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• Dual targeting of polyamine synthesis and uptake in diffuse intrinsic pontine gliomas

  Bulk RNA-seq data of tumour data.

  Dataset EGAD00001006794

• MutWP5: CRUK Mutographs of Cancer: Cancer Mastectomy (WG)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 40 women who underwent mastecomies due to breast cancer. LCM and sequencing will be conducted on both normal, unaffected breast, and, where possible, tumour tissue. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue, and compare findings between the normal and associated cancer tissues. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those who are BRCA carriers. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010114

• Irish Covid19 RNASeq Alignment Data files

  The Dataset contains 446 Covid19 patient's RNASeq Alignment files in BAM format for both genomic and transcriptomic alignment.

  Dataset EGAD00001010099

• Covid19 Transcriptomic Data analysis in Irish Population

  The Samples were collected from Saint James's Hospital, Dublin and after informed consent. RNA-Sequencing library preparation was performed using the Illumina TruSeq Stranded Total RNA Library Prep Globin kit (20020612) with an input amount of 100 – 1000 ng. Library preparation was automated and processed using a Hamilton NGS Star. Library quality was assessed using the Roche KAPA Library Quantification Kit (7960298001) and GX Caliper HS Assay (CLS760672, 760517), run on Roche Lightcycler 480 II and Perkin Elmer LabChip GX Touch analyser respectively. Libraries were pooled and sequenced (75bp PE) on an Illumina NovaSeq 6000 instrument using NovaSeq 6000 S2 Reagent Kit v1.5 (20028314) targeting 50M paired reads. Raw data (BCL format) were demultiplexed and converted to FASTQ format using BCL2FastQ (Illumina). Adapters were trimmed using Skewer (PMID: 24925680) and QC assessed using FASTQC. STAR (PMID: 23104886) was used to align reads to the reference genome (GRCh38/hg38) as well as to the transcriptome (GENCODE v. 25). Quality of the RNA alignment was assessed using Picard QC. Gene and isoform quantification was performed using RSEM (PMID: 21816040). The study shares expression data (gene.results) and chimeric.junction.out.

  Study EGAS00001007116

• Targeted and Whole Exome Sequencing for Validation of PGDx elio tissue complete

  This dataset consists of 116 tumor and normal samples analyzed with whole exome sequencing on the HiSeq2500 instruments with 100bp paired-end reads as well as 760 tumor and normal samples analyzed with the PGDx elio tissue complete assay. The PGDx elio tissue complete assay is a hybrid capture approach targeting 500+ genes with sequencing on the NextSeq instruments with 150bp paired-end reads. The bam files provided have been adapter masked and contain duplicate reads.

  Dataset EGAD00001008099

• RNA-Seq of 584 DLBCL Cases of Non-China Cohort from Phoenix Clinical Trial

  Standard RNA-Seq datasets. Check the associated paper for more details.

  Dataset EGAD00001008131

• Covid19 WGS variants analysis

  483 samples were collected for WGS and aligned with GRCh38 human genome. The variants were called using GATK (Sentieon v. 201808.03) in GVCF and VCF format.

  Dataset EGAD00001010138

• Intratumoural Heterogeneity Underlies Distinct Therapy Responses and Treatment Resistance in Glioblastoma

  Glioblastomas are the most common and lethal neoplasms of the central nervous system. Neighbouring glioma cells maintain extreme degrees of genetic and phenotypic variation that forms intratumoural heterogeneity. This genetic diversity allows the most adaptive tumour clones to show treatment resistance and conserve the tumour bulk. We aimed to model this phenomenon and test the effectiveness of several targeted therapeutic interventions to overcome therapy resistance. Heterogenic tumour masses were first deconstructed into single tumour cells, which were expanded independently as single-cell clones. SNP arrays, whole-genome and RNA sequencing, and CpG methylation analysis validated the unique molecular profile of each tumour clone, which displayed distinct pathologic features, including cell morphology, growth rate, and resistance to temozolomide and ionizing radiation. We also identified variable sensitivities to AURK, CDK, and EGFR inhibitors, consistent with the heterogenic molecular alterations that each clone harboured. These targeted therapies effectively eliminated the temozolomide- and/or irradiation-resistant clones and also parental polyclonal cells. Our findings indicate that polyclonal tumours create a dynamic environment that consists of diverse tumour elements and treatment responses. Designing targeted therapies based on a range of molecular profiles can be a more effective strategy to eradicate treatment resistance, recurrence, and metastasis.

  Study EGAS00001003438

• Genetics causes of male infertility in 185 patient-parent trios from Netherlands and UK

  The dataset is composed by the raw and processed sequencing data generated from 185 Patients affected by azoospermia or severe oligozoospermia recruited from the Netherlands and the UK.

  Dataset EGAD00001007862

• The Dutch Microbiome Project Batch 2

  The Dutch Microbiome Project (DMP) data includes shotgun metagenomic sequencing of faecal samples 8,208 Dutch individuals. Paired-end sequencing was performed using Illumina HiSeq 2000 platform. Data is archived in two batches to facilitate easier data access and upload to EGA. Batch 2 of DMP includes ~400 samples.

  Dataset EGAD00001007501

• EPICC: Evolutionary Predictions in Colorectal Cancer

  ATAC-seq profiling bam files from colorectal carcinoma and adenoma.

  Dataset EGAD00001008140

• NPC genome-wide human SNP array data

  nasopharyngeal carcinoma genome-wide human SNP array data for 4083 NPC cases and 4811 controls

  Dataset EGAD00010002296

• Swarm Learning to identify COVID-19, tuberculosis and leukemia patients based on blood transcriptomes

  Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis or COVID-19 is an important goal of modern precision medicine. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. We have recently illustrated that classical machine learning can identify leukemia patients based on their blood transcriptomes. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, artificial intelligence (AI), blockchain and privacy protection without the need for a central coordinator thereby going beyond federated learning. To illustrate its feasibility, using more than 12,000 transcriptomes from peripheral blood mononuclear cells and more than 2,000 peripheral blood transcriptomes we demonstrate that SL of omics data distributed across different individual sites leads to disease classifiers that outperform those developed at individual sites. Yet, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.

  Dataset EGAD00001006231

• RNAseq data for Failure of Differentiation of the Rhombic Lip Constitutes Medulloblastoma

  We used single-cell transcriptomics to study cells from the developing human cerebellum, and show that different molecular subgroups of medulloblastoma resemble distinct glutamatergic progenitors.

  Dataset EGAD00001008458

• Myeloma Genome Project Targeted Panel Validation dataset

  372 samples consisting of 185 patient paired CD138+ tumor and non-involved DNA pairs, plus 5 Horizon Diagnostic known mutation standards (HD). Samples were processed using the KAPA HyperCap protocol and hybridized onto a targeted panel for multiple myeloma and associated diseases. Reference Sudha et al Clinical Cancer Research, 2022.

  Dataset EGAD00001008689

• Irish Covid19 WGS Alignment Data

  Dataset contains 483 Irish origin Individuals with Covid19. For WGS, Alignment has been done using BWA-mem (sention v 201808.03 ) and BAM file generated.

  Dataset EGAD00001010098

• Next Generation Children project

  Whole genome sequencing of sick children in neonatal and paediatric intensive care units, aligned to reference assembly GRCh38.

  Dataset EGAD00001007868

• Single cell RNA sequencing of synovial B cells in early Rheumatoid Arthritis

  This dataset includes 3432 paired single cell sequencing fastq files derived from synovial B cells of 5 early Rheumatoid Arthritis patients. Libraries were prepared using the SmartSeq2 protocol. All wells contained ERCC spike-ins. Libraries were sequenced on an Illumina NovaSeq instrument with 2x100bp paired-end reads yielding a median of 2.5M reads/well. Sample aliases ending on 368 and 384 represent empty control wells. Sample aliases starting with P11417_4 and P13157_1 belong to ACPA- patient A7, sample aliases starting with P11417_5 and P11417_6 belong to ACPA+ patient A1, sample aliases starting with P13157_2 belong to ACPA- patient A3, sample aliases starting with P13157_3 and P13157_6 belong to ACPA+ patient A2, sample aliases starting with P13157_4 and P13157_5 belong to ACPA- patient A4.

  Dataset EGAD00001009076

• EPICC: Evolutionary Predictions in Colorectal Cancer WGS-3

  WGS profiling bam files from colorectal carcinoma and adenoma.

  Dataset EGAD00001008642

• ICGC Data Access Compliance Office

  To access Controlled Access ICGC data at the EGA you must first apply for access on the DACO form. Instructions on this process is available from this web site: http://docs.icgc.org/portal/access/

  Dac EGAC00001000010

• GCAT| WGS FASTQ V1

  This dataset include FASTQ files of 808 samples from GCAT cohort. Technology used HiSeq 4000, read length 150 bp, inner mate distance 300 bp. For each sample the paired -ends are generated in separated files. Each FASTQ is splitted in multiple LANEs and grouped by the Multiplex index.

  Dataset EGAD00001008201

• Blueprint Data Access Committee.

  Access to the sequence and alignment and genotype level data produced by the Blueprint Consortium is controlled by the Blueprint Data Access Committee (DAC). Access to data will be granted to qualified investigators for appropriate use. Please email blueprint-dac@ebi.ac.uk to request the Data Access Application and Data Access Agreement.

  Dac EGAC00001000135

• Dataset for Sarcoma RNA sequencing linked from study EGAS00001004813

  Sequencing data of 410 sarcoma tumor runs, which were uploaded to EGAS00001004813. The sequencing was always paired.

  Dataset EGAD00001010276

• Solve-RD_RND_cohort-1_DF1+2_V1

  Solve-RD data submitted to the ERN-RND cohort for re-analysis (Data freeze 1+2) v1

  Dataset EGAD00001009769

• Ancient Yakuts genomes and metagenomes

  Read data and bam alignments for 124 ancient yakuts

  Dataset EGAD50000001903