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• All counts

  This dataset is the expression profiling by high throughput sequencing, including all counts of genes for all samples in two groups. It includes 26 samples totally (Lean healthy group includes 12 samples: s001/SR01_Bright, s002/SR01_Dim, s004/SR02_Bright, s005/SR04_Dim, s006/SR04_Bright, s007/SR05_Dim, s008/SR05_Bright, s009/SR06_Bright, s011/SR07_Dim, s012/SR07_Bright, s013/SR08_Bright, s014/SR08_Dim; DM2 group includes 14 samples: s015/SR201_Bright, s017/SR202_Bright, s018/SR202_Dim, s019/SR203_Bright, s020/SR203_Dim, s021/SR204_Bright, s022/SR204_Dim, s023/SR205_Dim, s025/SR206_Dim, s026/SR206_Bright, s027/SR207_Dim, s028/SR207_Bright, s029/SR208_Bright, s030/SR208_Dim). It is a TAB file.

  Dataset EGAD50000001715

• WGS of Multiple Myeloma/MGUS/SMM cases (germline) - VCF files (SNPs)

  WGS - PCR-free library preparation (whole blood gDNA), paired-end (2x100bp) sequencing on DNBseq sequencing platform (BGI) Number of samples: 26

  Dataset EGAD50000001800

• scRNA-seq analysis of HGSC tumors, including immune TME, before and after NACT

  Gene expression profiles of single cells from 26 tumor and ascites samples samples from 17 patients

  Dataset EGAD50000000862

• MSI-Exome-DAC Team Microsatellites Instability and Cancer, INSERM Hospital Saint-Antoine, Paris 12, France Carte d'identité des tumeurs, French league against cancer, Paris France

  Dac EGAC00001000665

• endogene.bio

  The Endogene Bio Data Access Committee oversees and manages access to controlled genomic and clinical datasets deposited by the Endogene Bio research team.

  Dac EGAC50000000786

• H3Africa H3AChipDesign TrypanoGEN1

  26 samples from Cameroon generated for the H3Africa Chip Design Study. The dataset includes BAM, FASTQ and decompressed gVCF files.

  Dataset EGAD00001004393

• Center for Common Disease Genomics [CCDG] - Autoimmune: Inflammatory Bowel Disease (IBD) Exomes and Genomes

  The National Human Genome Research Institute (NHGRI) has funded a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Called the Centers for Common Disease Genomics (CCDG), this initiative will explore a range of diseases with the ultimate goal of: undertaking variant discovery for enough different examples of disease architectures and study designs to better understand the general principles of the genomic architecture underlying common, complex inherited diseases; understanding how best to design rare variant studies for common disease; developing resources, informatics tools, and innovative approaches and technologies for multiple disease research communities and the wider biomedical research community. The initial focus of the CCDGs will be in cardiovascular disease (early-onset coronary artery disease, atrial fibrillation, hemorrhagic stroke), neuropsychiatric disease (epilepsy, autism), and autoimmune/inflammatory disease (type 1 diabetes, inflammatory bowel disease). The Broad Institute is one of four selected CCDG project centers. The overarching aim of the Inflammatory Bowel Disease (IBD) program is to define the full allelic spectrum of protein-altering variation in genes associated to IBD, and assess their role in both Crohn's Disease (CD) and Ulcerative Colitis (UC) risk. The whole genome sequencing data generated here is comprised of samples from US-based diverse populations including African American, Puerto-Rican, Caribean and Cuban origins.

  Study phs001642

• ATAC-seq

  ATAC-seq data for 26 CLL samples (7 controls, 19 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005967

• Neurodevelopment_Nantes_hospital

  The neurodevelopmental team in the genetics unit of Nantes hospital focus on the identification on new genes causing neurodevelopmental disorders through whole-genome and transcriptomics sequencing.

  Dac EGAC50000000542

• How upcycled prostate cancer sequences enabled key findings on telomeres length

  The length of telomeres, the chromosomes’ end tails, as depicted in the figure below, physiologically shorten with aging. This means that younger cells have longer telomeres compared to their aged counterparts; one could say that the length of cells’ telomeres is a measure of their replicative power. Cancer cells are known to be able to turn this into their favour, and expand their replicative immortality through telomere lengthening and maintenance. Despite being a well-studied pan-cancer hallmark, the relation of the telomeres length to specific cancer genomic features is poorly understood. Julia Livingstone and her colleagues from P.C. Boutros' team in Los Angeles focused their work on prostate cancer. To investigate the relations between telomere length and the clinico-genomics of prostate tumours, they used whole genome sequencing (WGS) of 392 published tumour–reference pairs previously deposited at the EGA under accession numbers EGAS00001000400 and EGAS00001000900. Their work was published on Nature Communications in November 2021. They determined the telomere length of the sequencing sample, from both tumour and non-tumour adjacent tissues, and subsequently ran statistical analysis to investigate possible correlation between the telomeres length with several cancer features. The team discovered that telomeres length does not correlate with any specific driver gene mutation, but it does with the overall frequency of single nucleotides variants in a sample. This finding suggests that tumours with shorter telomeres accumulate more mutations without strong selective pressures for specific ones. While proliferation rate did not correlate directly with telomere length, methylation of almost half of all genes strongly did. The team’s results are massive and reveal a complicated and fascinating relation of different clinical and genomic features of prostate cancer with telomere length. In the paper discussion, they smartly consider different possibilities. As noted above, all these amazing results have been obtained analysing previously sequenced samples. Remarkably, the researchers who generated the samples did not focus at all on telomeres length. They indeed studied specific genes mutations and the features of early and late-onset prostate cancer. This proves, once again, the power of sequencing data re-use. This data is tremendously rich in information, and can hardly be exhausted with any number of analyses. Generating sequencing data, especially Whole Genome Sequencing, even better if tumor-reference paired, is like shopping every last article at the grocery store. You can then cook several recipes, or prepare a seven-course dinner, and still have ingredients left for someone else to borrow and cook something you have not thought about, a recipe that is just not in your cookbook. Noteworthy, the data retrieved from the EGA for this paper is associated with rich and complete metadata, meaning pheno-clinical information regarding the sample donors. As Julia Livingstone and her team wrote, using rich clinical annotations enabled them to assess the relationship between telomere length and patients outcome. This highlights once again the importance of reporting and annotating metadata correctly; It might feel like extra work for the researchers depositing their data, but it really has a huge value. Our team at the EGA is currently working to improve the metadata annotation process, with the aim to maximize the benefits for the scientific community.

  Blog prostate-cancer-sequences-enabled-key-findings-on-telomeres-length

• RNA-seq of tumor samples from clear cell renal cell carcinoma patients included in the Translational Program of the NIVOREN GETUG-AFU-26 trial.

  DAC for RNA-seq of tumor samples from clear cell renal cell carcinoma patients included in the Translational Program of the NIVOREN GETUG-AFU-26 trial.

  Dac EGAC50000000627

• Refractory Classic Hodgkins Lymphoma (cHL) sWGS

  Associated with the study: Blood-based Monitoring of Relapsed/Refractory Hodgkin Lymphoma Patients Predict Responses to Anti-PD-1 Treatment. 26 ctDNA Samples from 4 patients followed longitudinally. sWGS performed for copy number aberration (CNA) analysis.

  Dataset EGAD50000001163

• Common Fund (CF) Genotype-Tissue Expression Project (GTEx)

  Lay Description The aim of the Genotype-Tissue Expression (GTEx) Project is to increase our understanding of how changes in our genes affect human health and disease with the ultimate goal of improving health care for future generations. GTEx will create a database that researchers can use to study how inherited changes in genes lead to common diseases. GTEx researchers are studying genes in different tissues obtained from many different people. The GTEx project also includes a study of the GTEx donor consent process - this study will help ensure that the consent process and other aspects of the project effectively address the concerns and expectations of participants in the study. GTEx is a pioneering project that uses state-of-the-art protocols for obtaining and storing a large range of organs and tissues, and for testing them in the lab. Until now, no project has analyzed genetic variation and expression in as many tissues from the same person in such a large population as planned for GTEx. Scientific Description Understanding the role of variation in the human genome is crucial to elucidating genetic contributions to human health and disease. Despite the results of genome-wide association studies (GWAS) documenting strong statistical associations between genetic variation and human traits, the functional role for most of these variants is largely unexplained. Nearly 90% of these GWAS-implicated sites lie outside of protein-coding sequences, suggesting that these variants might regulate gene expression. The goal of the Genotype-Tissue Expression (GTEx) project is to establish a resource database and tissue biobank in which to study the relationship between genetic variation and gene expression and other molecular phenotypes in reference/non-diseased tissues. The ultimate resource will include approximately 960 post-mortem donors with several dozen tissues from each, a resource large enough to study both cis- and trans- gene expression quantitative trait loci (eQTLs). Some tissue will also be banked for additional molecular analyses. To request biosamples, please see the Biobank page for more information. GTEx was initially funded as a 2-year pilot project by the NIH Common Fund (CF) in 2010, and has been scaled up after demonstration of feasibility. The project will collect and analyze RNA levels in many different human tissues and each donor will be characterized for germline genetic variation through dense genotyping arrays and sequencing of either whole exomes or whole genomes. By treating RNA expression levels as quantitative traits, eQTLs will be identified as sites containing genetic variation that correlate with changes in RNA expression. Such eQTLs have been associated with 4%-12% of expressed human genes, and with common complex human diseases, including obesity, atherosclerosis, type 2 diabetes, Crohn's disease, and asthma. Additionally, few studies have examined the tissue specificity of eQTLs. A subset of banked tissue samples will also be analyzed for other molecular phenotypes, such as DNA methylation, DNaseI hypersensitivity sites, and proteomics. The GTEx project will thus serve as a resource database and tissue bank for many future studies, especially for understanding the functional basis of inherited susceptibility to disease. All GTEx releases since Version 5 follow the NIH Genomic Data Sharing (GDS) Policy whereby there are no restrictions on use or publication after release. Additional information about the GTEx Data Release and Publication policy can be found on the GTEx Data Portal at www.gtexportal.org.

  Study phs000424

• High-depth whole genome sequencing of paired ductal carcinoma in-situ (DCIS) and germline control samples from 26 individuals.

  Deposited here are whole-genome sequencing data for 26 paired breast cancer DCIS and matched-normal samples taken from the same individual. Average sequence coverage is 118x for DCIS samples and 41x for matched-normals. Matched-normal samples are from blood. Sequencing was performed on an Illumina HiseqX. Due to specific restrictions imposed by the ethical approval at sample collection, the use of the germline data is restricted to filtering of somatic mutation calls only and cannot be used outside this purpose.They are a part of a broader project that uses high-depth WGS to investigate the somatic mutation genomic landscape of DCIS in order to uncover biological insights into breast cancer progression as well as possible methods to stratify DCIS patients for individualised therapy or disease monitoring.

  Dataset EGAD50000002237

• RNA-Sequencing and Somatic Mutation Status of Adrenocortical Tumors: Novel Pathogenetic Insights

  To investigate the relationship between transcriptome profile and genetic background in a large series of adrenocortical tumors and to identify new potential pathogenetic mechanisms, we performed transcriptome profiling by high-throughput sequencing. We collected snap-frozen tissue from patients with adrenocortical tumors (n = 59) with known genetic background: 26 adenomas with Cushing syndrome, 17 adenomas with mild autonomous cortisol secretion, 9 endocrine-inactive adenomas, 7 adrenocortical carcinomas. Additionally, we sequenced 4 control samples.

  Study EGAS00001004533

• Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer

  Gene expression profiles of single cells from 26 chemo-naïve and IDS tumor tissue and ascites samples from 17 HGSC patients

  Study EGAS50000000607

• dataset1

  Contains 14 control samples and 26 case samples.

  Dataset EGAD00001008576

• BLUEPRINT release August 2015, ChIP-Seq for CD8-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 5 CD8-positive, alpha-beta T cell sample(s). 26 run(s), 26 experiment(s), 26 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001513

• WES of melanoma tumours prior to combined immune checkpoint blockade treatment.

  Paired melanoma tumor and normal (PBMC) WES data from a cohort of 26 patients subsequently treated with combined immune checkpoint blockade.

  Dataset EGAD00001005941

• Sequencing files for "Transcriptional Mechanisms of Resistance to Anti-PD-1 Therapy"

  Files from whole exome sequencing of 26 tumors and two matched normals from one melanoma patient. The 26 tumors include the untreated primary, cutaneous metastases and distant metastases to internal organs.

  Dataset EGAD00001003200

• L1 Retrotransposon sequencing in Cocaine Use Disorder - Study 1

  Cocaine Use Disorder (CUD) is a global public health problem, with no effective pharmacotherapies. While CUD is marked by high heritability, discovery of variants associated with risk for CUD has proven difficult. We used Restriction Enzyme Based Enriched L1 sequencing (REBELseq) to amplify L1 retrotransposons in gDNA isolated from medial prefrontal cortex NeuN+ neuronal nuclei from 25 CUD cases and 26 controls. Amplified libraries were sequenced on the Illumina HiSeq 4000 platform, and sequencing data was analyzed with the REBELseq bioinformatics pipeline.

  Study phs001966

• MEC/SEF RNA-seq

  RNA transcript expression sequencing of 27 MEC/SEF tumor samples. Samples were sequenced at Beijing Genomics Institute. Prior to sequencing, rRNA removal library preparation was done for any samples that had degraded. Samples were sequenced on a DNBseq platform sequencer with a read length of 100PE, producing approximately ~40M raw reads (~8GB) raw data per sample.

  Dataset EGAD50000000761

• Extensive and differential platinum chemotherapy mutagenesis in livers of children - Targeted NanoSeq

  Targeted NanoSeq using a bait set consisting of 285 genes, comprising mostly cancer genes, of a Validation cohort that includes platinum-exposed (n=195) and platinum naïve (n=60) samples from 26 individuals (children and foetal tissues). The samples are mainly liver samples (normal and cancer tissues) and obtained from children with liver cancer.

  Dataset EGAD00001016120

• Genetics of Neuropsychiatric and Neurodevelopmental Disorders

  Although schizophrenia is a highly heritable disease, relatively little progress had been made in securely identifying the genetic causes of this disorder, and most instances of schizophrenia in the general population remain unexplained. One avenue of explanation for the genetic basis of schizophrenia, however, has been effectively closed by recent research. Genome-wide association studies (GWAS) have now shown that common variation makes at most a modest contribution to the risk of schizophrenia. At the same time that the role of common variation has been circumscribed by GWAS, however, the analysis of copy number variants that are detectable on a genome-wide scale has revealed and replicated a number of very rare variants that associate with schizophrenia. These rare copy number variants that have been implicated in schizophrenia, however, have one striking feature in common: they are all risk factors for other brain related disorders beyond schizophrenia such as mental retardation, autism and epilepsy. These findings argue that genetic risk factors may confer a highly penetrant vulnerability to neuropsychiatric disorder, which is then further modified by interacting genetic or environmental factors to determine the ultimate manifestation. Most schizophrenia collections that are being studied today, however, have been selected precisely for their homogeneity: including only schizophrenia patients with no comorbidities, or schizophrenia patients with relatives who have schizophrenia but no other neuropsychiatric conditions. These selection criteria are inconsistent with what we now know about the bulk of the genetic differences that have been associated with disease. The central hypothesis of this project is that there are rare genetic variants that strongly elevate the risk of various neuropsychiatric diseases, and that these risk factors can be identified most readily in families segregating multiple neuropsychiatric conditions.

  Study phs000682

• Long-read data (PacBio)

  The goal of this project was to perform long-read RNA sequencing (LR-seq, PacBio) in combination with short-read RNA-seq for systematic characterization of the isoform diversity in primary breast tumor samples. We sequenced the full-length transcriptomes of 26 breast tumors and 4 normal breast samples.

  Dataset EGAD00001006597