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• Transcriptome of human CD4+ T-lymphyocyte (naive-T-cells) treated with Resveratrol from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008075

• Transcriptome of human CD4+ T-lymphyocyte (regulatory-T-cells) treated with Resveratrol from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008077

• postQC Genotypes

  postQC genotype data from the Affymetrix AxiomTM HGCoV2 1 array in plink binary format. QC was carried out using PLINK v1.9

  Dataset EGAD00010002436

• Single Cell Analysis Program - Transcriptome (SCAP-T)

  This initiative is part of the Single Cell Analysis Program (SCAP) and is funded through the NIH Common Fund (See http://nihroadmap.nih.gov/), which supports cross-cutting programs that are expected to have exceptionally high impact. Common Fund initiatives address key roadblocks in biomedical research that impede basic scientific discovery and its translation into improved human health. In addition, these programs capitalize on emerging opportunities to catalyze progress across multiple biomedical fields. Single cell analysis has recently emerged as an important field of research because technologies have improved in sensitivity and throughput sufficiently to begin measuring and understanding heterogeneity in complex biological systems and correlating it with changes in biological function and disease processes. By profiling individual cells it is possible to resolve rare cells, transient cell states, and the influence of organization and environment on such cells and states, which cannot be described by ensemble measurements. The long-term goal of the SCAP is to accelerate this move towards personalizing health to the cellular level by understanding the link between cell heterogeneity, tissue function and emergence of disease through the discovery, development and translation of innovative approaches which will dramatically change the way cells are characterized. The SCAP will focus on research, which will systematically measure, analyze and model cell-to-cell variation, and identify crucial differences and rare biological states, which may have important functional consequences. Under SCAP, there are three studies to evaluate the cellular heterogeneity using transcriptional profiling of single cells (U01): University of Pennsylvania: Role of single cell mRNA variation in systems associated electrically excitable cells University of Southern California: Evaluation of cellular heterogeneity using patchclamp and RNA-seq of single cells University of California at San Diego: Single cell sequencing and in situ mapping of RNA transcripts in human brains The SCAP has been designed as a five-year program with several components: (1) the collection, analysis and sharing of comprehensive expression datasets to understand the role of heterogeneity in tissues and systemically and identify critical parameters and states; (2) the discovery of new, innovative tools for spatiotemporal imaging, manipulation, analysis and modeling of a biologically relevant population of cells with minimal perturbation; (3) milestone-driven validation and translation of technologies for characterizing single cells in situ meeting the needs of end-users; and (4) development and coordination of a multidisciplinary research community through workshops and other collective endeavors. Further details about the NIH SCAP-T program can be found here http://commonfund.nih.gov/singlecell/. The SCAP-T data sets include the detailed phenotype information, experimental protocols, QC information, RNA-sequencing data and NGS results from human heart and human brain cells. The first data release includes 697 single cells from human brain and heart. The second data release includes an additional 978 single cells from human brain and heart. The third data release includes an additional 1631 single cells from human brain and heart. The fourth data release is a correction to this study, but adds no new cells. The fifth data release includes an additional 2984 single cells from human brain and heart. The sixth data release includes an additional 944 single cells from human brain and heart. The seventh data release includes an additional 1977 single cells from human brain and heart. The SCAP-T data portal provides a customized interface for users to quickly identify and retrieve files by phenotypes, and data properties such as sequencing facility or coverage for all of these 9231 single cells. For more information about the SCAP-T study and the data portal, please visit http://www.scap-t.org.

  Study phs000833

• Whole_genome_sequencing_of_rhabdomyosarcoma_tumouroids

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008270

• Paediatric IBD Mosaicism

  This is a DNA sequencing study investigating genetic mechanisms underpinning childhood inflammatory bowel disease.

  Study EGAS00001002489

• Blina_Tumour_project

  The aim of this study is to reconstruct the embryology of childhood tumours.

  Study EGAS00001006486

• Whole_Genome_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008274

• NINDS Family-Based Whole-Genome Sequencing to Find Modifiers of Age of Onset in Huntington's Disease

  The goal of our studies is to identify genetic modifiers of neurodegeneration in Huntington's disease (HD). HD is caused by expansion of CAG repeats in the huntingtin (Htt) gene, with longer stretches often leading to more rapid disease onset and progression. Yet, for a given number of repeats, the age of symptom onset can be variable, differing by up to decades. Thus, the age of onset of motor symptoms in HD is only partly explained by the length of the CAG expansion. Available evidence suggests that genetic modifiers contribute to the variation in HD onset. Identifying genetic modifiers is important because they may provide critical insights into HD pathogenesis and reveal key pathways that could be targeted by novel HD therapeutics. This is important since there are no disease-modifying therapies for HD, and mHtt is an unattractive small-molecule drug target. We recruited 21 HD families with varying characteristics of disease progression and age of onset and obtained medical histories, clinical records and DNA samples that were subjected to whole-genome sequencing (WGS). These WGS data describe families of 104 subjects, including HD patients and their unaffected family members. These individuals were selected based on individual clinical histories and family structures that best fit our criteria for expressing potential genetic modifiers. We are testing the hypothesis that novel, rare genetic variants contribute to HD and those genetic modifiers can be identified by WGS.

  Study phs001071

• Open_Targets_020_Epigenomes_of_Cell_Lines

  There is currently a drive to establish cell based assay systems of greater human biological and disease relevance through the use of well characterised transformed cell lines, primary cells and complex cellular models (e.g. co-culture, 3D models). However, although the field is gaining valuable experience in running more non-standard & complex cell assays for target validation and compound pharmacology studies, there is the lack of a systematic approach to determine if this expansion in cell assay models is reflected in increased human biological and disease relevance. The increasing wealth of publically available transcriptomic, and epigenome (ENCODE and Epigenome Roadmap) data represents an ideal reference mechanism for determining the relationship between cell types used for target & compound studies to primary human cells and tissues from both healthy volunteers & patients. The CTTV020 epigenomes of cell line project aims to generate epigenetic and transcriptomic profiles of cell lines and compare these with existing and newly generated reference data sets from human tissue and cell types. The aim is to identify assay systems which will provide greater confidence in translating target biology and compound pharmacology to patients. Multiple cell types commonly used within research have been grouped according to biology. Examples include erythroid, lung epithelial, hepatocyte cell types and immortalised models of monocyte / macrophage biology. 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 please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001003138