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• WES fastq files of IPDGC UK cohort

  As part of the International Parkinson's Disease Genomics Consortium, exomes of Parkinson's disease (PD) patients and healthy controls were sequenced to study the genetic etiology of PD. This UK cohort consists of 70 PD patients. Researchers can apply for access to fastq files for this cohort.

  Dataset EGAD00001003096

• Mapping of runs back to samples for snRNASeq data

  Mapping of runs back to samples for PDN, PDO and TIS snRNASeq data. 4 sets of matched PDN, PDO and TIS samples (12 unique samples total) underwent pooled snRNASeq (Parse Biosciences). 2 sublibraries were created from this and sequenced at separate times

  Dataset EGAD00001015609

• 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

• Searching for DNA methylation sites associated with panic disorder

  This study aimed to identify whole-genome DNA methylation profile of panic disoeder and to search DNA methylation sites associated with the disease

  Study JGAS000111

• Meta-Analysis of Genome-Wide Association Studies of Bladder Cancer Risk

  This meta-analysis consists of genome-wide genotype for 13,447 cases and 342,580 controls of European ancestry to identify novel susceptibility loci for bladder cancer. Cases were defined as histologically confirmed primary carcinoma of the urinary bladder of all stages, including carcinoma in situ (International Classification of Diseases for Oncology, C670-C679, 188). All histologic subtypes were included. Summary-level results for the association between genotyped/imputed variants and risk of bladder cancer are included.

  Study phs003342

• Inference_of_B_cell_clonality_and_function_from_single_cell_RNA_seq_data

  cDNA libraries for RNA-seq will be prepared for sequencing. cDNA will be created following the Smart-Seq2 protocol, and a part of the cDNA from each cell will be used for preparation of RNA-seq libraries using the Nextera XT kit. The other part of the cDNA will be used as template for verification of the computationally recovered immunoglobulin sequences from the RNA-seq dataset by using a targeted PCR approach to generate a multiplexed sequencing library

  Study EGAS00001002963

• PCA Atlas Visium spatial transcriptomics data (FASTQs, spaceranger BAMs and Visium mapping tables)

  This dataset contains Visium spatial transcriptomics data for the PCA Atlas study EGAS00001008332. It includes: - Raw Visium FASTQ runs (EGAR00004172272–EGAR00004172283) for all libraries. - spaceranger alignment BAM analyses for those libraries where BAM output was generated. - visium.csv: a slide-level mapping table linking Visium slide samples to EGA sample accessions, sequencing runs/experiments and spaceranger BAM analyses. - visium_tma.csv: a TMA mapping table describing how mini-TMA slides relate slide-level Visium libraries to tissue-level samples/cores. Together these objects provide the raw and aligned Visium data and the technical/biological mappings needed to understand and reuse the spatial transcriptomics component of the PCA Atlas. High resolution histology images are available from the following Zenodo repository, along with additional spaceranger outs for each slide: - https://zenodo.org/records/17411292

  Dataset EGAD00001015796

• Genomic-Enabled Medicine for Recurrent Glioblastoma

  In this prospective study, we performed genome-wide tumor/normal exome sequencing and tumor RNA-sequencing for recurrent glioblastoma patients. A subset of patients had both enhancing tumor and non-enhancing tissue sequenced to investigate spatial heterogeneity of this disease. Together, this information builds our understanding of the genomic underpinnings of glioblastoma, and contributes toward the knowledge base for identifying and developing more effective treatments for patients with glioblastoma.

  Study phs001460

• Prevalence and Clinical Characteristics of hearing loss caused by MYH14 mutation

  Mutations in MYH14 are reported to cause autosomal dominant nonsyndromic hereditary hearing loss (ADNSHL), and 35 variants have been reported to cause hearing loss in various ethnic groups, but the information on prevalence as well as clinical features are fragmented. In this study, genetic screening for MYH14 variants was carried out using a large series of Japanese hearing loss patients to reveal the information. Massively parallel DNA sequencing of 68 target candidate genes was utilized in 8074 (including 1336 ADNSHL) unrelated Japanese hearing loss patients (probands) to identify genomic variations responsible for hearing loss. We identified 20 patients with 14 mutations from 15 pedigrees. The prevalence was proved to be 0.19% (15/8074) among hearing loss patients, and 1.12% (15/1336) among ADNSHL patients. The 12 mutations out of 14 mutations identified were novel. The patients typically showed late onset, slowly progressive modelate flat-type hearing loss. The clinical information revealed by the present study would contribute to further diagnosis and management of MYH14-associated hearing loss.

  Study JGAS000323

• Pan Prostate Cancer Group data

  The Pan Prostate Cancer Group intends to provide breakthrough advances from the analysis of a very large series of Whole Genome DNA data, contributed by many of the leading scientists and clinicians working in prostate cancer genomics generating Whole Genome DNA Sequence (WGS) data from prostate cancer patients. The accumulated data, collected and compared in a common format including common storage, re-analysis through a single pipeline, and investigation to achieve a variety of scientific goals, are presented in this series. This project will provide a global framework for the future analysis of even larger series of genomes as costs of DNA sequencing, storage, and sequence analysis decrease. In addition to these WGS data from different clinical categories, ethnicities, levels of aggressiveness, and disease managed by different treatments with information linked to long-term clinical follow up, we intend to add additional layers of data, including transcriptome and methylation data. The current data series focuses primarily on the analysis of WGS data. For further information, please refer to https://panprostate.org/, and The Pan Prostate Cancer Group Data Sharing Policy, EGAP00001003712.

  Study EGAS00001002876