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• Human genomic and phenotypic synthetic data for the study of rare diseases

  The purpose of this dataset is to facilitate development of technical implementations for rare disease data integration, analysis, discovery, and federated access. This synthetic dataset includes clinical and genomic data from 6 rare disease cases. It consists of 18 whole genomes (6 index cases with their parents) which have genetic background based on public human data sequenced in the context of the Illumina Platinum initiative (Eberle, MA et al. (2017)) and made available by the HapMap project (https://www.genome.gov/10001688/international-hapmap-project). In each of the cases, real causative variants correlating with the phenotypic data provided were spiked-in. The cases included in this synthetic dataset correspond to the following type of disorders: CASE 1- Congenital myasthenic syndrome (Autosomal Dominant -de novo variant) CASE 2- Macular dystrophy (Autosomal Dominant) CASE 3- Muscular dystrophy (Autosomal Recessive-compound heterozygous variants) CASE 4- Mitochondrial disorder (Autosomal Recessive-consanguineous case - homozygous variant) CASE 5- Breast cancer (Autosomal Dominant) CASE 6- Similar as case 1 for patient matchmaking tests: Congenital myasthenic syndrome (Autosomal Dominant-de novo variant) For each case you will be able to download the following data: clinical information (phenopackets per individual and pedigree per family), raw genomic data (FASTQ and BAMs) and processed genomic data (vcfs). When using the data, the following should be acknowledged: the RD-Connect GPAP (https://platform.rd-connect.eu/), EC H2020 project EJP-RD (grant # 825575), EC H2020 project B1MG (grant # 951724) and Generalitat de Catalunya VEIS project (grant # 001-P-001647).

  Study EGAS00001005702

• Open_Targets_020_Epigenomes_of_Cell_Lines

  There is currently a drive to establish cell based assay systems of greater human biological and disease relevance through the use of well characterised transformed cell lines, primary cells and complex cellular models (e.g. co-culture, 3D models). However, although the field is gaining valuable experience in running more non-standard & complex cell assays for target validation and compound pharmacology studies, there is the lack of a systematic approach to determine if this expansion in cell assay models is reflected in increased human biological and disease relevance. The increasing wealth of publically available transcriptomic, and epigenome (ENCODE and Epigenome Roadmap) data represents an ideal reference mechanism for determining the relationship between cell types used for target & compound studies to primary human cells and tissues from both healthy volunteers & patients. The CTTV020 epigenomes of cell line project aims to generate epigenetic and transcriptomic profiles of cell lines and compare these with existing and newly generated reference data sets from human tissue and cell types. The aim is to identify assay systems which will provide greater confidence in translating target biology and compound pharmacology to patients. Multiple cell types commonly used within research have been grouped according to biology. Examples include erythroid, lung epithelial, hepatocyte cell types and immortalised models of monocyte / macrophage biology. 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/

  Study EGAS00001003137

• Open_Targets_020_Epigenomes_of_Cell_Lines

  There is currently a drive to establish cell based assay systems of greater human biological and disease relevance through the use of well characterised transformed cell lines, primary cells and complex cellular models (e.g. co-culture, 3D models). However, although the field is gaining valuable experience in running more non-standard & complex cell assays for target validation and compound pharmacology studies, there is the lack of a systematic approach to determine if this expansion in cell assay models is reflected in increased human biological and disease relevance. The increasing wealth of publically available transcriptomic, and epigenome (ENCODE and Epigenome Roadmap) data represents an ideal reference mechanism for determining the relationship between cell types used for target & compound studies to primary human cells and tissues from both healthy volunteers & patients. The CTTV020 epigenomes of cell line project aims to generate epigenetic and transcriptomic profiles of cell lines and compare these with existing and newly generated reference data sets from human tissue and cell types. The aim is to identify assay systems which will provide greater confidence in translating target biology and compound pharmacology to patients. Multiple cell types commonly used within research have been grouped according to biology. Examples include erythroid, lung epithelial, hepatocyte cell types and immortalised models of monocyte / macrophage biology. 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/

  Study EGAS00001003139

• Transcriptomic intra-tumor heterogeneity of colorectal cancer varies depending on tumor location within the colorectum

  Background; Intra-tumor heterogeneity (ITH) of colorectal cancer (CRC) complicates molecular tumor classification, such as transcriptional subtyping. Differences in cellular states, biopsy cell composition, and tumor microenvironment may all lead to ITH. Here we analyze ITH at the transcriptomic and proteomic levels to ascertain whether subtype discordance between multiregional biopsies reflects relevant biological ITH or lack of classifier robustness. Further, we study the impact of tumor location on ITH.MethodsMultiregional biopsies from stage II and III CRC tumors were analyzed by RNA sequencing (41 biopsies, 14 tumors) and multiplex immune protein analysis (89 biopsies, 29 tumors). CRC subtyping were performed using consensus molecular subtypes (CMS), CRC intrinsic subtypes (CRIS), and TUMOR types. ITH-scores and network maps were defined to determine the origin of heterogeneity. A validation cohort was used with one biopsy per tumor (162 tumors).ResultsOverall, inter-tumor transcriptional variation exceeded ITH, and subtyping calls were frequently concordant between multiregional biopsies. Still, some tumors had high ITH and were classified discordantly. Proximal tumors showed ITH related to differences in the microenvironment, while ITH within distal tumors were cancer-cell related. This subtype discordancy reflected actual molecular ITH within the tumors. The relevance of the subtypes was reflected at protein level where several inflammation markers were significantly increased in immune related transcriptional subtypes, which was verified in an independent cohort (Wilcoxon rank sum test; p<0.05).ConclusionOur transcriptomic and proteomic analyses show that the tumor location along the colorectum influence the ITH of CRC, which again influence the concordance of subtyping.

  Study EGAS00001004668

• A Machine Learning Tool Integrating Circulating Cell-Free DNA Methylation with Clinical Variables for Non-Invasive Diagnosis of Liver Graft Pathology

  Liver Transplant Recipients (LTRs) with elevated liver enzymes can develop graft injury due to various etiologies, such as T-cell mediated rejection (TCMR) and NASH (NASH-LT). While liver biopsy is the gold standard method for diagnosis of graft pathology, it is invasive and is associated with risks. The goal of our study was to develop a Machine Learning (ML) tool integrating clinical variables with methylation patterns on circulating DNA in plasma as a non-invasive diagnostic tool of graft injury. We generated methylation profiles of circulating DNA in a pilot study of 43 LTRs (11 NASH-LT, 19 TCMR, and 13 Control-LT), and developed an L2 multinomial logistic regression ML approach across 101 bootstrapped models to distinguish between the graft conditions. NASH was associated with distinctive methylation patterns on genes involved in fatty acid metabolism, while methylation of platelet-derived growth factors was identified in patients with TCMR. Our ML model achieved a mean multi-classification accuracy of 0.91, with mean specificity and sensitivity of 0.94 and 0.91, respectively. The model was found to be particularly adept at detecting TCMR and Control-LT, with true positive rates (TPRs) of 95% and 90%, and areas under the curve (AUROC) of 0.992 and 0.985, respectively. For NASH-LT, the models achieved a performance with a TPR of 82% and an AUROC of 0.991. These preliminary results suggest that our newly developed ML tool, leveraging cell-free DNA methylation and clinical variables, is a promising non-invasive classifier of graft pathology.

  Study EGAS00001007171

• OMKar

  The whole genome karyotype refers to the sequence of large chromosomal segments that make up an individuals genotype. karyotype analysis, which includes descriptions of aneuploidies and other rearrangements is crucial for understanding genetic risk factors, for diagnosis, treatment decisions, and genetic counseling linked to constitutional disorders. The current karyotyping standard is based on microscopic examination of chromosomes, a complex process that requires high expertise and offers Mb scale resolution. Optical Genome Mapping (OGM) technology can identify large DNA lesions in a cost-effective manner. In this paper, we developed OMKar, a method that uses OGM data to create a virtual karyotype. OMKar processes Structural (SV) and Copy Number (CN) Variants as inputs and encodes them into a compact breakpoint graph. It recomputes copy numbers using Integer Linear Programming to maintain CN balance and then identifies constrained Eulerian paths representing entire donor chromosomes. In tests using 38 whole genome simulations of constitutional disorders, OMKar reconstructed the karyotype with 88% precision and 95% recall on SV concordance and 95% Jaccard score on CN concordance. We applied OMKar to 50 prenatal, 41 postnatal, and 63 parental samples from ten different sites. OMKar reconstructed the correct karyotype in 144 out of 154 samples, covering 25 of 25 aneuploidies, 32 of 32 balanced translocations, and 72 of 82 unbalanced variations. Detected constitutional disorders included Cri-du-chat, Wolf-Hirschhorn, Prader-Willi deletions, Down, and Turner syndromes. Importantly, it identified a plausible genetic mechanism for five cases of constitutional disorder that were not detected by other technologies. Together, these results demonstrate the robustness of OMKar for OGM-based karyotyping.

  Study EGAS00001008245

• WGS_skin_punches

  We wish to understand the degree of copy number alterations in normal human sun exposed skin. We have collected small punches of epithelia and using a bait set design have shown the these span a clone. WGS will provide copy number information on these.

  Study EGAS00001004465

• FFPE cohort with RNA-seq data of tumor samples

  This dataset contains RNA-seq raw data in fastq format from 14 tumor samples. The samples are from primary tumors or metastasis and represent various cancer entities. The samples are formalin-fixed paraffin-embedded (FFPE) treated. For target enrichment SureSelect XT Human All Exon V6 was used. The libraries were sequenced in paired-end mode (2 x 50 nt) on a NovaSeq6000 S2 flow cell.

  Dataset EGAD00001006781

• Epigenome-wide association study of cocaine use disorder in postmortem human brain tissue

  Analysis of cocaine use disorder (CUD) associated epigenome-wide DNA methylation (DNAm) alterations in human postmortem brain tissue of Brodmann Area 9. Tissue samples from N=21 CUD cases and N=21 individuals without CUD originating from the Douglas Bell Canada Brain Bank (DBCBB) were included. Epigenome-wide DNAm was investigated using the Illumina Infinium MethylationEPIC array.

  Dataset EGAD00010002396

• RNAseq of preneoplasia lung adenocarcinoma

  RNA sequencing of 168 pulmonary samples including lung preneoplasia atypical adenomatous hyperplasia (AAH, N=38), adenocarcinoma in situ (AIS, N=22), minimally invasive adenocarcinoma (MIA, N=19) and invasive lung adenocarcinoma (ADC, N=38) and adjacent lung tissues (Normal, N=62).

  Study EGAS50000000271

• RNA sequencing of BCP-LBL patients samples

  In order to perform comprehensive transcriptomics and gene fusion analyses of a cohort of BCP-LBL patients, we performed RNA sequencing of 49 tissue samples from BCP-LBL patients. Because the material was available as FFPE, and had a relative low quality, we used a capture-based approach, where NGS libraries obtained from total RNA were captured in 4-plex, using a whole exome capture panel.

  Dataset EGAD50000000419

• Genomic Sequencing of Pediatric Rhaboid Cancers

  In this study, we sequenced the exomes of 35 rhabdoid tumors, highly aggressive cancers of early childhood. This study is part of a larger effort to characterize pediatric cancers as part of the Slim Initiative for Genomic Medicine (SIGMA) project.

  Study phs000508

• Blood Handling and Leukocyte Isolation Methods Impact the Global Transcriptome of Immune Cells

  In order to characterize the effects of upstream sample handling on the transcriptome of isolated leukocyte populations, we simulated various sample handling methods on whole blood prior to leukocyte isolation for low-input RNA-sequencing.

  Study phs001563

• 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

• Whole Exome Sequencing of Parathyroid Cancer Identifies Candidate Driver Mutations and Core Pathways

  Here we performed genomic characterization of twenty-nine sporadic parathyroid cancers using next generation whole exome sequencing. Thirty-three novel candidate driver genes for sporadic parathyroid cancer were proposed, in addition to previously known driver genes CDC73 and MEN1.

  Study phs001765

• Systematic Analysis of Coding and Non-coding Elements in Developmental Pathways Implicated in Holoprosencephaly Pathogenesis

  We applied targeted capture to examine 153 genes representative of all the major vertebrate developmental pathways among 340 probands and 285 of their unaffected parents to rank their relative significance as causes for holoprosencephaly (HPE).

  Study phs001653

• Kids First: Genetics of Kidney and Urinary Tract Malformations

  Congenital defects of the kidney and urinary tract are a common cause of kidney failure in children and adults and elucidation of the genetics of these disorders will provide new opportunities for diagnosis, risk stratification and prevention of complications.

  Study phs002162

• Whole Exome Sequencing Bipolar cases matched controls performed at Broad Inst on cohort from Germany

  This research project was a collaboration between University Hospital Frankfurt and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 823 Bipolar case/control samples from collaborators in Germany. Genomic DNA from each sample was sequenced to a mean depth of 20x. The exome used Twist capture and samples were sequenced on Illumina HiSeqX machines producing cram files.

  Dataset EGAD50000000563

• Whole Exome Sequencing of Bipolar cases, matched controls at Broad Inst on a cohort from Netherlands

  This research project was a collaboration between VU and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 943 Bipolar case/control samples from collaborators in the Netherlands. Genomic DNA from each sample was sequenced to a mean depth of 20x. The exome used Twist capture and the samples were sequenced on Illumina HiSeqX machines producing cram files

  Dataset EGAD50000000619

• Clinical panel sequencing of cancer of unknown primary using TruSight Oncology 500 (TSO500)

  TruSight Oncology 500 (TSO500) cancer panel sequencing data on paired tumour and germline DNA from cancer of unknown primary samples. All of the samples in this dataset are matched to WGS data. This data was used to compare biomarker yield against what was achieved with WGS and contains n=51 tumour-only samples. All samples are in BAM format aligned with GRCh38 reference genome.

  Dataset EGAD50000000657

• Whole Exome Sequencing of Bipolar cases, matched controls at Broad Inst on cohort from Cambridge, UK

  This research project was a collaboration between Cambridge University, UK and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 2,873 Bipolar case/control samples from collaborators in UK. Genomic DNA from each sample was sequenced to a mean depth of 20x. The exome used Twist capture and samples were sequenced on Illumina HiSeqX machines producing cram files

  Dataset EGAD50000000627

• Single-cell transcriptomics of neuroblastoma tumors

  Single-cell RNA-sequencing (CITE-seq) and single-nucleus RNA-sequencing data were generated from neuroblastoma patient tumors to uncover transcriptional heterogeneity at a single-cell resolution. In addition, we used the data to validate our findings of the NBAtlas.

  Study EGAS50000000223

• 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

• MCRI-WEHI GRREAT Ataxia Sequencing Cohort

  This study evaluated the effectiveness of long- and short-read sequencing as diagnostic tools for CA. We recruited 110 individuals (48 females, 62 males) with a clinical diagnosis of CA. Short-read genome sequencing (SR-GS) was performed to identify pathogenic RE and also non-RE variants in 356 genes associated with CA. Long-read adaptive sequencing (LR-AS) was performed to identify pathogenic RE in 67 genes.

  Dataset EGAD50000000815

• IMPRESS_all

  Dataset containing tumor, normal and blood sample data, for various tissue and tumor types. Data is targeted methylation data, using smMIP probes to target informative CpG sites in the genome. Sample type can be inferred from the name, with 'W' being normal samples and 'TC' tumor samples. Dataset contains 259 samples in duplicate, with one half of the runs being cut by MSRE's, and the other not. For every sample paired fastq files are present.

  Dataset EGAD50000000882