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• Submitters and requesters Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue Here, we show the amount and global distribution of submitters and requesters Requester accounts created by country The below figure represents the number of requester accounts by country. In most of the cases cases, country was inferred from the requester email domain. Those requesters whose email domain could not be assigned to a country such as @gmail.com, are not included. Note that a requester is a user that has had a granted permission at some point in time. Requester Accounts Created by Country Download as PNG Download as JPG Download as WEBP Download as CSV map pie mapChart('requester-country', 'https://stats.ega-archive.org/requester/country') pieChart('requester-country-pie-chart', 'https://stats.ega-archive.org/requester/country','Requester accounts created by country') Requester accounts created by year The below figure represents the number of requester accounts by year. Note that a requester is a user that has had a granted permission at some point in time. Requester Accounts Created by Year Download as PNG Download as JPG Download as WEBP Download as CSV barChart('requester-year', 'https://stats.ega-archive.org/requester/year', ['Requester Accounts', 'Years'], true) Industry requester accounts The below figure represents the top 15 industry requesting companies. Industry Requester Accounts Download as PNG Download as JPG Download as WEBP Download as CSV pieChart('industry-requester-accounts', 'https://stats.ega-archive.org/industry/requester/accounts', 'Industry Requester Accounts') Submitter accounts created by country The below figure represents the number of submitter accounts by country. Location was infered from the requester email domain. Submitter Accounts Created by Country Download as PNG Download as JPG Download as WEBP Download as CSV map pie mapChart('submitter-accounts', 'https://stats.ega-archive.org/submitter/accounts/country') pieChart('submitter-accounts-pie-chart', 'https://stats.ega-archive.org/submitter/accounts/country','Submitter accounts created by country') Submitter accounts created by year The below figure represents the number of submitter accounts by year. Submitter Accounts Created by Year Download as PNG Download as JPG Download as WEBP Download as CSV barChart('submitter-users-year', 'https://stats.ega-archive.org/submitter/users/year', ['Submitter Accounts', 'Years'], true) DAC Members by Country The below figure represents the number of DAC members by country. DAC Members by Country Download as PNG Download as JPG Download as WEBP Download as CSV map pie mapChart('dac-members-by-country', 'https://stats.ega-archive.org/dac_member/country') pieChart('dac-members-by-country-pie-chart', 'https://stats.ega-archive.org/dac_member/country','DAC members by country')

  Documentation about/statistics/community

• DAC for "Resolving spatial subclonal genomic heterogeneity of loss of heterozygosity and extrachromosomal DNA in gliomas"

  This DAC will be created for sequencing data for the manuscript "Resolving spatial subclonal genomic heterogeneity of loss of heterozygosity and extrachromosomal DNA in gliomas." The dataset includes Illumina-based exome sequencing of tumor specimens, paired blood specimens, bulk RNA-seq, and Visium FFPE.

  Dac EGAC50000000579

• BLUEPRINT September 2016, ATAC-seq for osteoclast from venous blood, on Genome GRCh38

  ATAC-seq data for 2 sample(s) for osteoclast from venous blood, on Genome GRCh38. 2 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002907

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

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

  Dataset EGAD00001002903

• BLUEPRINT September 2016, ATAC-seq Multiple Myeloma for plasma cell from bone marrow, on Genome GRCh38

  ATAC-seq data for 4 sample(s) Multiple Myeloma for plasma cell from bone marrow, on Genome GRCh38. 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002920

• BLUEPRINT September 2016, ATAC-seq for plasma cell from tonsil, on Genome GRCh38

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

  Dataset EGAD00001002912

• The data access committee for Single molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer

  Dac EGAC00001003253

• Screening for human epigenetic variation at CpG islands

  Screening for human epigenetic variation at CpG islands

  Dataset EGAD00001000059

• DATA FILES FOR SJACT (WGS)

  DATA FILES FOR SJACT

  Dataset EGAD00001000160

• DATA FILES FOR SJINF

  DATA FILES FOR SJINF

  Dataset EGAD00001000165

• Fastq files for SAIF genome

  This dataset contain the raw files generated for SAIF genome project

  Dataset EGAD00001000254

• Engineering Immune Cells for Therapy, Institute for Medical Microbiology, Immunology and Hygiene, Technical University Munich

  CRISPR engineering of human T cells

  Dac EGAC50000000405

• DATA FILES FOR SJEWS-WGS

  DATA FILES FOR SJEWS-WGS

  Dataset EGAD00001001020

• DATA FILES FOR Histone Capture bams

  DATA FILES FOR Histone Capture bams

  Dataset EGAD00001000657

• SNP data for Ovarian cancer PRS

  SNP data for Ovarian cancer PRS (cases)

  Dataset EGAD00001008143

• Role of Genetic Factors in the Pathogenesis of Lung Disease

  Three individuals with lymphangioleiomyomatosis (LAM) underwent evaluation for Tuberous Sclerosis Complex (TSC)-associated mucocutaneous and internal findings. Next-generation sequencing (NGS) of cutaneous findings was used to confirm clinical suspicion for mosaic TSC in individuals with LAM. Mosaic mutations in TSC2 were found in these three individuals.

  Study phs003108

• Genotype variables of the 61 COVID-19 patient cohort used in the main project of data integration

  We selected SNPs with a minor allele frequency (MAF) higher than 10% in the Caucasian population from the Ensembl database. Specifically, we selected ACE2 (rs2285666), MX1 (rs469390), and TMPRSS2 (rs2070788) for the present analysis. For more information, please refer to:

  Study EGAS50000000589

• RNAseq of 12q-amplified osteosarcomas

  RNA-seq of 12q-amplified osteosarcomas for detection of gene fusions and gene expression analysis. RNA-seq was used to look for novel gene fusions and promoter swapping events, and study the expression levels of identified 3' partners. Additionally, global gene expression patterns were used to separate cases into clusters.

  Study EGAS50000000494

• Japanese Alzheimer's disease neuroimaging initiative

  Japanese ADNI aims to establish imaging and biofluid markers that can predict and monitor the progression of changes in the brains of elderly individuals with AD, mild cognitive impairment (MCI) or normal cognition, and eventually be used as a surrogate markers for the clinical trials of DMDs for AD.

  Study JGAS000051

• Hybrid untargeted and targeted RNA sequencing facilitates genotype-phenotype associations at single-cell resolution

  Raw sequencing data for the manuscript "Hybrid untargeted and targeted RNA sequencing facilitates genotype-phenotype associations at single-cell resolution". It includes PacBio long-read scRNA-seq, illumina short-read scRNA-seq and Twist-based targeted scRNA-seq datasets for two adrenal samples.

  Study EGAS50000001537

• The role of MALT1 in driving IBN resistance in MCL

  The bulk RNA-seq dataset was generated for the cell lines below and used for two major purposes: 1. DEG analysis and GSEA analysis comparing IBN-R and IBN-S cells 2. DEG analysis and GSEA analysis comparing MCL cells with/without MI-2 treatment.

  Study EGAS00001006832

• Microinjection_of_hIPSC_derived_intestinal_organoids_with_Salmonella_Typhimurium

  To generate an RNA-Seq dataset for organoids apically stimulated with Salmonella Typhimurium.These data are part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001001253

• WGS-Lung Cancer sample 30 pair

  This study includes 30 paired samples of Korean Lung Cancer paitients. 30 Lung Cancer samples are paired with each normal(Saliva derived for control) sequencing data.The results of this WGS study were analyzed by Mutect and GATK for ranking germline variants and cancer variants to make mutational profile matrix.

  Study EGAS00001001474

• Korean Lung Cancer - 36 pair WES data

  This study includes 36 paired samples of Korean Lung Cancer paitients.36 Lung Cnacer samples are paired with each normal(Saliva derived for control) sequencing data.The results of this WES study were analyzed by Mutect and GATK for ranking germline variants and cancer variants to make mutational profile matrix.

  Study EGAS00001002843

• RNA-Seq on OCIAML-22 Fractions

  The donor AML patient (unsorted) that was used to generate OCI-AML22 was expanded for the indicated number of days (Donor: 0 day, 12d: 12 days, 2m: 2 months, 3m: 3 months), then sorted for the indicated fraction (CD34p: CD34+; CD34m: CD34-, CD38m: CD38-, CD38m: CD38-).

  Study EGAS00001006512

• February 2020 data update (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.

  February 2020 data update (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 EGAD00001006220

• FFPE, buffycoat and cell free DNA from N. German esophagus cancer patients - 2021

  28 patients. Cell-free DNA and leukocyte DNA, both from before any neoadjuvant treatment. Tumor FFPE tissue (plus metastasic tissue for some patients) from after any neoadjuvant treatment. Some cfDNA from follow-up time points (e.g. after neodj. treatment or in the clinical course). IDT xgen Pan-Cancer panel for hybridisation capture. Illumina TruSeq library prep for cfDNA and leukocyte DNA, Illumina DNA Prep for FFPE DNA. n=150 libraries in total.

  Dataset EGAD00001010081

• ChIP-Seq (H3K36me3) 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 (H3K36me3) 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 EGAD00001001231

• ChIP-Seq (H3K4me3) 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 (H3K4me3) 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 EGAD00001001233

• ChIP-Seq (H3K4me1) 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 (H3K4me1) 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 EGAD00001001232

• DNA methylation (RRBS) data for the validation the glioblastoma progression study (GBMatch).

  Genome-wide profiling of DNA methylation levels by RRBS in 150 glioblastoma tumor samples. Patients were selected to represent the general population of glioblastoma patients based on Austrian Brain Tumor Registry. These DNA methylation profiles were created for the validation of the glioblastoma progression study (GBMatch) and consist of 106 profiles from FFPE samples and 44 profiles from fresh-frozen samples. For the 44 fresh-frozen samples also WGS data (43 genomes) and RNA-seq data (37 transcriptomes) have been produced for validation purposes.

  Dataset EGAD00001004074

• 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

• Identifying causative mutations for Thrombocytopenia with Absent Radii

  Identifying causative mutations for Thrombocytopenia with Absent Radii

  Dataset EGAD00001000018

• DAC for hepatoblastoma sequencing data

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

  Dac EGAC50000000548

• TTN gene targeted sequencing for AMC cohort

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

  Dataset EGAD00001005497

• DATA FILES FOR SJEPD

  DATA FILES FOR SJEPD

  Dataset EGAD00001000854

• DATA FILES FOR GRUBER SJAMLM7 EXOME

  DATA FILES FOR GRUBER SJAMLM7 EXOME

  Dataset EGAD00001003134

• RNA-seq data for NRF2 study

  RNA-seq data for clinical samples

  Dataset EGAD00001002243

• 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

• 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

• 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