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• Stitched BAM files for manuscript titled Discrepancies in Tumour Mutation Burden (TMB) reporting from sequential Endobronchial ultrasound trans bronchial needle aspiration (EBUS TBNA) samples within single lymph node stations for SNP and INDEL Variant Calling.

  Stitched BAM files for manuscript titled Discrepancies in Tumour Mutation Burden (TMB) reporting from sequential Endobronchial ultrasound trans bronchial needle aspiration (EBUS TBNA) samples within single lymph node stations for SNP and INDEL Variant Calling.

  Dataset EGAD00001012639

• Glioblastoma CRISPR Screen (2017-04-27)

  This is an in vitro genome-wide CRISPR/cas9 screen in human glioblastoma stem cells, screening for genes essential for survival of these cells. These cells express cas9 and have been transfected with a guide RNA library causing gene knockouts. We will analyse the sequencing data for depletion of guide RNAs. This dataset contains all the data available for this study on 2017-04-27.

  Dataset EGAD00001003308

• Human tumor scMultiome

  We profile 10 high-grade gliomas patient brain tumor samples by single-cell multiome ATAC + gene expression, using the 10X Chromium technology. 3 sets of fastq are provided for each samples: R1 and R2 for gene expression, R1 and R2 for ATAC-seq as well as index1 and index2 for ATAC-seq.

  Dataset EGAD00001008349

• Re-aligned BAM files for manuscript titled Discrepancies in Tumour Mutation Burden (TMB) reporting from sequential Endobronchial ultrasound trans bronchial needle aspiration (EBUS TBNA) samples within single lymph node stations for Copy Number Variant Calling.

  Re-aligned BAM files for manuscript titled Discrepancies in Tumour Mutation Burden (TMB) reporting from sequential Endobronchial ultrasound trans bronchial needle aspiration (EBUS TBNA) samples within single lymph node stations for Copy Number Variant Calling.

  Dataset EGAD00001012638

• ChIP-Seq (H3K4me3, H3K4me1, H3K9me3, H3K27ac, H3K27me3, H3K36me3, Input) data for HL60 cell line generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency.

  ChIP-Seq (H3K4me3, H3K4me1, H3K9me3, H3K27ac, H3K27me3, H3K36me3, Input) data for HL60 cell line generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency.

  Dataset EGAD00001002238

• DNA Whole Exome Sequence for manuscript titled: Evaluation of Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration (EBUS-TBNA) Samples from Advanced Non-Small Cell Lung Cancer for Whole Genome, Whole Exome and Comprehensive Panel Sequencing

  DNA Whole Exome Sequence for manuscript titled: Evaluation of Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration (EBUS-TBNA) Samples from Advanced Non-Small Cell Lung Cancer for Whole Genome, Whole Exome and Comprehensive Panel Sequencing

  Dataset EGAD00001015395

• The neo-open reading frame peptides that comprise the tumor framome are a rich source of neoantigens for cancer immunotherapy

  Identification of immunogenic cancer neoantigens as targets for therapy is challenging. Here, we integrate whole genome and long-read transcript sequencing of cancers to identify the collection of neo-open reading frame peptides (NOPs) expressed in tumors. We termed this collection of NOPs the tumor framome. NOPs represent tumor-specific peptides that are different from wild-type proteins and may be strongly immunogenic. We describe a class of hidden NOPs that derive from structural genomic variants involving an upstream protein coding gene driving expression and translation of non-coding regions of the genome downstream of a rearrangement breakpoint, i.e. where no gene annotation or evidence for transcription exists. The entire collection of NOPs represents a vast number of possible neoantigens particularly in tumors with many structural genomic variants and a low number of missense mutations. We show that NOPs are immunogenic and epitopes derived from NOPs can bind to MHC class I molecules. Finally, we provide evidence for the presence of memory T cells specific for hidden NOPs in peripheral blood from a patient with lung cancer. This work highlights NOPs as a major source of possible neoantigens for personalized cancer immunotherapy and provides a rationale for analyzing the complete cancer genome and transcriptome as a basis for detection of NOPs.

  Study EGAS00001006021

• Genome-wide DNA-methylation assessment by MethylCap-seq and Infinium HumanMethylation450 BeadChips: an independent large-scale comparison

  DNA-methylation is an important epigenetic feature in health and disease. Two cost-efficient genome-scale methodologies to assess DNA-methylation are MethylCap-seq and Illumina's Infinium HumanMethylation450 BeadChips (HM450). However, objective information regarding the best-suited methodology for a specific research question is scant. Therefore, we performed a large-scale evaluation on a set of 70 brain tissue samples obtained from 65 glioblastoma and 5 non-tumoral brain tissues, using a gold standard free Bayesian modeling procedure. While conditional specificity was adequate for both approaches, conditional sensitivity was systematically higher for HM450. Also the genome-wide characteristics were compared, revealing that the HM450 probes assess less than 10% of the regions identified as methylated by MethylCap-seq. Hence the latter method may detect more potentially relevant DNA-methylation, defined by either functional location or previously reported differentially methylated candidate regions. Our results therefore indicate that – at least for the tissue under study - both methodologies are complementary, with a higher sensitivity for HM450, but a far larger genome-wide coverage for MethylCap-seq. Note that here only the relevant MethylCap-seq data is deposited, for the HM450 data we refer to GEO (GSE60274).

  Study EGAS00001001191

• Pipeline Olympics: Continuous benchmarking of computational workflows for DNA methylation sequencing data

  DNA methylation is a widely studied epigenetic mark and a powerful biomarker of cell type, age, environmental exposure, and disease. Genome-wide DNA methylation profiling is usually performed using whole-genome sequencing following selective conversion of unmethylated cytosines into thymines via bisulfite treatment or enzymatic methods. Numerous software tools facilitate processing of raw sequencing data for DNA methylation profiling, but a comprehensive benchmarking has been lacking. In this study, we systematically compared methods and tools for processing DNA methylation sequencing data. We established a dedicated benchmarking dataset comprising genome-wide DNA methylation profiling data for four reference samples using five experimental protocols each. The results of data processing were compared to highly quantitative locus-specific DNA methylation measurements available for 46 genomic loci. Based on this gold-standard reference dataset, we identified workflows that consistently demonstrated high performance. We present our results in an interactive application that provides a “living benchmark” ready for continuous updating as new workflows are proposed and require benchmarking against existing methods. In summary, our study provides guidance for any laboratory studying DNA methylation on a genome-wide scale.

  Study EGAS50000000541

• Effective screening strategy for deafness using a new-genetation sequencing platform: a consecutive analysis

  The diagnosis of the genetic etiology of deafness contributes to the clinical management of patients. We performed the following four genetic tests in three stages for 52 consecutive deafness subjects in one facility. We used the Invade assay and Sanger sequencing for the GJB2 gene or SLC26A4 gene in the first stage test, TaqMan genotyping assay in second stage test, and targeted exon sequencing using the massively parallel DNA sequencing in third stage test. Overall, we identified the genetic cause in 40% (21/52) of patients. The diagnostic rates of the first-, second- and third-stage genetic testing were 17%(9/52), 9%(4/43) and 21% (8/39), respectively. The combination approach using these genetic tests appears to be useful as a diagnostic tool for deafness patients. We recommended that genetic testing for the screening of common mutations in deafness genes using the Invader assay or TaqMan genotyping assay be performed as the initial evaluation. For the remaining undiagnosed cases, targeted exon sequencing using MPS is clinically and economically beneficial. This data set was MPS reads data (fastq) for 39 sensorineural hearing loss patients.

  Study JGAS000032

• MGP Panel: a comprehensive targeted genomics panel for molecular profiling of multiple myeloma patients

  We designed a comprehensive multiple myeloma (MM) targeted sequencing panel to identify common genomic abnormalities in a single assay and validated it against known standards. The panel comprised 228 genes/exons for mutations, 6 regions for translocations, and 56 regions for copy number abnormalities (CNAs). Toward panel validation, targeted sequencing was conducted on 233 patient samples and further validated using clinical fluorescence in situ hybridization (FISH) (translocations), multiplex ligation probe analysis (MLPA) (CNAs), whole genome sequencing (WGS) (CNAs, mutations, translocations) or droplet digital PCR (ddPCR) of known standards (mutations). Canonical IgH translocations were detected in 43.2% of patients by sequencing, and aligned with FISH except for one patient. CNAs determined by sequencing and MLPA for 22 regions were comparable in 103 samples and concordance between platforms was R2=0.969. VAFs for 74 mutations were compared between sequencing and ddPCR with concordance of R2=0.9849. In summary, we have developed a targeted sequencing panel that is as robust or superior to FISH and WGS. This molecular panel is cost effective, comprehensive, clinically actionable and can be routinely deployed to assist risk stratification at diagnosis or post-treatment to guide sequencing of therapies.

  Study EGAS00001006222

• DAC for Melanoma Exome Dataset for Identification of Mutated Epitopes

  Dac EGAC00001000546

• Helmholtz Centre for Infection Research, Centre for Individualised Medicine

  Dac EGAC00001002208

• Helmholtz Centre for Infection Research, Centre for Individualised Medicine

  Dac EGAC00001002844

• Helmholtz Centre for Infection Research, Centre for Individualised Medicine

  Dac EGAC00001003086

• Helmholtz Centre for Infection Research, Centre for Individualised Medicine

  Dac EGAC00001003058

• WTCCC case-control study for Autoimmune Thyroid Disease - Combined Controls

  WTCCC genome-wide case-control association study for Autoimmune Thyroid Disease (ATD) - Combined Controls.

  Study EGAS00000000021

• WTCCC case-control study for Ankylosing Spondylitis - Combined Controls

  WTCCC genome-wide case-control association study for Ankylosing Spondylitis (AS) - Combined Controls.

  Study EGAS00000000019

• ICR Centre for Paediatric Experimental Medicine

  This is the DAC for granting access to consented patient data generated in association with the Centre for Paediatric Experimental Medicine at ICR. Data access can be granted by bioinformatician Claire Lynn: claire.lynn@icr.ac.uk

  Dac EGAC50000000362

• Whole Genome Sequencing Reveals Potential Therapeutic Strategy for Monomorphic Epitheliotropic Intestinal T-cell Lymphoma

  Whole Genome Sequencing Reveals Potential Therapeutic Strategy for Monomorphic Epitheliotropic Intestinal T-cell Lymphoma

  Study EGAS00001003876

• Exome sequencing of primary and relapse neuroblastoma

  In this dataset, 16 trios- primary tumor, relapse and corresponding normals- for patients with neuroblastoma are provided. For one patient, more than one relapse was available for the analyses.

  Dataset EGAD00001001607

• Control iPSC lines with clinically annotated genetic variants for versatile multi-lineage differentiation Part 2

  This is 2nd part of data for original Control iPSC lines with clinically annotated genetic variants for versatile multi-lineage differentiation,

  Dataset EGAD00001005359

• Multiplexing cortical brain organoids for the longitudinal dissection of developmental traits at single cell resolution

  Dissecting human neurobiology at high resolution and with mechanistic precision requires a major leap in scalability, given the need for experimental designs to multiple individuals and, prospectively, population cohorts. To lay the foundation for this, we have implemented and benchmarked complementary strategies to multiplex brain organoids by pooling cells from different pluripotent stem cell lines (PSC) either during organoid generation (mosaic models) or before single cell-RNAseq library preparation (downstream multiplexing). We have also developed a new computational method, SCanSNP, and a consensus call to deconvolve cell identities, overcoming current criticalities in doublets prediction and low quality cells identification. We validated both multiplexing methods for charting neurodevelopmental trajectories at high resolution, thus linking specificity to genetic variation. Finally, we modelled their scalability across different multiplexing combinations and showed that mosaic organoids represent an enabling method for high-throughput settings. Together, this multiplexing suite of experimental and computational methods provides a highly scalable resource for disease modelling.

  Study EGAS50000000698

• Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma (arrays set)

  Multiple myeloma (MM) patients undergo repetitive bone marrow (BM) aspirates for genetic characterization. Circulating tumor cells (CTCs) are detectable in peripheral blood of virtually all MM cases and are prognostic, but their applicability for non-invasive screening has been poorly investigated. Here, we used next-generation flow cytometry to isolate matched CTCs and BM tumor cells from 42 patients and compare their genetic profile. In 8 cases, tumor cells from extramedullary plasmacytomas were also sorted and whole-exome sequencing performed in the three spatially-distributed clones. Noteworthy, ≥84% mutations present in BM and extramedullary clones were detectable in CTCs. Concordance between BM tumor cells and CTCs was high for chromosome arm-level copy-number alterations (≥94%) though not for translocations (43%). All high-risk genetic abnormalities except one t(4;14) were detected in CTCs whenever present in BM tumor cells. Altogether, these results support CTCs for non-invasive risk-stratification of MM patients based on their numbers and genetic profile.

  Study EGAS00001004314

• Data Use Ontology (DUO) at EGA

  For those of you that follow the updates of GA4GH you will have seen the unanimous approval of the steering committee for DUO to be included in its suite of technical standards for the sharing of genomic and health related data. DUO has three main features: Each term has been generated by the community and includes a human readable definition that can be expanded where necessary. It is a machine-readable file that encodes how that data can be used and how a researcher intends to use the data. Can be implemented alongside an advanced search algorithm which would allow authenticated users to query and gain access to datasets pertaining to their research. e.g., an industry researcher working on cancer could potentially be matched to any dataset that is allowed for commercial use and for cancer research and offered the opportunity to fetch them automatically. So, what does this mean for EGA? EGA as a driver project has adopted this standard and will be utilizing it for two main purposes in the first instance. i) It will allow EGA users to instantly identify what terms they will need to agree to as part of any Data Access Agreements. This will save valuable time for both applicants and Data Access Committees alike - as you will be able to see if you are likely to be able to access the data based on your research intentions and working background. ii) In the future it will allow users to be able to search for data based on Data Use Ontology terms e.g., you could search for all data that can be used for General Research Use (GRU). As EGA moves forwards submitters will be asked to align their data access policies with DUO so that their dataset(s) can be tagged appropriately on our web pages. If you would like to add DUO to any of your existing datasets, or to any future datasets, do please just let us know.

  Blog data-use-ontology