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• Functional genomics approaches to understand osteoarthritis (2019-08-01)

  The aim of this work is to apply an integrated systems approach to understand the biological underpinnings of large joint (hip and knee) osteoarthritis which culminates in the need for total joint replacement (TJR). We will obtain diseased and non-diseased cartilage as well as other disease-relevant tissue following TJR, coupled with a blood sample. We will generate genotype data and will characterise the pairs of diseased and non-diseased tissue samples in terms of methylation, transcription (RNASeq) and expression (quantitative proteomics). We will apply integrative approaches to combine information across the omics levels to characterise genes, pathways, and networks that underlie osteoarthritis progression. 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 2019-08-01.

  Dataset EGAD00001005215

• Whole-genome sequences of single-cell derived clonal samples and bulk blood samples from human

  Human single cells were clonally expanded by culture and whole-genome sequenced. This dataset includes 334 clonal samples and 7 blood bulks from seven individuals (DB2, DB3, DB5, DB6, DB8, DB9, DB10). We extracted genomic DNA materials from clonally expanded cells and matched peripheral blood using DNeasy Blood and Tissue kits (Qiagen) according to the protocol. DNA libraries for WGS were generated by an Accel-NGS 2S Plus DNA Library Kit (Swift Biosciences) from 1 µg of genomic DNA materials. WGS was performed on either the Illumina HiSeq X platform or the NovaSeq 6000 platform to generate mean coverage of 25.2X for 374 clonally expanded cells and 94.8X for 7 matched blood tissues.

  Dataset EGAD00001007032

• PELICAN33 Phenomic Dataset

  To better understand variation in metastatic prostate cancer behaviour, we assembled and analyzed longitudinal clinical and autopsy records in 33 men. The dataset is contained in a self-explanatory Excel Workbook, with each patient identified as A1, A2, etc. as listed in the "Combined longitudinal clinical and autopsy phenomic assessment in lethal metastatic prostate cancer: recommendations for advancing precision medicine" publication in European Urology Open Science. Please see Jasu J, Tolonen T, Antonarakis ES, Beltran H, Halabi S, Eisenberger MA, Carducci MA, Loriot Y, Van der Eecken K, Lolkema M, Ryan CJ, Taavitsainen S, Gillessen S, Högnäs G, Talvitie T, Taylor RJ, Koskenalho A, Ost P, Murtola TJ, Rinta-Kiikka I, Tammela T, Auvinen A, Kujala P, Smith TJ, Kellokumpu-Lehtinen PL, Isaacs WB, Nykter M, Kesseli J, Bova GS. Combined Longitudinal Clinical and Autopsy Phenomic Assessment in Lethal Metastatic Prostate Cancer: Recommendations for Advancing Precision Medicine. Eur Urol Open Sci. 2021 Jul 2;30:47-62. doi: 10.1016/j.euros.2021.05.011. PMID: 34337548; PMCID: PMC8317817. for more details.

  Dataset EGAD00001007800

• Bulk-tissue paired-end RNA-sequencing of anterior cingulate cortex samples derived from Lewy body disease patients

  Profiling of 24 human anterior cingulate cortex samples by bulk-tissue RNA-sequencing. Samples were derived from 5 non-neurological control individuals and 19 individuals with Lewy body disease (Parkinson’s disease = 7 individuals; Parkinson’s disease with dementia = 6 individuals; dementia with Lewy bodies = 6 individuals). Paired-end FASTQ files for each of the human samples are provided and are denoted by the suffixes R1 (read 1) and R3 (read 2). Fastp (v 0.20.0), a fast all-in-one FASTQ pre-processor, was used for adapter trimming, read filtering and base correction. Fastp default settings were used for quality filtering and base correction. Further details on parameters used are available here: https://github.com/RHReynolds/RNAseqProcessing/blob/master/QC/prealignmentQC_fastp_PEadapters.R.

  Dataset EGAD00001007698

• Rare Disease Synthetic Dataset

  The purpose of this project is to provide public human datasets for the study of rare diseases. The use of public human genomic background combined with the in-silico insertion of real disease-causing variants enable to have a representative dataset for testing purposes without facing ethical and legal issues associated with the use of human sensitive data. This project aims to help development of technical implementations for rare disease data integration, analysis, discovery, and federated access.

  Dataset EGAD00001008392

• Postmortem Single Nuclei and Bulk RNA-seq data of the Motor Cortex and Spinal Cord for Healthy, C9ALS and sALS Patients

  This dataset contains both snRNA-seq and bulk RNA-seq data from 19 different patients, comprising of 9 healthy controls (4 Spinal Cord, 5 Motor Cortex) as well as 5 C9ALS patients and 5 sporadic ALS patients, each with paired data from the spinal cord and motor cortex. For the snRNA-seq data, fastq files containing the raw reads are provided, many of these samples were pooled for sequencing and subsequently require demultiplexing using SNPs using a tool such as freemuxlet. The paired bulk RNA-seq data (raw fastq files) can be used to acquire the ground truth per patient - a list of pooled samples can be found in map.txt. snRNA-seq data was produced using the 10X Genomics 3' v3 kit and bulk RNA-seq was produced using the Illumina TruSeq v2 kit.

  Dataset EGAD00001009686

• RNA sequencing of end-stage kidney disease patients with COVID-19

  This dataset contains paired RNA sequencing data for end-stage kidney disease (ESKD) patients on dialysis. There are two cohorts. The first includes 179 samples from 51 COVID-19 patients recruited during the initial phase of the COVID-19 pandemic (April-May 2020) and 55 non-infected ESKD patients as controls. 17 patients initially recruited as controls as part of the Wave 1 cohort were later infected with COVID-19 in January-March 2021. We acquired a total of 90 samples during the acute infection and convalescent samples for 12 of the 17 patients following the acute COVID-19 episode. RNA-seq counts and full clinical metadata for these cohorts are available without restriction from Zenodo (https://doi.org/10.5281/zenodo.6497251).

  Dataset EGAD00001009752

• Platelets contain a repertoire of DNA fragments that map over the human nuclear genome, including tumour-derived DNA in patients with active malignancy.

  This dataset consists of WGS from six patients with a recent diagnosis of gastrointestinal adenocarcinoma. DNA-Seq libraries were prepared from platelet DNA (pDNA) and plasma cfDNA obtained from the same peripheral blood sample. pDNA was size selected into 2 groups (small, s-pDNA: less than 600 bp and long, b-pDNA: greater than 600 bp). The short fragments were further cleaned to remove <100 bp fragments and large platelet fragments were fragmented via sonication. Libraries were then prepared using the NEBNext Ultra II DNA Sample Preparation Kit for Illumina according to the manufacturer’s protocol and sequenced on an Illumina NextSeq 500 (300 cycle PE) at low-pass (0.1X) for all samples, and 10X for the cfDNA and s-pDNA, using four lanes in each sample.

  Dataset EGAD00001009856

• CCA ChIP-seq data (63 CCA, 8 normal, 19 cell-line)

  Fastq files for H3K27ac ChIP-seq (with matched input-DNA control) for 63 CCAs, 8 normal bile duct tissues, 16 CCA cell-lines (including replicates), and 3 normal cholangiocyte cell-lines (including replicates). Library prep was performed with the NEBNext ChIP-seq library preparation kit. Each library (including matching input DNA) was sequenced to an average depth of 20 to 30 million raw reads on Illumina HiSeq4000 sequencer, with paired-end sequencing (except for 3 normal bile tissues done with single-end sequencing).

  Dataset EGAD00001012103

• Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease

  A comprehensive clinical and multi-omic profiling for 199 patients with acute coronary syndrome (ACS), an acute subcategory of CAD, that we recruited in two major Israeli hospitals. We demonstrate that ACS has distinct serum metabolome and gut microbial signatures, as compared to a control cohort. Metabolic aberrations linked with microbiome and diet show a gradual trend with significant metabolite deviations in control participants with metabolic impairment suggesting their involvement in earlier dysmetabolic phases preceding clinically overt CAD.

  Study EGAS00001005342