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• RNAseq files (dataset 2 of 2) for Mullighan PAX5_B-ALL

  RNAseq files (dataset 2 of 2) for Mullighan PAX5_B-ALL paper titled "PAX5-driven Subtypes of B-cell Acute Lymphoblastic Leukemia"

  Dataset EGAD00001004463

• BLUEPRINT release August 2014, DNase-Hypersensitivity for inflammatory macrophage

  DNase-Hypersensitivity data for 2 inflammatory macrophage sample(s). 2 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_dnaseseq_analysis_20140811

  Dataset EGAD00001000926

• 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/

  Dataset EGAD00001001363

• BLUEPRINT release August 2016, DNase-Hypersensitivity for macrophage - T=6days LPS, on genome GRCh38

  DNase-Hypersensitivity data for 4 macrophage - T=6days LPS sample(s). 6 run(s), 4 experiment(s), 4 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_dnaseseq_analysis_20160816

  Dataset EGAD00001002290

• Combination Therapies for Personalised Cancer Medicine in drug resistant EGFR mutant lung cancer (2019-06-10)

  This study involves mutagenizing a range of different cell lines with ENU to identify those mutations which engender resistance to targeted treatment. . This dataset contains all the data available for this study on 2019-06-10.

  Dataset EGAD00001005080

• Single-Cell Data from "Spatiotemporal T-cell tracking for personalized T-cell receptor T-cell therapy designs in childhood cancer"

  This dataset contains the FASTQ files from the Single-Cell GEx+TCR 10X Genomics data from the study "Spatiotemporal T-cell tracking for personalized T-cell receptor T-cell therapy designs in childhood cancer" (EGAS00001008174)

  Dataset EGAD00001015632

• Integrating Circulating Tumor DNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer

  DNA was isolated from pre-treatment and mid-treatment plasma samples and matched germline samples from patients undergoing chemoradiation therapy for stage IIB-IIIB non-small cell lung cancer. Sequencing libraries were hybrid captured using custom selector pools designed to target genes frequently mutated in non-small cell lung cancer prior to ultra-deep next generation sequencing.

  Study phs003947

• Comprehensive survey for the construction of an integrated database of food, gut microbiome, and health information (the "Sukoyaka Health Survey") A data collection study exploring the relationship between the gut microbiome, food, health, and the genome.

  We conducted a cohort-based study to investigate the association between the genetic background and diet/lifestyle in 51 healthy Japanese. We analyzed some participants living in Nagahama. The SNP array (Japonica Array v2) was used for genotyping. Shotgun metagenomic sequencing of fecal microbiome were analyzed.

  Study JGAS000680

• Khoe-San genome Project (KSGP)

  The Khoe-San Genome Project (KSGP) includes whole genome data generated for 190 study participants from Southern Africa (Namibia and South Africa) representing people speaking a Namibian Khoe-San language (n=150, 12 groups) or regional non-Khoe-San groups (n=40), specifically defined here as Namibian Himba (n=10), South African Southern Bantu (n=12 and representing the Ubuntu Genome Project led by the University of Limpopo) or South African Coloured/European (n=18). Deep whole genome data has been generated for the purposes of cataloguing the extent of regional genetic diversity, creating population specific references and evolutionary trajectories, while creating a population-matched panel of normal (PoN) genetic diversity required for disease associated studies across the region.

  Dac EGAC50000000798

• in silico drug target prediction for melanoma

  The in silico single gene deletion step was adapted to simulate the knock-out of all targets of a drug on an objective function such as growth or energy balance. Based on publicly available and in-house large scale transcriptomic data, metabolic models for melanoma were reconstructed, enabling the prediction of 28 candidate drugs and estimating their respective efficacy.

  Study EGAS00001006463