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• Patient 16CH

  One file of patient 16 with WGS done on Illumina HiSeq X-Ten. For research purpose and authorised user only.

  Dataset EGAD00001003440

• FFPE Normal Panel V3 Cancer Panel

  FFPE normal panel generation for use with V3 cancer panel 0618521

  Dataset EGAD00001001122

• Strelka somatic snv vcfs for EGAS00001004572

  Bulk Strelka somatic snv vcfs from tumour-normal analysis in EGAS00001004572

  Dataset EGAD00001006907

• Rare Diseases Clinical Research Network (RDCRN) Vasculitis Clinical Research Consortium (VCRC) Longitudinal Protocol for Takayasu's Arteritis

  The development and validation of accurate biological markers and predictors of disease activity and outcome for Takayasu's arteritis, a form of idiopathic vasculitis, would have a major positive impact on the clinical care of patients with this rare disease, be an important advance in the design of clinical trials and feasibility of new drug development, and provide important insight into the pathogenesis of this condition. Current measures of disease activity, including erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), while helpful in selected settings, are mostly considered highly flawed and markers whose poor correlation with disease flares makes them inadequate to help guide therapy. Similarly, ESR and CRP are clearly unhelpful as diagnostic tests for vasculitis. The current state of clinical investigation for vasculitis relies heavily upon determination of disease status solely by clinical presentation and investigator opinion. Thus, discovery and validation of more precise markers that quantifiably measure activity, predict disease course, and correlate with an important biological process, would be of great importance. Knowledge to be gained from this study could potentially be highly important. Discovery of effective biomarkers of vasculitis and assessment of long term disease course and complications by the collection of clinical and diagnostic imaging studies proposed could lead to better care and less toxic treatment regimens. Furthermore, insight into the pathophysiology of vasculitis could be gained and this might lead to better treatments and improved outcomes in terms of reduction of vascular and treatment related complications. Ideally, excellent biomarkers reflect a sophisticated understanding of the pathophysiology of a disease.

  Study phs000589

• Imputed_genetics

  Imputed genetic data for INTERVAL proteomics cohort

  Dataset EGAD00010001544

• mtDNA-Roma

  mtDNA variant positions vcf files for 86 human samples

  Dataset EGAD00010001844

• Gene Expression Profiling for study EGAS00001004165

  Gene Expression Profiling from 44 Mantle Cell Lymphoma cases

  Dataset EGAD00010001842

• Sequencing data for oesophageal and related samples - OACs release 1 (RNA)

  Dataset EGAD00001002259

• Illumina BeadArray SNP arrays

  SNP measurement. Illumina BeadArray SNP arrays for the study "Molecular characteristics in Burkitt lymphoma over age groups"

  Dataset EGAD00010002137

• INMUNGEN_CoV2_genotype

  Cohort: Raw genotype files for INMUNGEN_CoV2 cohort. Genotype Chip: HumanCore Exome Chip (Illumina) and Axiom Spanish Biobank Array (Thermofisher) genomic build: b37

  Dataset EGAD00010002174

• Converging and evolving immuno-genomic routes towards immune escape in breast cancer

  Data access committe for the project: Converging and evolving immuno-genomic routes towards immune escape in breast cancer

  Dac EGAC50000000074

• Biomarker Data from KATHERINE: A Phase III Study of Adjuvant Trastuzumab Emtansine versus Trastuzumab in Patients with Residual Invasive Disease after Neoadjuvant Therapy for HER2-Positive Breast Cancer

  Study EGAS00001006229

• GEOCODE Cohort

  Transcriptomic profile for whole blood after 24hours of stimulations

  Dataset EGAD50000001320

• WXS_TALL_t_14_16_translocation

  Whole exome-sequencing for patients with t(14;16) translocated T-ALL.

  Dataset EGAD50000001783

• scWGS primaries

  Single cell WGS (low pass) for primary samples

  Dataset EGAD00001006138

• Follicular T cell lymphoma

  Paired-end RNA-seq of follicular T cell lymphoma for the discovery of fusion transcripts

  Dataset EGAD00001006379

• RNAseq BAM files

  RNAseq BAM files for Coding and non-coding mantle cell lymphoma driver mutations

  Dataset EGAD00001006268

• pilot

  Pilot study for genome-wide sequencing of cell-free DNA from nipple aspirate fluid and plasma of breast cancer patient

  Dataset EGAD00001010836

• Reference epigenome IPS01_N_Fibroblast_smRNA-Seq data generated from KEP study

  A IPS01_N_Fibroblast_smRNA-Seq single end data for iPSC(Oct4)

  Dataset EGAD00001003501

• Reference epigenome CKD25_C_Podo_WGBS data generated from KEP study

  A CKD25_C_Podo_WGBS paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003470

• Reference epigenome CKD24_C_Podo_WGBS data generated from KEP study

  A CKD24_C_Podo_WGBS paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003469

• Reference epigenome IPS04_X_Fibroblast_mRNA-Seq data generated from KEP study

  A IPS04_X_Fibroblast_mRNA-Seq paired end data for iPSC(Oct4)

  Dataset EGAD00001003491

• Reference epigenome OB57_D_PreA_WGBS data generated from KEP study

  A OB57_D_PreA_WGBS paired end data for Preadipocyte(fat)

  Dataset EGAD00001003480

• Reference epigenome IPS01_N_Fibroblast_mRNA-Seq data generated from KEP study

  A IPS01_N_Fibroblast_mRNA-Seq paired end data for iPSC(Oct4)

  Dataset EGAD00001003488

• Reference epigenome IPS04_X_Fibroblast_smRNA-Seq data generated from KEP study

  A IPS04_X_Fibroblast_smRNA-Seq single end data for iPSC(Oct4)

  Dataset EGAD00001003504