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• Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023

  Synthetic - This submission contains a subset of a synthetic dataset derived from the project Heilsa Tryggvedottir - a Nordic collaboration on sharing sensitive human data. Heilsa Tryggvedottir is funded by the Nordic e-Infrastructure Collaboration (NeIC), the ELIXIR nodes of Finland, Norway, and Sweden, Computerome in Denmark, and the Estonian Scientific Computing Infrastructure (ETAIS). In the synthetic data creation process, it was attempted to strike a fine balance between the usability of the datasets (e.g. technical FEGA development, testing, user training, and basic bioinformatics) and compliance with GDPR. File names and file content (e.g. headers in fastq) are anonymized. Moreover, the X, Y, and mitochondrial sequences have been discarded from the original data since these data can be used for maternal, paternal, or ethnic origin tracing. The dataset does not follow natural haplotype distribution (inherent to imputation panels). The only inputs derived from real sequence data are variant distribution density per chromosome and learning sequencing error models. The synthetic dataset consists of two fastq files, a cram file, a vcf file, and two index files.

  Dataset EGAD50000000119

• GoNL release 5 haplotype panel

  The samples in this panel come from 250 families: 248 parents-child trios and 2 parent-child duos. As the children do not provide additional haplotypes or population information, they were excluded from the panel. The samples present in the release are composed of 248 couples, 2 single individuals and 1 sample composed from the 2 haplotypes from the duo's children transmitted by their missing parent. The composed sample is named gonl-220c_223c.The files contain a total of 18.9M SNVs and 1.1M INDELs in autosomal chromosomes. They were generated by phasing/imputing the SNVs (a) and INDELs (b) using MVNCall. Only sites passing filters are reported. Sites filtered as part of the GoNL inaccessible genome were kept (but flagged as filtered) and still may contain true positive calls but should be used with care as they are located in parts of the genome that are less well captured (systematic under or over-covered or low-mapping quality)

  Dataset EGAD00001000744

• Integrated genomic characterization of adrenocortical carcinoma

  Adrenocortical carcinomas (ACC) are aggressive cancers originating in the cortex of the adrenal glands. Despite the overall poor prognosis, ACC outcome is heterogeneous. CTNNB1 and TP53 mutations are frequent in these tumors, but the complete spectrum of genetic changes remains undefined. Exome sequencing and SNP array analysis of 45 ACC revealed recurrent alterations in known drivers (CTNNB1, TP53, CDKN2A, RB1, MEN1) and genes not previously reported to be altered in ACC (ZNRF3, DAXX, TERT and MED12), which were validated in an independent cohort of 77 ACC. The cell-surface transmembrane E3 ubiquitin ligase ZNRF36 was the gene the most frequently altered (21%), and appears as a potential novel tumor suppressor gene related to the ß-catenin pathway.Our integrated genomic analyses led to the identification of two distinct molecular subgroups with opposite outcome. The C1A group of poor outcome ACC was characterized by numerous mutations and DNA methylation alterations, whereas the C1B group with good prognosis displayed a specific deregulation of two miRNA clusters. Thus, aggressive and indolent ACC correspond to two distinct molecular entities, driven by different oncogenic alterations.

  Dataset EGAD00001000764

• A practical guide for mutational signature analysis in hematological malignancies

  Analysis of mutational signatures is becoming routine in cancer genomics, with implications for pathogenesis, classification, prognosis, and even treatment decisions. However, the field lacks a consensus on analysis and result interpretation. Using whole-genome sequencing of multiple myeloma (MM), chronic lymphocytic leukemia (CLL) and acute myeloid leukemia, we compare the performance of public signature analysis tools. We describe caveats and pitfalls of de novo signature extraction and fitting approaches, reporting on common inaccuracies: erroneous signature assignment, identification of localized hyper-mutational processes, overcalling of signatures. We provide reproducible solutions to solve these issues and use orthogonal approaches to validate our results. We show how a comprehensive mutational signature analysis may provide relevant biological insights, reporting evidence of c-AID activity among unmutated CLL cases or the absence of BRCA1/BRCA2-mediated homologous recombination deficiency in a MM cohort. Finally, we propose a general analysis framework to ensure production of accurate and reproducible mutational signature data.

  Dataset EGAD00001005028

• RNA-seq FASTQ files from newborn screening dried blood spot samples

  Paired-end RNA-seq FASTQ files from 21 newborn screening dried blood spot (DBS) samples. These DBS samples were obtained from extremely low gestional age newborns, where 10 of them were affected by a fetal inflammatory response (FIR) before birth, and 11 were unaffected. Total RNA was sequenced using an Illumina NextSeq-500 instrument. The sample preparation protocol included the depletion of rRNA and globin mRNA using the Globin Zero Gold rRNA Removal Kit from Illumina. Libraries were prepared using the NebNext Ultra TM II Directionl RNA LIbrary Prep Kit (New England Biolabs). Each sample was sequenced in 4 lanes, leading to 8 FASTQ files per sample and a total of 21x8=168 FASTQ files. There is an additional number of 8 FASTQ files corresponding to sample BS13, which was downsampled to 1/4 of its original depth (see BS13_README file for details).

  Dataset EGAD00001004991

• DRIP-seq data for Molecular Characterization of ETMRs

  This dataset contains DRIP-seq data from 2 patients. DNA–RNA hybrids were extracted from tissue derived from ETMR patient-derived xenograft (PDX) models (BT183) that were treated using topotecan or saline as described previously27. Tumours were subsequently frozen and pelleted using ultracentrifugation. DNA–RNA hybrids were extracted as described previously using the same protocol that is applied for cultured cells21. DNA was extracted using proteinase K followed by phenol–chloroform extraction and ethanol precipitation. Subsequently the DNA was fragmented using the restriction enzymes HindIII, EcoRI, BsrGI, XbaI and SspI (New England Biolabs). Digested DNA was subsequently incubated with the anti-DNA–RNA hybrid anti- body S9.6 (Merck, MABE1095) and immunoprecipitated using agarose beads. Bound DNA–RNA hybrids were eluted and incubated with pro- teinase K and cleaned with an additional phenol–chloroform–ethanol extraction. The DNA was subsequently sonicated and sequenced using a Hiseq 2000 machine with a 50-bp single-read protocol. Each treat- ment condition was performed in duplicate and both RNase H and the input was included as negative controls resulting in 10 Fastq files.

  Dataset EGAD00001006219

• Human liver mtDNA sequencing

  BAM files containing paired-end mtDNA sequencing data from morphologically normal human liver. Clonal CCO-deficient patches of hepatocytes were identified in human liver samples, and samples were taken along a line spanning approximately from the portal triad to the central hepatic vein. Individual BAM files are named according to their patch, line and cut, where cut 1 is nearest to the portal triad, and cuts 2, 3 etc. lying further from the portal triad. Other file types include "Bulk" samples, contain sequencing data of the remaining CCO-deficient cells that were not sampled as part of the line of cuts, and "Stroma" control samples (used for identifying germ-line variants). Sequenced on NextSeq 500 platform.

  Dataset EGAD00001007991

• Transcriptional consequences of SETBP1 variants in developmental disorders and malignancies

  The SETBP1 gene encodes a DNA-binding nuclear protein that regulates gene expression across multiple tissues. Precise control of SETBP1 dosage is essential for normal cellular function, as both loss- and gain-of-function variants of SETBP1 can lead to severe phenotypic consequences. De novo germline gain-of-function point mutations that prolong SETBP1 half-life result in Schinzel-Giedion syndrome (SGS), an ultra-rare and severe congenital disorder associated with extensive developmental abnormalities and health complications. These SGS-associated variants cluster within a hotspot in the SKI-homology domain of SETBP1, overlapping with somatic mutations recurrently observed in myeloid leukaemia with poor prognosis. Thus far, the precise mechanisms by which these SETBP1 mutations drive disease remain largely unclear. Here, through single-cell and single-nucleus mRNA sequencing of a rare collection of primary patient samples with SGS and myeloid leukaemia, we examined the transcriptional consequences of SETBP1 gain-of-function variants in haematopoietic cells, comparing their impacts when acquired in the germline and somatically in malignancies.

  Dataset EGAD00001015482

• Genomic Advances in Sepsis (GAinS) genotyping

  This cohort comprises a subset of patients enrolled in the Genomic Advances in Sepsis (GAinS) study, an established biobank of adult sepsis patients. Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Patients with sepsis due to community acquired pneumonia or faecal peritonitis were recruited from 35 hospitals across the UK from 2005-2018, with samples for functional genomics and detailed clinical information collected over the first five days of ICU admission to investigate how host genetics affects the individual repsonse to sepsis. DNA was extracted from buffy coat or whole blood samples using the Qiagen DNA extraction protocol, the automated Maxwell Blood purification kit (Promega), or the QIAamp Blood Midi kit protocol (Qiagen). Genotyping data were generated using the Illumina HumanOmniExpress BeadChip (295 patients), the Infinium CoreExome BeadChip (655 patients), and the Infinium Global Screening Array BeadChip (307 patients). Genotyping QC and imputation into the Haplotype Reference Consortium was perfomed within each batch. The datasets were combined and following post-imputation filtering data were available on 1168 samples.

  Dataset EGAD00001015369

• Gene expression QTL mapping in stimulated iPSC-derived macrophages

  Immune cells sense and respond to external stimuli, initiating an inflammatory response, with genetic variants potentially altering these responses. Traditional QTL mapping often uses naïve cells, missing condition-specific variants detectable post-stimulation. Our study uses a high-throughput platform to map eQTLs across 24 conditions using iPSC-derived macrophages, identifying that 76% of eQTLs in stimulated conditions were also present in naïve cells. We found response eQTLs (reQTLs) vary widely across conditions, with rare single-condition reQTLs being overrepresented among disease-colocalizing eQTLs. This study nominates 21.7% more disease effector genes at GWAS loci through reQTL colocalization, with 38.6% not found in the GTEx catalogue, highlighting the importance of condition-specific regulatory variation in understanding disease risk alleles. Our findings underscore the value of condition-specific studies in elucidating the genetic mechanisms underlying complex diseases. 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/

  Dataset EGAD00001015380