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• Targeted capture ctDNA Library CRCQV42Run024-13

  Targeted capture ctDNA Library CRCQV42Run024-13 from patient PBC0001306, TNBC FFPE sample

  Dataset EGAD00001010466

• Targeted capture ctDNA Library CRCQV42Run026-16

  Targeted capture ctDNA Library CRCQV42Run026-16 from patient PBC0005064, TNBC FFPE sample

  Dataset EGAD00001010489

• Targeted capture ctDNA Library CRCQV42Run42-13

  Targeted capture ctDNA Library CRCQV42Run42-13 from patient PBC0001467, TNBC FFPE sample

  Dataset EGAD00001010825

• Reference epigenome Pancreas-Islet08 miRNA-Seq data generated from KEP study

  Pancreas-Islet08 miRNA-Seq single end data

  Dataset EGAD00001007189

• Targeted capture ctDNA Library CRCQV42Run021-4

  Targeted capture ctDNA Library CRCQV42Run021-4 from patient PBC0001328, TNBC FFPE sample

  Dataset EGAD00001010446

• Reference epigenome Islet-derived_iPSC04 mRNA-Seq data generated from KEP study

  Islet-derived_iPSC04 mRNA-Seq paired end data

  Dataset EGAD00001007880

• Targeted capture ctDNA Library CRCQV34Run005-11

  Targeted capture ctDNA Library CRCQV34Run005-11 from patient PBC0002595, TNBC FFPE sample

  Dataset EGAD00001010342

• Reference epigenome Islet-derived_MSC08 mRNA-Seq data generated from KEP study

  Islet-derived_MSC08 mRNA-Seq paired end data

  Dataset EGAD00001007879

• Reference epigenome Islet-derived_MSC06 miRNA-Seq data generated from KEP study

  Islet-derived_MSC06 miRNA-Seq single end data

  Dataset EGAD00001007881

• Targeted capture ctDNA Library CRCQV34Run015-20

  Targeted capture ctDNA Library CRCQV34Run015-20 from patient PBC0001432, TNBC FFPE sample

  Dataset EGAD00001010382

• Targeted capture ctDNA Library CRCQV34Run015-22

  Targeted capture ctDNA Library CRCQV34Run015-22 from patient PBC0001673, TNBC FFPE sample

  Dataset EGAD00001010383

• Targeted capture ctDNA Library CRCQV34Run005-15

  Targeted capture ctDNA Library CRCQV34Run005-15 from patient PBC0002824, TNBC FFPE sample

  Dataset EGAD00001010346

• Targeted capture ctDNA Library CRCQV34Run005-20

  Targeted capture ctDNA Library CRCQV34Run005-20 from patient PBC0002680, TNBC FFPE sample

  Dataset EGAD00001010348

• Targeted capture ctDNA Library CRCQV34Run005-4

  Targeted capture ctDNA Library CRCQV34Run005-4 from patient PBC0001627, TNBC FFPE sample

  Dataset EGAD00001010349

• Targeted capture ctDNA Library CRCQV34Run005-18

  Targeted capture ctDNA Library CRCQV34Run005-18 from patient PBC0001845, TNBC FFPE sample

  Dataset EGAD00001010347

• Targeted capture ctDNA Library CRCQV34Run005-13

  Targeted capture ctDNA Library CRCQV34Run005-13 from patient PBC0002383, TNBC FFPE sample

  Dataset EGAD00001010344

• Targeted capture ctDNA Library CRCQV34Run015-23

  Targeted capture ctDNA Library CRCQV34Run015-23 from patient PBC0001255, TNBC FFPE sample

  Dataset EGAD00001010384

• Targeted capture ctDNA Library CRCQV34Run015-24

  Targeted capture ctDNA Library CRCQV34Run015-24 from patient PBC0002255, TNBC FFPE sample

  Dataset EGAD00001010385

• Targeted capture ctDNA Library CRCQV40Run027-17

  Targeted capture ctDNA Library CRCQV40Run027-17 from patient PBC0004076, TNBC FFPE sample

  Dataset EGAD00001010390

• Targeted capture ctDNA Library CRCQV40Run027-18

  Targeted capture ctDNA Library CRCQV40Run027-18 from patient PBC0004350, TNBC FFPE sample

  Dataset EGAD00001010391

• Targeted capture ctDNA Library CRCQV40Run027-19

  Targeted capture ctDNA Library CRCQV40Run027-19 from patient PBC0004414, TNBC FFPE sample

  Dataset EGAD00001010392

• Targeted capture ctDNA Library CRCQV42Run017-24

  Targeted capture ctDNA Library CRCQV42Run017-24 from patient PBC0002294, TNBC FFPE sample

  Dataset EGAD00001010414

• Targeted capture ctDNA Library CRCQV42Run020-4

  Targeted capture ctDNA Library CRCQV42Run020-4 from patient PBC0001312, TNBC FFPE sample

  Dataset EGAD00001010430

• Targeted capture ctDNA Library CRCQV42Run021-10

  Targeted capture ctDNA Library CRCQV42Run021-10 from patient PBC0001470, TNBC FFPE sample

  Dataset EGAD00001010433

• Reference epigenome SMC01_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC01_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003845

• Reference epigenome SMC02_WGBS data generated from KEP study

  A SMC02_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003872

• Reference epigenome SMC03_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC03_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003847

• Reference epigenome SMC05_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC05_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003849

• Reference epigenome SMC06_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC06_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003850

• Reference epigenome SMC07_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC07_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003851

• Reference epigenome SMC03_WGBS data generated from KEP study

  A SMC03_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003888

• Reference epigenome SMC05_WGBS data generated from KEP study

  A SMC05_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003873

• Reference epigenome CKD23_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003497

• Reference epigenome SMC04_WGBS data generated from KEP study

  A SMC04_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003889

• HipSci - Monogenic Diabetes - RNA Sequencing - July 2017

  HipSci - Monogenic Diabetes - RNA Sequencing - July 2017

  Dataset EGAD00001003530

• Reference epigenome SMC04_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC04_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003848

• Reference epigenome SMC06_WGBS data generated from KEP study

  A SMC06_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003874

• Reference epigenome SMC08_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC08_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003852

• Reference epigenome CKD27_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003500

• Reference epigenome SMC02_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC02_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003846

• Reference epigenome SMC09_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC09_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003853

• Reference epigenome SMC07_WGBS data generated from KEP study

  A SMC07_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003875

• Reference epigenome CKD23_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003481

• 20180208_EGA_Trench_MetCellLine.1

  Illumina platform sequencing data of SureSelect exome libraries prepared from 3 samples from one donor: a normal, primary breast cancer, and cell line derived from metastasis

  Dataset EGAD00001003956

• Reference epigenome SMC09_WGBS data generated from KEP study

  A SMC09_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003877

• Reference epigenome SMC01_WGBS data generated from KEP study

  A SMC01_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003871

• Reference epigenome KNIH010 mRNA-seq data generated from KEP study

  A KNIH010 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002176

• Reference epigenome KNIH011 mRNA-seq data generated from KEP study

  A KNIH011 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002177

• Reference epigenome SMC08_WGBS data generated from KEP study

  A SMC08_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003876

• HipSci - Monogenic Diabetes - RNA Sequencing - October 2016

  HipSci - Monogenic Diabetes - RNA Sequencing - October 2016

  Dataset EGAD00001003180

• Reference epigenome CKD27_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003484

• Targeted capture ctDNA Library CRCQV42Run021-9

  Targeted capture ctDNA Library CRCQV42Run021-9 from patient PBC0001323, TNBC FFPE sample

  Dataset EGAD00001010449

• Targeted capture ctDNA Library CRCQV42Run022-8

  Targeted capture ctDNA Library CRCQV42Run022-8 from patient PBC0002826, TNBC FFPE sample

  Dataset EGAD00001010459

• Targeted capture ctDNA Library CRCQV42Run023-19

  Targeted capture ctDNA Library CRCQV42Run023-19 from patient PBC0002383, plasma baseline repeat sample

  Dataset EGAD00001010462

• Targeted capture ctDNA Library CRCQV42Run023-20

  Targeted capture ctDNA Library CRCQV42Run023-20 from patient PBC0001224, plasma baseline repeat sample

  Dataset EGAD00001010463

• Targeted capture ctDNA Library CRCQV42Run024-14

  Targeted capture ctDNA Library CRCQV42Run024-14 from patient PBC0002853, TNBC FFPE sample

  Dataset EGAD00001010467

• Targeted capture ctDNA Library CRCQV42Run025-19

  Targeted capture ctDNA Library CRCQV42Run025-19 from patient PBC0003364, TNBC FFPE sample

  Dataset EGAD00001010482

• Targeted capture ctDNA Library CRCQV42Run025-5

  Targeted capture ctDNA Library CRCQV42Run025-5 from patient PBC0001589, TNBC FFPE sample

  Dataset EGAD00001010483

• Targeted capture ctDNA Library CRCQV42Run025-7

  Targeted capture ctDNA Library CRCQV42Run025-7 from patient PBC0003334, TNBC FFPE sample

  Dataset EGAD00001010485

• Targeted capture ctDNA Library CRCQV42Run034-24

  Targeted capture ctDNA Library CRCQV42Run034-24 from patient PBC0001396, TNBC FFPE sample

  Dataset EGAD00001010572

• Reference epigenome KNIH011 miRNA-seq data generated from KEP study

  A KNIH011 miRNA-seq single end data for podocytes

  Dataset EGAD00001002770

• Targeted capture ctDNA Library CRCQV42Run036-10

  Targeted capture ctDNA Library CRCQV42Run036-10 from patient PBC0002062, plasma 21 month sample

  Dataset EGAD00001010592

• Targeted capture ctDNA Library CRCQV42Run036-20

  Targeted capture ctDNA Library CRCQV42Run036-20 from patient PBC0003364, plasma 15 month sample

  Dataset EGAD00001010599

• Targeted capture ctDNA Library CRCQV42Run041-8

  Targeted capture ctDNA Library CRCQV42Run041-8 from patient PBC0001404, TNBC FFPE sample

  Dataset EGAD00001010638

• Targeted capture ctDNA Library CRCQV42Run42-14

  Targeted capture ctDNA Library CRCQV42Run42-14 from patient PBC0001375, TNBC FFPE sample

  Dataset EGAD00001010826

• SF12374

  SF12374 snATAC Oligodendroglioma. Tumor Location: Right frontal. Age: 33. Sex: Male.

  Dataset EGAD00001006315

• Methylation tumour profiles for EGAS00001004572

  Bulk methylation tumour profiles from infinium methylation epic bead kit in EGAS00001004572

  Dataset EGAD00001006909

• Targeted capture ctDNA Library CRCQV42Run022-22

  Targeted capture ctDNA Library CRCQV42Run022-22 from patient PBC0002062, plasma 12 month repeat sample

  Dataset EGAD00001010456

• Targeted capture ctDNA Library CRCQV42Run035-20

  Targeted capture ctDNA Library CRCQV42Run035-20 from patient PBC0003334, plasma 6 month sample

  Dataset EGAD00001010585

• GBM-Space: Joint Transcriptome and Chromatin Accessibility Profiling of Glioblastoma (10x Genomics - Multiome)

  GBM-Space: Joint Transcriptome and Chromatin Accessibility Profiling of Glioblastoma (10x Genomics - Multiome)

  Dataset EGAD00001015526

• After the completion of CINECA, EUCANCan, and euCanSHare. What's next?

  CINECA, EUCANCan, and euCanSHare were part of the EUCAN Cluster, made up of six projects that received funding under the same Horizon 2020 call. All EUCAN projects (CINECA, EUCANCan, EUCAN-Connect, euCanSHare, Receptor Plus, and ReCoDID) were aimed at facilitating data reuse and knowledge discovery by enhancing data exchange and long-term collaboration in the health field. Here are some highlights about EGA’s contribution to these projects. CINECA: advances in Federated discovery and infrastructure for cohorts CINECA (Common Infrastructure for National Cohorts in Europe, Canada, and Africa) developed a federated cloud-based infrastructure for making genomic and biomolecular data accessible. The project has assembled a virtual cohort of 1.4 million individuals from sources such as the EGA, CanDIG and H3Africa. The EGA–CRG co-leaded a work package Work Package 1 on Federated Data Discovery and Querying. Beacon v2, championed by Jordi Rambla and Lauren Fromont, has been one of the central elements for the discovery of human genetic and phenotypic data. The EGA-CRG contributed to the development of a model for cohort discovery inside the Beacon v2 model. The team also delivered a Discovery Portal was implemented to explore cohorts and individuals of synthetic data. It is a UI that gathers a network of Beacons, a service for query expansion, and a visualisation tool. Have a look at the Discovery Portal UI, gathering a network of Beacons. You will find entities such as the Barcelona Supercomputing Center (BSC), BioData.pt and the European Genomic Data Infrastructure (GDI), among others. euCanSHare: facilitation of data access management This joint EU-Canada project aimed to build a European and Canadian FAIR platform for cardiovascular data sharing and analysis. The EGA’s tasks contributed to the development of the data management plan and data flows, main web-portal and interoperability protocols. An important part of the EuCanShare platform is the data access manager, a tool for the data owners to control the access to their sensitive datasets. We built a user-friendly interface for data access committees (data owners) to easily manage their data requests and access credentials. The data access portal facilitates the creation and internal organization of the data access committees as well as the linkage of data usage conditions to specific datasets. The interface includes filters for browsing requests and page to visualize the request history helping with the handling of data access requests. EUCANCan: toward a federation of clinical institutions in oncology The EUropean-CAnadian CANcer Network worked towards building a federated network to advance personalized medicine in oncology, by promoting the standard analysis, management and sharing of harmonized genomic and phenotypic data. The EGA led tasks on defining data flows and preparing an adapted infrastructure for long-term data storage and sharing. One of the key contributions was the conception of the EGA communities, offering standard and interoperable solutions for data discovery, processing, and sharing to projects and institutions that would like to manage and share data in the context of the EGA ecosystem. What's Next? The participation in projects such as CINECA, EUCANCan, and euCanSHare provide us with experience for future projects. We are happy to enhance data sharing and reuse, vital for advancing clinical and genomic research. The Discovery Portal is a wonderful proof of concept for the Beacon network. Next, we will finalise both the network and the user interface, and make sure it can be applied to more clinically-centred settings like hospitals. Most data hosted at the EGA are about cancer research. Currently, we contribute to building tools and infrastructure to empower oncology research in several European projects such as EUCAIM, EUCANIMAGE and EOSC4Cancer. We also do not lose sight of initiatives in the field such as the International Cancer Genome Consortium (ICGC) with the project ARGO, whose data model was adopted in EUCanCan.

  Blog cineca-eucancan-and-eucanshare-were-concluded-in-june

• The aim of this project is to identify, on 15 French Caucasian and 10 African-Caribbean men, through an integrative approach of DNA sequencing and transciptomic analyses, relevant genomic events that characterize or allow targeting the various phenotypes of aggressiveness of early stages of prostate cancer.

  Every year about 70,000 new cases of prostate cancer are diagnosed in France and 8,700 men died from this disease. But the incidence and the rate of mortality per 100,000 men in the French West Indies are respectively 1.5x and 2x upper than the rates observed in the metropolitan France. The aim of this project is to identify, on 15 French Caucasian and 10 African-Caribbean men, through an integrative approach of DNA sequencing and transciptomic analyses, relevant genomic events that characterize or allow targeting the various phenotypes of aggressiveness of early stages of prostate cancer.

  Study EGAS00001002176

• HELIUS cohort gut microbiome batch2

  The gut microbiota composition is unique to every individual but is shaped by common factors including diet, lifestyle, medication use, early-life determinants, living environment or genetics. Most of these factors may be influenced by ethnicity. This study explored variations in fecal microbiota composition in 6048 individuals with different ethnic backgrounds living in the same geographical area (Amsterdam, the Netherlands). The HELIUS data are owned by the Amsterdam University Medical Centers, location AMC in Amsterdam, The Netherlands. To allow sharing of microbiome data collected in HELIUS with (inter)national researchers, 16s rRNA sequence analysis has been stored at the European genome-phenome archive (EGA; accession code EGAD00001004106). This requires that access needs to be granted, also because the HELIUS data are stored with relevant phenotypical variables. Access is granted to all researchers affiliated with an internationally recognized research institution who request to use the HELIUS data within the EGA context, after having signed the data transfer agreement. Any researcher can request the data by submitting a proposal to the HELIUS Executive Board as outlined at http://www.heliusstudy.nl/en/researchers/collaboration, by email: heliuscoordinator at amsterdamumc dot nl. The HELIUS Executive Board will check proposals if they do not conflict with ethical approvals and informed consent forms of the HELIUS study.

  Dataset EGAD00001009732

• HELIUS cohort gut microbiome

  The gut microbiota composition is unique to every individual but is shaped by common factors including diet, lifestyle, medication use, early-life determinants, living environment or genetics. Most of these factors may be influenced by ethnicity. This study explored variations in fecal microbiota composition in 6048 individuals with different ethnic backgrounds living in the same geographical area (Amsterdam, the Netherlands). The HELIUS data are owned by the Amsterdam University Medical Centers, location AMC in Amsterdam, The Netherlands. To allow sharing of microbiome data collected in HELIUS with (inter)national researchers, 16s rRNA sequence analysis has been stored at the European genome-phenome archive (EGA; accession code EGAD00001004106). This requires that access needs to be granted, also because the HELIUS data are stored with relevant phenotypical variables. Access is granted to all researchers affiliated with an internationally recognized research institution who request to use the HELIUS data within the EGA context, after having signed the data transfer agreement. Any researcher can request the data by submitting a proposal to the HELIUS Executive Board as outlined at http://www.heliusstudy.nl/en/researchers/collaboration, by email: heliuscoordinator at amsterdamumc dot nl. The HELIUS Executive Board will check proposals if they do not conflict with ethical approvals and informed consent forms of the HELIUS study.

  Dataset EGAD00001004106

• DAC for "Pyjacker identifies enhancer hijacking events in acute myeloid leukemia including MNX1 activation via deletion 7q"

  This is the DAC for the study "Pyjacker identifies enhancer hijacking events in acute myeloid leukemia including MNX1 activation via deletion 7q" of Christoph Plass (c.plass@dkfz.de).

  Dac EGAC50000000472

• COVID-19 Exome Sequencing DAC policy

  This is the DAC policy for exome sequencing data from COVID-19 patients from EASI genomics and NIMgentics and belonging to the study "Variability in immune response genes and prediction of severe SARS-CoV-2 infection(INMUNGEN-Cov2 project)"

  Dac EGAC50000000065

• Tumor Whole-exome sequencing

  20 tumor tissue WES data with paired cfDNA sWGS data. This data is used for concordance analysis between cfDNA-based and tissue-based CNA. The WES libraries were sequenced across 3 independent runs using multiple lanes.

  Dataset EGAD50000001864

• IL2 data set including 59 samples

  The dataset is referenced by EGA Study ID EGAS00001003605, which includes the short-reads data for 59 samples. All short-reads data files are in fastq format.

  Dataset EGAD00001004967

• RNA sequencing of 20 intra/extra hepatic bileduct organiods

  This dataset contains RNA sequencing data for 20 intra/extra hepatic bileduct organiods. Data is in BAM format and was processed by STAR.

  Dataset EGAD00001005225

• Lymphocyte RNA profiling

  This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002183

• Nanopore medulloblastoma data

  Nanopore data from 3 medulloblastoma samples of which 2 are tumor-normal pairs sequenced with the MinIon and one is tumor only data sequenced on the PromethIon. The data consists of BAM files aligned using minimap2

  Dataset EGAD00001010851

• Colon adenomas and adenocarcinomas and matched mucosae

  Whole exome sequencing of neoplastic colorectal lesions, matched normal mucosa and peripheral blood leucocytes from 7 individuals. Data is contained within FASTQ files.

  Dataset EGAD00001010875

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Rotenone from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008062

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008054

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D2, replicate 2, glucose concentration 1mM

  Dataset EGAD00001008056

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Resveratrol from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008060

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008064

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D3, replicate 2, glucose concentration 1mM

  Dataset EGAD00001008066

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Resveratrol from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008068

• Transcriptome of human CD4+ T-lymphyocyte (regulatory-T-cells) treated with Resveratrol from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008070

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Resveratrol from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008072

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Resveratrol from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008040

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Rotenone from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008042

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008044

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Oligomycin from donor D1, replicate 2, glucose concentration 1mM

  Dataset EGAD00001008046

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Resveratrol from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008050

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells) treated with Rotenone from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008052

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Oligomycin from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008088

• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Rotenone from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008090

• Transcriptome of human CD4+ T-lymphyocyte (regulatory-T-cells) treated with Resveratrol from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008078