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• WTCCC case-control study for Coronary Artery Disease - Combined Controls

  WTCCC genome-wide case-control association study for Coronary Artery Disease (CAD) using six disease collections together with the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000004

• WTCCC case-control study for Type 1 Diabetes - Combined Controls

  WTCCC genome-wide case-control association study for Type 1 Diabetes (T1D) using six disease collections together with the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000015

• WTCCC case-control study for Type 2 Diabetes - Combined Controls

  WTCCC genome-wide case-control association study for Type 2 Diabetes (T2D) using six disease collections together with the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000017

• A new beta-globin mutation responsible of a beta-thalassemia (HbVar database ID 2928) was observed in 8 unrelated French families. The mutation carriers originated from Nord-Pas-de-Calais, a Northern French region where the chief town is Lille.5 unrelated mutation carriers were genotyped for a set of 12 microsatellites from chromosome 11, around the beta-globin gene. Among the 5 mutation carriers, 4 were genotyped for 97 European Ancestry Informative SNPs (EAIMs). 32 controls from Nord-Pas-de-Calais were genotyped for the microsatellites and SNPs.

  Dac EGAC00001000245

• Large Cancer Fingerprint Screening for Detection of Minimal Residual Disease.

  We describe an approach for large cancer fingerprint screening and its application to cell-free DNA from patients with cancer. Our approach involves tracking somatic mutations identified from patients' tumor biopsies, in the cell-free DNA from a blood draw.

  Study phs001977

• NCI's Collection of Datasets for Health, Medical, and Biomedical Research Purposes

  This collection provides access to NCI's collections of datasets for the purpose of performing health, medical, or biomedical (HMB) research. This collection would not support ancestry lineage research not related to a specific medical or health conditions.

  Study phs003044

• Healthy control cfMeDIP-seq

  This dataset comprises 46 raw fastq files derived from 23 plasma cfDNA samples from 23 healthy (CTL) cancer-free donors. For each sample, 10ng of cfDNA was used for library preparation utilizing the cfMeDIP-seq technology based on the 5mC antibody-immunoprecipitation strategy. Libraries were validated via Bioanalyzer trace analysis and sequenced on the Illumina NovaSeq 6000 platform with paired-end 150-bp model for ~100 million reads per sample.

  Dataset EGAD50000000652

• DAC for "Longitudinal copy number variation analysis from plasma DNA of head and neck cancer patients under re-radiotherapy"

  This is the DAC for "Longitudinal copy number variation analysis from plasma DNA of head and neck cancer patients under re-radiotherapy" with Florian Janke f.janke@dkfz-heidelberg.de German Cancer Research Center, Division of Cancer Genome Research Holger Sültmann h.sueltmann@dkfz-heidelberg.de German Cancer Research Center, Division of Cancer Genome Research Magdalena Görtz magdalena.goertz@dkfz-heidelberg.de German Cancer Research Center, Multiparametric Methods for Early Detection of Prostate Cancer

  Dac EGAC50000000114

• Whole-Exome Sequencing Plasma Control Samples for Benchmarking

  This study contains whole-exome sequencing plasma and HapMap (12877, 12878) control samples for bioinfomatic bencmarking. Plasma samples contain variations in the genes KRAS, NRAS, PIK3CA, and EGFR, including insertions and deletions, at 0.0%, 0.1%, 1.0%, and 5.0%.

  Study EGAS50000000565

• Transcriptomic analysis of LINE1 expression in the human brain

  Twist Bioscience probes were designed to LINE1 and HERV sequences for targeted amplification. Enriched cDNA libraries were then sequenced on the PacBio Sequel II, 4 samples/SMRTcell. The available files have undergone ccs (using the default parameters except for --min-rq 0.9, which was a recommended parameter for Iso-Seq) and barcode demultiplexing. Recommended analysis follows https://isoseq.how/, starting at the primer removal and demultiplexing step with lima.

  Dataset EGAD50000000265

• Lam-ESC&Lam-Recombination data

  This dataset includes all data produced in the study describing "Lam-ESC&Lam-Recombination", a method for sequencing of signal joints and coding joints generated from human light chain antigen receptor loci during V(D)J recombination in B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) patients. This dataset includes: - Amplicon Lam-ESC for a total of 86 BCP-ALL patients - fastq files - Amplicon Lam-Recombination for a total of 43 BCP-ALL patients - fastq files

  Dataset EGAD50000000597

• Ankara Bilkent City Hospital Clinical Research Ethics Committee

  Ankara Bilkent City Hospital Clinical Research Ethics Committee is responsible for the oversight and management of access to genomic and phenotypic data generated by the IDH1 Somatic Mutation Profile in Intrahepatic Cholangiocarcinoma . The committee reviews applications for data access to ensure that the proposed research is consistent with the informed consent provided by participants and meets ethical standards. Access is granted to qualified researchers for non-commercial research purposes upon signing a Data Access Agreement (DAA).

  Dac EGAC50000000940

• RNAseq of jejunum (small intestine) harvested from CDAHFD mice treated for 8 weeks with either the MGAT2 inhibitor compound BMS-963272 or vehicle

  This experiment consists of RNAseq of jejunum (small intestine) harvested from CDAHFD mice treated for 8 weeks with either the MGAT2 inhibitor compound BMS-963272 (N = 14) or with vehicle (N = 14).

  Study EGAS00001006584

• Digital tEchnology For Lung Cancer Treatment

  DEFLeCT metadata: 1) A table containing the mutational profile for all human genes across the cohort of the 81 PROMOLE lung cancer samples. 2) A table containing the expression levels for all human genes across the cohort of the 81 PROMOLE lung cancer samples. 3) A table containing the clinical, histology data and follow-ups of the cohort of 81 PROMOLE lung cancer patients.

  Dataset EGAD00001010838

• Isotype-resolved sequencing of B-cell receptor in sorted memory populations (2017-09-13)

  Optimisation of ex vivo Memory B cell Expansion/Differentiation for Interrogation of Rare Peripheral Memory B Cell Subset Responses 1) 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/ This dataset contains all the data available for this study on 2017-09-13.

  Dataset EGAD00001003747

• Y-phylogeny-haplogroupDE (2019-04-03)

  High-coverage whole genome sequences using Hiseq X for 4 individuals to investigate their Y chrosmosmes' relationship to the known phylogeny. 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/ . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004892

• Dataset-linking-WGS-samples-in-ega-box-81-via-README-for-study-EGAS00001002923

  This dataset contains 318 Tumor and Control WGS files submitted in another EGA box for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.

  Dataset EGAD00001007861

• DNA methylation (RRBS) data for the glioblastoma progression study (GBMatch).

  Genome-wide profiling of DNA methylation levels by RRBS in 349 samples, derived from 112 glioblastoma (IDH wildtype) patients, 13 IDH muated brain tumor patients, and 5 normal brain controls. For each patient samples from at least two and up to six tumor resections are available. For 6 patients multiple regions of each tumor were sampled.

  Dataset EGAD00001003427

• Data for Paper: Combining transcription factor binding affinities with open chromatin data for accurate gene expression prediction

  mRNA, total RNA, small noncoding RNA, NOMe-Seq and DNase-Seq data from following samples (not every Sequencing Type for every sample): 01_HepG2_LiHG_Ct1 41_Hf01_LiHe_Ct 41_Hf02_LiHe_Ct 41_Hf03_LiHe_Ct 51_Hf03_BlCM_Ct 51_Hf04_BlCM_Ct 51_Hf03_BlEM_Ct 51_Hf04_BlEM_Ct 51_Hf03_BlTN_Ct 51_Hf04_BlTN_Ct Metadata available at deep.dkfz.de

  Dataset EGAD00001002735

• To profile the landscape of sebaceous tumours_RNAseq

  Sebaceous tumours are a rare cutaneous cancer with potential for aggressive behaviour. However, limited information is available on these cancers with few published cases. Here we wish to exome sequence these cancers to define the first genomic landscape for this malignancy. We will extract RNA from formalin-fixed, paraffin-embedded (FFPE) cores. Cores may be obtained from lesional and non-lesional tissues of primaries as well as matching metastases. The extracted RNA will be used for RNA sequencing.

  Dataset EGAD00001015368

• BLUEPRINT September 2016, Bisulfite-Seq Acute Lymphocytic Leukemia for precursor B cell from bone marrow, on Genome GRCh38

  Bisulfite-Seq data for 1 sample(s) Acute Lymphocytic Leukemia for precursor B cell from bone marrow, on Genome GRCh38. 3 run(s), 1 experiment(s), 0 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001002969

• National Eye Institute (NEI) Age-Related Eye Disease Study (AREDS)

  The Age-Related Eye Disease Study (AREDS) was initially designed as a long-term multi-center, prospective study of the clinical course of age-related macular degeneration (AMD) and age-related cataract. In addition to collecting natural history data, AREDS included a clinical trial of high-dose vitamin and mineral supplements for AMD and a clinical trial of high-dose vitamin supplements for cataract. AREDS participants were 55 to 80 years of age at enrollment and had to be free of any illness or condition that would make long-term follow-up or compliance with study medications unlikely or difficult. On the basis of fundus photographs graded by a central reading center, best-corrected visual acuity and ophthalmologic evaluations, 4,757 participants were enrolled in one of several AMD categories, including persons with no AMD. The clinical trials for AMD and cataract were conducted concurrently. AREDS participants were followed on the clinical trials for a median time of 6.5 years. Subsequent to the conclusion of the clinical trials, participants were followed for an additional 5 years and natural history data were collected. The AREDS research design is detailed in AREDS Report 1. AREDS Report 8 contains the mainline results from the AMD trial; AREDS Report 9 contains the results of the cataract trial. Blood samples were also collected from 3,700+ AREDS participants for genetic research. Genetic samples from 600 AREDS participants (200 controls, 200 Neovascular AMD cases, and 200 Geographic Atrophy cases) were selected using data available in March 2005 and then were evaluated with a genome-wide scan. These data, as well as selected phenotypic data, were made available in the dbGaP. DNA from AREDS participants, which is currently being stored in the AREDS Genetic Repository, is available for research purposes. However, not all of the 3,700+ AREDS participants who submitted a blood sample currently have DNA available. In addition to including the data from the genome-wide scan on the 600 original samples, this second version of the AREDS dbGaP database provides a comprehensive set of data tables with extensive clinical information collected for the 4,757 participants who participated in AREDS. The tables include information collected at enrollment/baseline, during study follow-up, fundus and lens pathology, nutritional estimates, quality of life measures and measures of morbidity and mortality. In November 2010, over 72,000 high quality fundus and lens photographs of 595 AREDS participants (of the original 600 selected for the genome-wide scan) were made available in the AREDS dbGaP. In addition to the genome-wide scan data, the fundus and lens grading data for these participants are also available through the AREDS dbGaP. Details about the ocular photographs that are available may be found in the document "Age-Related Eye Disease Study (AREDS) Ocular Photographs". In January 2012, a measure of daily sunlight exposure was added in a separate "sunlight" table. Furthermore, the "followup" table has been revised. The visual acuity for the right eye was inadvertently missing at odd-numbered visits (01, 03, 05, etc.). This data is now part of the table. In February 2014 over 134,500 high-quality fundus photographs (macular field F2) of 4613 AREDS participants were added to the existing AREDS dbGaP resource. The AREDS dbGaP image archive already contains over 72,000 high quality fundus and lens photographs of 595 AREDS participants for whom dbGaP-accessible genotype data exist. Information about the available ocular photographs found in the document "Age-Related Eye Disease Study (AREDS) Ocular Photographs" has been updated with an addendum. It is hoped that this resource will better help researchers understand two important diseases that affect an aging population. These data may be applied to examination and inference on genetic and genetic-environmental bases for age-related diseases of public health significance and may also help elucidate the clinical course of both conditions, generate hypotheses, and aid in the design of clinical trials of preventive interventions. Definitions of Final AMD Phenotype Categories Please see phd001138.1 for a detailed description of how AREDS participants' final AMD phenotype was categorized. User's Guide for AREDS Phenotype Data A detailed User's Guide for the AREDS phenotype data is available. This User's Guide is meant to be a comprehensive document which explains the complexities of the AREDS data. It is recommended that all researchers using AREDS phenotype data make use of this User's Guide.

  Study phs000001

• HCC.GNE RNA-Seq

  RNA-Sequencing data (raw read sequences) for 23 samples, from 12 patients, for the study "Diverse modes of genomic alterations in Hepatocellular Carcinoma"

  Dataset EGAD00001000886

• CCA methylation data (12 CCA, 7 normal)

  Raw idat files for DNA methylation profiling for 12 CCAs and 7 normal bile duct tissues. DNA methylation profiling was performed using Infinium MethylationEPIC v2.0 Kit.

  Dataset EGAD00010002613

• RNA-seq data for Heterogeneity of IKZF1 genomic alterations and risk of relapse in childhood BALL

  RNA-seq data for 14 samples with B-cell acute lymphoblastic leukemia

  Dataset EGAD50000000151

• Single-cell RNA-seq of Bone marrow samples

  For two donors, we collected CD34 plus and CD34 minus hematopoietic cells for the single-cell RNA-seq analysis.

  Dataset EGAD50000000514

• WGS data for genomic determinants of response and resistance to inotuzumab in B-ALL

  Whole genome sequencing data for 45 samples with B-cell acute lymphoblastic leukemia

  Dataset EGAD50000000096

• WES data for genomic determinants of response and resistance to inotuzumab in B-ALL

  Whole exome sequencing data for 69 samples with B-cell acute lymphoblastic leukemia

  Dataset EGAD50000000097

• Haukeland University Hospital Data Access Committee for STRAT-PARK datasets archived in Federated EGA Norway

  This is the Data Access Committee mandated by Haukeland University Hospital for the STRAT-PARK cohort: an initiative to stratify Parkinson's disease.

  Dac EGAC50000000428

• DAC for "Identification and characterization of tertiary lymphoid structures in brain metastases"

  This is the DAC for the study "Identification and characterization of tertiary lymphoid structures in brain metastases" of Prof. Dr. Guido Reifenberger (Guido.Reifenberger@med.uni-duesseldorf.de)

  Dac EGAC50000000369

• Whole exome sequencing of prDLBCL

  To investigate the mutational landscape of prDLBCL, 34 cases were selected for whole exome sequencing.

  Study EGAS50000000403

• sc-DECISION

  Data derived from PBMC and blood samples for the single cell and bulk RNAseq analysis for the sc-DECISION paper. https://doi.org/10.1101/2025.02.04.25321370

  Dataset EGAD50000001622

• HeLa S3 (CCL-2.2) HiC Sequencing

  HiC chromatin conformation sequencing was performed for the purpose of chromosome fusion and structural rearrangement identification.

  Study phs000665

• Data Access Committee for Translational analyses from a phase II study of pembrolizumab and epigenetic modification with azacitidine in platinum-resistant epithelial ovarian cancer

  This DAC is responsible for request review and handling related to the study above.

  Dac EGAC50000000697

• Liver Regulatory Genomics Data Access Committee

  Data access committee for genomic datasets generated for human liver tissue and human primary liver cells by the Regulatory Genomics of Metabolic Disease group (Imperial College London, Section of Genetics and Genomics).

  Dac EGAC50000000715

• DAC for "Drug Development against Tumor Microtube Networks in Glioblastoma"

  This is the DAC for the study "Drug Development against Tumor Microtube Networks in Glioblastoma" of Dirk C. Hoffmann, Dirk.Hoffmann@dkfz.de and Frank Winkler, Frank.Winkler@med.uni-heidelberg.de

  Dac EGAC50000000326

• single cell transcriptomic analysis of tumor samples collected from 5 patients with EMM

  Contains the raw single cell RNAseq data used for the study

  Study EGAS50000000034

• TENX064

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX064 1 samples; filetype=fastq

  Dataset EGAD00001006479

• SCRNA10X_SA_CHIP0163_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_002 1 samples; filetype=fastq

  Dataset EGAD00001006474

• SCRNA10X_SA_CHIP0142_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0142_001 1 samples; filetype=fastq

  Dataset EGAD00001006464

• Normal sample for patient SA224

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA224

  Dataset EGAD00001009548

• Tumour sample for patient SA720

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA720

  Dataset EGAD00001009585

• Exome and Transcriptome for gynecologic cancer gene-drug analysis

  Total of 180 gynecologic tumor specimens were subjected for targeted-exome and/or whole-transcriptome sequencing.

  Dataset EGAD00001005331

• Tumour sample for patient SA601

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA601

  Dataset EGAD00001009205

• Tumour sample for patient SA588

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA588

  Dataset EGAD00001009194

• Tumour sample for patient SA590

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA590

  Dataset EGAD00001009196

• Tumour sample for patient SA654

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA654

  Dataset EGAD00001009206

• Normal sample for patient SA604

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA604

  Dataset EGAD00001009217

• Normal sample for patient SA398

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA398

  Dataset EGAD00001009231

• Normal sample for patient SA221

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA221

  Dataset EGAD00001009362

• Tumour sample for patient SA425

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA425

  Dataset EGAD00001009356

• Tumour sample for patient SA234

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA234

  Dataset EGAD00001009525

• Tumour sample for patient SA226

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA226

  Dataset EGAD00001009532

• Tumour sample for patient SA231

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA231

  Dataset EGAD00001009535

• Normal sample for patient SA591

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA591

  Dataset EGAD00001009241

• Exome and Transcriptome for gastric cancer gene-drug analysis

  Newly generated 52 gastric tumor specimens were subjected for targeted-exome and/or whole-transcriptome sequencing

  Dataset EGAD00001005766

• Tumour sample for patient SA591

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA591

  Dataset EGAD00001009197

• McGill EMC Release 4 in tissue "venous blood" for cell type "B cell"

  McGill EMC Release 4 in tissue "venous blood" for cell type "B cell"

  Dataset EGAD00001001278

• SCRNA10X_SA_CHIP0149_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0149_001 1 samples; filetype=fastq

  Dataset EGAD00001006467

• SCRNA10X_SA_CHIP0080_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0080_002 1 samples; filetype=fastq

  Dataset EGAD00001006462

• TENX066

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX066 1 samples; filetype=fastq

  Dataset EGAD00001006481

• SCRNA10X_SA_CHIP0163_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_001 1 samples; filetype=fastq

  Dataset EGAD00001006473

• SCRNA10X_SA_CHIP0063_000

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0063_000 1 samples; filetype=fastq

  Dataset EGAD00001006478

• SCRNA10X_SA_CHIP0152_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0152_002 1 samples; filetype=fastq

  Dataset EGAD00001006472

• SCRNA10X_SA_CHIP0172_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_001 1 samples; filetype=fastq

  Dataset EGAD00001006475

• TENX062

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX062 1 samples; filetype=fastq

  Dataset EGAD00001006477

• TENX065

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX065 1 samples; filetype=fastq

  Dataset EGAD00001006480

• Tumour sample for patient SA676

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA676

  Dataset EGAD00001009343

• WXS files for Mullighan Leventaki ALCL WXS

  WXS files for Mullighan Leventaki ALCL paper titled "Integrative molecular analysis of pediatric Anaplastic large cell lymphoma reveals subtypes with distinct immune suppression signatures."

  Dataset EGAD00001005936

• Tumour sample for patient SA1028

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009614

• Tumour sample for patient SA600

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA600

  Dataset EGAD00001009204

• SCRNA10X_SA_CHIP0172_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_002 1 samples; filetype=fastq

  Dataset EGAD00001006476

• Normal sample for patient SA495

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA495

  Dataset EGAD00001009372

• DATA FILES FOR MULLIGHAN MEF2D RNASEQ STRANDED

  DATA FILES FOR MULLIGHAN MEF2D RNASEQ STRANDED

  Dataset EGAD00001002692

• Tumour sample for patient SA668

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA668

  Dataset EGAD00001009211

• Normal sample for patient SA1026

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009587

• Tumour sample for patient SA101

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA101

  Dataset EGAD00001009527

• Tumour sample for patient SA277

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA277

  Dataset EGAD00001009539

• Normal sample for patient SA237

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA237

  Dataset EGAD00001009558

• Normal sample for patient SA576

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009596

• Normal sample for patient SA530

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA530

  Dataset EGAD00001009234

• Tumour sample for patient SA276

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA276

  Dataset EGAD00001009182

• Tumour sample for patient SA283

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA283

  Dataset EGAD00001009184

• Tumour sample for patient SA655

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA655

  Dataset EGAD00001009207

• Tumour sample for patient SA592

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA592

  Dataset EGAD00001009198

• Normal sample for patient SA402

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA402

  Dataset EGAD00001009232

• Tumour sample for patient SA595

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA595

  Dataset EGAD00001009200

• Tumour sample for patient SA599

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA599

  Dataset EGAD00001009203

• Normal sample for patient SA535

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA535

  Dataset EGAD00001009235

• Normal sample for patient SA665

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA665

  Dataset EGAD00001009252

• Normal sample for patient SA404

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA404

  Dataset EGAD00001009233

• Tumour sample for patient SA279

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA279

  Dataset EGAD00001009183

• Tumour sample for patient SA398

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA398

  Dataset EGAD00001009188

• Tumour sample for patient SA593

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA593

  Dataset EGAD00001009199

• Normal sample for patient SA609

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA609

  Dataset EGAD00001009219

• Normal sample for patient SA605

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA605

  Dataset EGAD00001009218

• Normal sample for patient SA595

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA595

  Dataset EGAD00001009244

• Normal sample for patient SA599

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA599

  Dataset EGAD00001009247

• Normal sample for patient SA655

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA655

  Dataset EGAD00001009251

• Tumour sample for patient SA609

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA609

  Dataset EGAD00001009262