrosis factor (TNF) inhibitor (etanercept or adalimumab, plus background methotrexate for all subjects and hydroxychloroquine for subjects who were taking this at screening). Consenting subjects will be screened for eligibility and randomized to a treatment arm. Subjects will be randomized to a treatment arm with either synthetic disease-modifying antirheumatic drugs (DMARDs) [triple therapy: sulfasalazine, methotrexate, and hydroxychloroquine] or biologic DMARDs [etanercept or adalimumab, plus background methotrexate for all subjects and hydroxychloroquine for subjects who were taking this at screening]. Once randomized, a baseline visit will be conducted with each subject. Baseline data collection includes questionnaires, disease activity score, and the first FDG-PET/CT imaging. After the baseline at week 0, subjects will visit with their rheumatologist at weeks 6, 12, 18, and 24 for safety labs and further collection of disease activity scores and questionnaires. The second FDG-PET/CT will be performed at week 24. Blood specimens will be collected at weeks 0, 6, 18, and 24 for bioassays. Subject participation will end after the week 24 visit. Patients and care providers will be unblinded. The FDG-PET/CT image readers will be blinded to treatment arm as well as timepoint of image acquisition.

  Study phs003720

• NIDDK IBD Genetics Consortium Ulcerative Colitis Genome-Wide Association Study

  This dataset contains data for 1,028 white, non-Hispanic, European ancestry individuals with ulcerative colitis who were included in a genome-wide association study published by Silverberg et al. (2009). These individuals were ascertained in North America and selected to have either left-sided or extensive disease (i.e., individuals with proctitis only were excluded). Genotyping was performed using the Illumina HumanHap300v2 (n = 540) and HumanHap550v3 (n = 488) Genotyping BeadChips at the Feinstein Institute for Medical Research. Control data (not included) were obtained from the NIDDK IBD Genetics Consortium's Crohn's Disease GWAS (available from dbGaP) and from studies 64 and 65 deposited in the Illumina iControlDB. Seven hundred eighty individuals in this dataset were taken from the NIDDK IBD Genetics Consortium cell line repository (http://www.niddkrepository.org). These individuals are identified in the file dbGaP_SubjectDS.txt. The subject IDs for these individuals may be used to request corresponding samples for follow-up research through the repository. In addition, complete phenotype data for these individuals are included, collected using the Consortium's forms and phenotyping manual (both included). The remaining 248 individuals were identified from pre-existing collections ascertained by members of the Consortium or their collaborators. For these samples, several of the items in the phenotype file are incomplete. Those who wish to replicate the results in Silverberg et al. should note that 6 individuals with missing genotype rates > 0.07 were excluded from that analysis (leaving 1,022 affected samples total). In addition, the minor allele frequencies (MAFs) reported in the publication were calculated using only those individuals who were included in the allelic association tests (n = 977 for SNPs included in the HumanHap300 and n = 476 for SNPs included only in the HumanHap550). These tests were performed using conditional logistic regression on gender-ancestry strata; individuals who were not placed in a stratum (using the procedure described in the supplementary information for Silverberg et al.) were excluded. The indicator variables hh300 and hh550 in the file dbGaP_PhenotypeDS.txt identify the samples included in the allelic association tests, and may be used to replicate the published MAFs among affected individuals.

  Study phs000345

• Genetics of Human Inherited Retinal Diseases (GHIRD)

  The goal of this project is to identify novel mutations and genes involved in human retinal disorders, a stated priority of the National Eye Institute. To accomplish this, additional genes whose mutations cause inherited retinal diseases (IRD) will be identified by combining targeted gene panel sequencing, whole exome sequencing, and whole genome sequencing. We have collected about 1050 patient families around the world. In this proposal, we will identify the underlying mutations in these patients using a combination of targeted gene panel sequencing, whole exome sequencing, whole genome sequencing, bioinformatics, statistics, and functional studies. Genes targeted for gene panel sequencing are: ABCA4; ASIC2; JAG1; ABCC6; ARL3; ATP1B2; BBS1; BBS2; BBS4; OPN1SW; CAPN5; C21orf2; CA4; CACNA1F; CDH3; CHM; ERCC8; CLCN2; CLCN3; CLCN7; TPP1; CLN3; CLN5; CNGB1; CNGA1; CNGA3; COL2A1; COL9A1; COL11A1; CRX; VCAN; CTSD; TIMM8A; DMD; CLN8; ERCC6; EFEMP1; OPN1MW; GNAT1; GNAT2; GNGT1; GRM6; GUCA1A; GUCA1B; GUCY2F; GUCY2D; HARS; CFH; HK1; IDH3B; IMPDH1; IMPG1; KCNJ13; KIF11; LRP5; MAK; MITF; TRPM1; MTTP; MVK; MYO7A; NDP; NEK2; NEUROD1; NPHP1; NRL; OAT; OPA1; OTX2; PAX2; PAX6; PDCL; PCYT1A; PDE6A; PDE6C; PDE6G; PDE6H; PDE6B; PEX1; PEX6; PEX7; PEX10; PEX12; PEX13; PEX14; PFDN5; PGK1; PHYH; PIN1; PLA2G5; PPT1; PRKCZ; PEX19; PEX2; PEX5; RB1; RBP3; RBP4; OPN1LW; RDH5; PRPH2; RGR; RHO; GRK1; RLBP1; ROM1; RP9; RP1; RP2; RPGR; RPE65; RS1; SAG; ATXN7; CCL2; SLC6A6; ELOVL4; TEAD1; NR2F1; TIMP3; NR2E1; TTPA; TUB; TULP1; UCHL3; USH2A; CLRN1; VLDLR; BEST1; WFS1; ALMS1; MKKS; FZD4; OFD1; PEX3; CTSF; GBF1; ADAM9; RGS9; PEX11B; PROM1; UNC119; PRPF4; PRPF3; AIFM1; SLC24A1; LRAT; ASIC3; RAB28; PEX16; ITM2B; SLC4A7; IQCB1; CROCC; IFT140; DHX38; MFN2; NR2E3; USH1C; TOPORS; MERTK; FBLN5; CIB2; PRPF8; SDCCAG8; IFT27; TREX1; RRAS2; CEP164; ATF6; TRIM32; RIMS1; SNRNP200; CLUAP1; EMC1; ZNF423; TTLL5; AGTPBP1; RPGRIP1L; CRB1; TSPAN12; ARL2BP; AIPL1; PRPF6; FSCN2; ABHD12; PRPF31; TCTN3; IFT172; CNNM4; NPHP3; INVS; BBS9; FLVCR1; RDH8; IMPG2; WDPCP; RDH11; TMEM216; TMEM138; LZTFL1; CNGB3; AHI1; MKS1; CLN6; BBS7; POMGNT1; PEX26; SPATA7; KIZ; KLHL7; INPP5E; MDM1; CABP4; RPGRIP1; MCOLN1; CC2D2A; KIAA1549; SLC7A14; WDR19; NYX; CDH23; SEMA4A; MPP5; NMNAT1; TMEM237; PCDH15; C5orf42; GNPTAB; TMEM231; TCTN1; SRD5A3; BBS10; ZNF408; TTC21B; CSPP1; LPCAT1; DHDDS; PDZD7; PANK2; CEP290; OPA3; DNAJC5; PITPNM3; MFRP; HMCN1; ADGRV1; ARL6; FAM161A; TMEM126A; GNPTG; GJA10; RAX2; TUBGCP6; TMEM67; ACBD5; C12orf65; CDHR1; BBIP1; REEP6; CACNA2D4; RP1L1; CEP41; C1QTNF5; NXNL1; TTC8; USH1G; BBS5; ZNF513; RDH12; SLC38A8; C8orf37; VPS13B; NXNL2; BBS12; ADGRA3; LCA5; KCNV2; ADAMTS18; ARL13B; ATOH7; MFSD8; NPHP4; CCDC66; CYP4V2; CRB2; RD3; LRIT3; EYS; KIF7; CERKL; RGS9BP; C2orf71; GDF6; DTHD1; GPR179; CISD2; CCR2; PRCD; WHRN; ADGRA3. The current dbGaP study release makes available all sequencing-derived vcf data of study participants.

  Study phs001517

• Data Access Committee for the DNA sequencing data included in the study "A generalizable machine learning framework for classifying DNA repair defects using ctDNA exomes”

  Dac EGAC00001003111

• "Usage of small amounts of DNA for Illumina sequencing"

  "Usage of small amounts of DNA for Illumina sequencing"

  Dataset EGAD00001000034

• Dataset for TIX

  All files for TIX individuals

  Dataset EGAD50000000426

• RNAseq for 4 pdx and 1 cell-line

  RNAseq for #1049, #111, #1217, #206, COV362

  Dataset EGAD50000000032

• PBMC

  Whole-Exome-Sequencing of 5 pooled patients for SNP-based demultiplexing

  Study EGAS50000000654

• DATA FILES FOR SJINF RNASeq

  DATA FILES FOR SJINF RNASeq

  Dataset EGAD00001001098

• Deep sequencing of melanoma for driver mutations

  Deep sequencing of melanoma for driver mutations

  Dataset EGAD00001001445

• Australian genomes

  Mapped data (bam files) for high-throughput whole genome sequence data for 83 modern Aboriginal Australians

  Dataset EGAD00001002001

• Resident memory CD8 T cells persist for years in human small intestine

  Resident memory CD8 T cells (Trm) have been shown to provide effective protective responses in the small intestine (SI) in mice. A better understanding of the generation and persistence of SI CD8 Trm cells in humans may have implications for intestinal immune-mediated diseases and vaccine development. Analyzing normal and transplanted human SI we demonstrated that the majority of SI CD8 T cells were bona fide CD8 Trm cells that survived for over 1 year in the graft. Intraepithelial and lamina propria CD8 Trm cells showed a high clonal overlap and a repertoire dominated by expanded clones, conserved both spatially in the intestine and over time. Functionally, lamina propria CD8 Trm cells were potent cytokine-producers, exhibiting a polyfunctional (IFN-γ+ IL-2+ TNF-α+) profile, and efficiently expressed cytotoxic mediators after stimulation. These results suggest that SI CD8 Trm cells could be relevant targets for future oral vaccines and therapeutic strategies for gut disorders.

  Study EGAS00001003676

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

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

  Study phs001788

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

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

  Study EGAS00001005464

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

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

  Study EGAS00001008051

• OLD DATA FILES FOR SJMB - Superseded by EGAD00001001864

  OLD DATA FILES FOR SJMB - Superseded by EGAD00001001864

  Dataset EGAD00001000269

• RNAseq for 8 PDX

  RNAseq for #111, #1177, #206, #201, #29, #931, WO-19, WO-2

  Dataset EGAD50000000116

• Preeclampsia InterPregGen Consortium: GWAS meta-analysis summary statistics for European fetal preeclampsia cases versus controls and GWAS genotype data for European fetal preeclampsia cases

  Study EGAS00001001048

• sc-DECISION

  Data derived from PBMC and blood samples for the single cell and bulk RNAseq analysis for the sc-DECISION paper. https://doi.org/10.1101/2025.02.04.25321370

  Dac EGAC50000000642

• Pulldown_DNA_methylation_study_v2

  Pilot study to set up sequencing protocols for targeted pulldown methylation profiling

  Study EGAS00001000979

• PBMC_dual_10X_kit

  Dual snRNAseq and ATACseq of replicate PBMC samples for 10X beta testing.

  Study EGAS00001004834

• WGS data for Glioblastoma (EGAS00001003953, H016)

  Whole genome sequencing data for 20 human glioblastoma patients.

  Dataset EGAD00001006545

• McGill EMC Release 4 for assay "H3K27ac"

  McGill EMC Release 4 for assay "H3K27ac"

  Dataset EGAD00001001298

• McGill EMC Release 4 for assay "H3K9me3"

  McGill EMC Release 4 for assay "H3K9me3"

  Dataset EGAD00001001299

• McGill EMC Release 4 for assay "H3K4me1"

  McGill EMC Release 4 for assay "H3K4me1"

  Dataset EGAD00001001296

• McGill EMC Release 4 for assay "H3K36me3"

  McGill EMC Release 4 for assay "H3K36me3"

  Dataset EGAD00001001295

• BLUEPRINT: DNaseI-seq for monocytes

  DNaseI-seq for monocytes

  Dataset EGAD00001000674

• Dataset for "Genomic landscape of oral cancers" (CGI WGS)

  Dataset for "Genomic landscape of oral cancers" (CGI WGS)

  Dataset EGAD00001004339

• DATA FILES FOR Histone-NSD2_RNASeq

  DATA FILES FOR Histone-NSD2_RNASeq

  Dataset EGAD00001000655

• McGill EMC Release 4 for assay "H3K4me3"

  McGill EMC Release 4 for assay "H3K4me3"

  Dataset EGAD00001001297

• DATA FILES FOR GRUBER SJAMLM7 RNASEQ

  DATA FILES FOR GRUBER SJAMLM7 RNASEQ

  Dataset EGAD00001003135

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

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

  Dataset EGAD00001004356

• McGill EMC Release 4 for assay "H3K27me3"

  McGill EMC Release 4 for assay "H3K27me3"

  Dataset EGAD00001001294

• Low T cell diversity is associated with poor outcome in bladder cancer - Bulk TCRseq data

  T cells are crucial effector cells in the endogenous defense against cancer, yet the clinical impact of their quantity, diversity, and dynamics remains underexplored. In this study we performed bulk TCRseq on tumor and serial blood samples from patients with muscle invasive (n patients = 119) or non-muscle invasive bladder cancer (n patients = 30). The goal was to investigate the clinical relevance of the blood and tumor TCR repertoire in patients with bladder cancer.

  Study EGAS50000000940

• Catalogue Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue What is in the EGA? The European Genome-phenome Archive (EGA) overview The EGA archives and distributes the results of several types of studies. Such studies include genome-wide association studies (GWAS), different purpose sequencing and molecular diagnosis assays among others. Moreover, these studies target many different kind of diseases from cancer to neurodegenerative alterations. We summarised all this data in the charts below. The statistics are computed daily based on an updated list of studies. Each pie chart depicts a different aspect of the studies: which disease type was studied, which type of sampling method was used and what was the scope of the genomic analysis. Number of studies per disease type Studies in the EGA by disease barHorizontalChart('disease-graph', 'https://stats.ega-archive.org/catalog/by_disease') Number of studies per technology type Studies in the EGA by technology barHorizontalChart('technology-graph', '/stats/studies/technology', 'Include dbGaP') Number of studies per sample type Studies in the EGA by samples barChart('samples-graph', '/stats/studies/sample-types', 'Include dbGaP')

  Documentation about/statistics/catalogue

• Transcriptome Analysis of Malignant Peripheral Nerve Sheath Tumor-like Melanoma

  Malignant peripheral nerve sheath tumor (MPNST)-like melanoma is a rare malignancy with overlapping characteristics of both neural sarcoma and melanoma. The genomics of MPNST-like melanoma have not been previously described. In this study, we performed whole transcriptome sequencing analysis in 8 samples from 6 patients diagnosed with MPNST-like melanoma. In correlation with deletion ofxa0SERPINB4xa0in all our samples, there was noxa0SERPINB4xa0mRNA expression in our cohort, suggesting a potential tumor-suppressor role of SERPINB4 in MPNST-like melanomas.xa0HRAS, a gene uncommonly mutated in cutaneous melanomas, was mutated in 2 patients, but with no increased mRNA expression.xa0BRAFxa0mRNA expression, resultant from an atypicalxa0BRAFxa0mutation, was increased in association with an inactivatingxa0NF1xa0mutation. Our data demonstrate the role of alternative mechanisms of RAS pathway activation in MPNST-like melanomas and suggest the potential role of other molecular pathways in its carcinogenesis.

  Dataset EGAD00001007000

• Somatic L1 retrotransposition in normal colorectal epthelium

  Throughout an individual’s lifetime, genomic alterations accumulate in somatic cells. However, the mutational landscape induced by retrotransposition of long interspersed nuclear element-1 (L1), a widespread mobile element in the human genome, is poorly understood in normal cells. Here, we explored the whole-genome sequences of 406 normal colorectal clones, 12 MUTYH-associated adenomatous clones, and 19 matched colorectal cancer tissues. In addition, we analyzed promoter DNA methylation status of retrotransposition-competent L1 (in 139 clones) and read-through RNA expression profiles (in 116 clones) to investigate the epigenetic regulation of L1 activity.

  Dataset EGAD00001010183

• CcRCC_metabolic_heterogeneity

  Heterogeneity is a hallmark of clear cell renal cell carcinoma (ccRCC). In this study, hyperpolarized [1-13C]pyruvate MRI (HP-13C-MRI) was used to probe metabolic heterogeneity non-invasively in 6 ccRCC patients from which 58 tumor and healthy tissues biopsies were acquired postoperatively. MRI parameters were correlated with 146 metabolite concentrations and the expression of 2523 metabolic genes across 34 metabolic pathways. Using metabologram projections and pathway consensus scoring, several metabolic pathways were correlated with the metabolic MRI, including glycolysis, pentose phosphate pathway, and the TCA cycle. A simple thresholding approach applied to the imaging metrics was sufficient to provide meaningful metabolic pathway information. We also show that tissue metabolic heterogeneity increases in regions with higher pyruvate-to-lactate conversion on imaging. These results provide validation of the biological relevance of HP-13C-MRI and support the possibility of its clinical use to improve disease characterization, stratification, targeting of biopsies, and providing novel methods to monitor treatment response.

  Dataset EGAD00001015780

• Genomic and transcriptomic landscape of T-cell lymphoblastic lymphoma compared to T-cell acute lymphoblastic leukemia: similar subtypes and different fusions

  Due to the lower incidence of T-LBL and difficulties in obtaining diagnostic T-LBL material, extensive research on T-LBL has been hampered whereas genetic aberrations in T-ALL are thoroughly characterized. Given the similarities and differences between T-LBL and T-ALL, the question has been raised whether T-LBL and T-ALL represent two different diseases or different manifestations of the same disease. This study aims to identify the genomic and transcriptomic landscape of T-LBL and compare the findings to what is found T-ALL. Comparison of the molecular aberrations between T-LBL and T-ALL can provide insights into the overlap and differences in malignant development between the two entities, which could lead to improved risk stratification in T-LBL in order to eventually adapt T-LBL treatment protocols based on molecular-genetic prognostic factors.

  Dataset EGAD00001015357

• 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