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• Sequencing of uveal melanoma metastases

  Whole genome, exome and RNA sequencing of uveal melanoma metastases, primary tumors and matched normal DNA.

  Dataset EGAD00001006031

• Human tumour and normal-matched WES

  We profiled 23 high-grade gliomas patient tumor samples and 4 normal-matched patient samples by whole exome sequencing. The raw fastq files are provided.

  Dataset EGAD00001006099

• DIPG WES and RNA-Seq

  We characterised H3K27M-mutant diffuse intrinsic pontine glioma (DIPG, n=21) and RNA-Seq (n=26 DIPG, 12 normal brain)

  Dataset EGAD00001006450

• Clinical phenotype data

  Clinical data corresponding to the patients with ovarian cancer studied using scRNA-seq and bulk RNA-seq. Variables include molecular subtype, predicted immune phenotype, reviewing pathologist comment, final immune phenotype, histology characterization, and tumor stage.

  Dataset EGAD00001006973

• Sequencing data for "Replication timing alterations are associated with mutation acquisition during tumour evolution in breast and lung cancer"

  Repli-seq data for "Replication timing alterations are associated with mutation acquisition during tumour evolution in breast and lung cancer"

  Dataset EGAD00001015363

• Relationship between low LDL cholesterol concentrations not due to statin therapy and risk of type 2 diabetes: a US-based cross-sectional observational study using electronic medical records

  Lipid lowering therapy using HMG-CoA reductase inhibitors (statins) is associated with an approximately 9-12% increase in the risk of new-onset type 2 diabetes (T2DM). The risk of diabetes could be increased by statins directly; however, genetic approaches have also implicated low LDL cholesterol (LDL-C) concentrations as a risk factor for T2DM. Mendelian randomization studies using functional variants in both HMGCR (GeneID:3156) and PCSK9 (GeneID:255738) genes found a higher risk of T2DM in individuals with variants associated with lower LDL-C concentrations. Since PCSK9 and HMGCR are involved in lipid metabolism through distinct molecular pathways, the altered glycemic effect associated with variants in both genes is likely to be the result of their common effect on LDL-C concentrations. Despite the findings from statin clinical trials and genetic studies, there is little direct evidence implicating low LDL-C concentrations with increased risk of T2DM. Individuals who have very low LDL-C concentrations not due to lipid lowering therapy can provide insights into the relationship between low LDL-C concentrations and T2DM. Here, we used de-identified electronic health records (EHRs) to test the hypothesis that low LDL-C concentrations are associated with T2DM.

  Study phs001588

• Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA)

  The OPPERA project has two sub-studies investigating effects of genetic, physiological, psychological and clinical factors on the risk of developing painful temporomandibular disorder (TMD). OPPERA I baseline case-control study of chronic TMJD (dbGaP study accession phs000762). This sub-study uses a case-control study design to investigate characteristics associated with chronic TMD. The parent OPPERA phenotype file contains an indicator variable (PHASE1CASECONTROL) identifying participants in this sub-study. Data users should use the relevant case-classification variable (CHRNONICTMDCASE) and apply statistical methods appropriate for case-control studies when analyzing TMJD in this sub-study. OPPERA I prospective cohort for first onset of TMJD (dbGaP study accession phs000850). This sub-study uses a prospective cohort study design to investigate characteristics that predict risk of developing first-onset TMD. The parent OPPERA phenotype file contains an indicator variable (PROSPECTIVECOHORT) identifying participants in this sub-study. Data users should use the relevant case-classification variable (INCIDENTCASE) and the relevant time-in-study variable (FOLLOWUPYEARS) when analyzing TMJD in this sub-study. It is recommended that data users apply statistical methods appropriate for cohort studies of a dynamic population (for example, Cox regression models, where INCIDENTCASE is the censoring variable and FOLLOWUPYEARS is time in study).

  Study phs000761

• DEMENTIA-SEQ: WGS in Lewy Body Dementia and Frontotemporal Dementia

  Lewy body dementia, amyotrophic lateral sclerosis/frontotemporal dementia, and multiple system atrophy are age-related, neurodegenerative syndromes that are poorly understood. Delineating the genetic risk that is driving the pathophysiology of these neurological diseases is fundamental for understanding disease mechanisms and for developing disease-modifying treatments. In version 1 of the study/dbGaP deposition, we performed a whole-genome sequencing study consisting of 7,403 total samples, including 2,633 genomes from patients with Lewy body dementia, 2,641 frontotemporal dementia patients, and 1,980 neurologically healthy controls. Of these, 6,907 were uploaded to dbGaP as the basis of the DementiaSeq, phs001963 dataset. The data relating to these samples are available on dbGaP. In version 2 of this study/dbGaP deposition, we made much of these data available on Anvil. More specifically, data for 6,254 of these samples were also uploaded to the ALS Compute platform on AnVIL. The data for the remaining 653 samples are only available on dbGaP. The dbGaP/AnVIL Table lists the availability of dbGaP and AnVIL for each individual sample: phd008475.In version 3 of the study/dbGaP deposition, we added whole-genome sequence data generated using DNA samples obtained from 683 patients diagnosed with multiple system atrophy.

  Study phs001963

• Phylogenetic analysis of paired breast carcinomas identifies genetic events associated with clonal recurrence and invasive progression

  Development of ipsilateral breast carcinoma following diagnosis of breast ductal carcinoma in situ (DCIS) has been assumed to represent recurrence of the primary tumour. However, this may not be the case and it is important to know how often recurrences are new tumours. Ipsilateral primary-recurrence pairs (n=78) were sequenced to test their clonal relatedness. Shared genetic events were identified from whole exome sequencing (n=54 pairs) using haplotype-specific copy number and phylogenetic analysis. The remaining pairs were sequenced by a targeted panel or low-coverage whole genome sequencing. We included 32 non-recurrent DCIS to compare recurrent and non-recurrent disease. We found that 7% of DCIS recurrences were non-clonal by whole exome sequencing, indicative of a new breast carcinoma. Lower resolution methods detected a higher non-clonality rate (29%). Comparing primary DCIS with their recurrences found that evolution of DCIS to invasive disease was associated with increased ploidy and copy number events. TP53 mutations were enriched in DCIS with clonal recurrence compared with non-recurrent DCIS. Our results verify that de novo “recurrent tumours” of independent origin occur in patients who may be at high risk. (https://www.biorxiv.org/content/10.1101/2024.05.19.594731v2.full)

  Study EGAS50000001298

• Comparison of structural variations from 10X Genomics linked-reads and conventional Illumina short-reads sequencing

  Structural variations (SVs) are large genomic rearrangements that can drive many diseases. Conventional short-reads whole genome sequencing (cWGS) allows their identification with base-pair resolution, but suffers from high false discovery rate. cWGS taps in short-range information from short-reads while linked-reads sequencing (10XWGS) utilizes long-range information. 10XWGS allows linkage of short-reads originating from the same large DNA molecule with a unique barcode captured in a gel bead in emulsion. This mitigates alignment-based artefacts from cWGS especially in repetitive regions. However, the false discovery rate of this technology is unclear. In this study, we performed a comprehensive analysis of different type and size of SVs predicted from these two technologies. The SVs common between both technologies were found to be highly specific by PCR and Sanger sequencing while validation rate dropped for uncommon events. Further, we propose a novel enrichment approach for filtering out false positive calls from both the technologies independently. To this end, we trained a machine learning model for respective technologies and used it to characterise SVs from MCF7 cell line and a primary breast cancer tumor with high precision. This approach would be valuable in understanding true mechanisms driven by SVs in various diseases.

  Study EGAS00001004093

• Whole exome sequencing on primary retinoblastoma tissues and matching lymphocyte DNA.

  Retinoblastoma is a rare childhood cancer initiated by RB1 mutation or MYCN amplification, while additional alterations may be required for tumor development. However, the view on single nucleotide variants is very limited. To better understand oncogenesis, we determined the genomic landscape of retinoblastoma. We performed exome sequencing of 71 retinoblastomas and matched blood DNA. Next, we determined the presence of single nucleotide variants, copy number alterations and viruses. Aside from RB1, recurrent gene mutations were very rare. Only a limited fraction of tumors showed BCOR (7/71, 10%) or CREBBP alterations (3/71, 4%). No evidence was found for the presence of viruses. Instead, specific somatic copy number alterations were more common, particularly in patients diagnosed at later age. Recurrent alterations of chromosomal arms often involved less than one copy, also in highly pure tumor samples, suggesting within-tumor heterogeneity. Our results show that retinoblastoma is among the least mutated cancers and signify the extreme sensitivity of the childhood retina for RB1 loss. We hypothesize that retinoblastomas arising later in retinal development benefit more from subclonal secondary alterations and therefore, these alterations are more selected for in these tumors. Targeted therapy based on these subclonal events might be insufficient for complete tumor control.

  Study EGAS00001001690

• Large-scale viral genome analysis identifies novel clinical associations between hepatitis B virus and chronically infected patients

  Despite the high global prevalence of chronic hepatitis B (CHB) infection, datasets covering the whole hepatitis B viral genome from large patient cohorts are lacking, greatly limiting our understanding of the viral genetic factors involved in this deadly disease. We performed deep sequencing of viral samples from patients chronically infected with HBV to investigate the association between viral genome variation and patients’ clinical characteristics. We discovered novel viral variants strongly associated with viral load and HBeAg status. Patients with viral variants C1817T and A1838G had viral loads nearly three orders of magnitude lower than patients without those variants. These patients consequently experienced earlier viral suppression while on treatment. Furthermore, we identified novel variants that either independently or in combination with precore mutation G1896A were associated with the transition from HBeAg positive to negative phase of infection. These observations are consistent with the hypothesis that mutation of the HBeAg open reading frame is an important factor driving CHB patient’s HBeAg status. This analysis provides a detailed picture of HBV genetic variation in the largest patient cohort to date and highlights the diversity of plausible molecular mechanisms through which viral variation affects clinical phenotype.

  Study EGAS00001003689

• Longitudinal evaluation of serum microRNAs as biomarkers for neuroblastoma burden and therapeutic p53 reactivation

  Accurate assessment of treatment response and residual disease is indispensable for the evaluation of cancer treatment efficacy. However, performing tissue biopsies for longitudinal follow-up poses a major challenge in the management of solid tumours like neuroblastoma. In the present study, we evaluated whether circulating miRNAs are suitable to monitor neuroblastoma tumour burden and whether treatment-induced changes of miRNA abundance in the tumour are detectable in serum. We performed small RNA sequencing on longitudinally collected serum samples from mice carrying orthotopic neuroblastoma xenografts that were exposed to treatment with idasanutlin or temsirolimus. We identified 57 serum miRNAs to be differentially expressed upon xenograft tumour manifestation, out of which 21 were also found specifically expressed in the serum of human high-risk neuroblastoma patients. The murine serum levels of these 57 miRNAs correlated with tumour tissue expression and tumour volume, suggesting potential utility for monitoring tumour burden. In addition, we describe serum miRNAs that dynamically respond to p53 activation following treatment of engrafted mice with idasanutlin. We identified idasanutlin-induced serum miRNA expression changes upon one day and 11 days of treatment. By limiting to miRNAs with a tumour-related induction, we put forward hsa-miR-34a-5p as a potential pharmacodynamic biomarker of p53 activation in serum.

  Study EGAS00001006678

• Knoll et al.: The life-saving benefit of dexamethasone in severe COVID-19 is linked to a reversal of monocyte dysregulation

  Following the demonstration of its life-saving effect in clinical trials, dexamethasone quickly became standard-of-care in the treatment of severe COVID-19. Beneficial effects were reported for patients requiring supplemental oxygen or invasive mechanical ventilation. Yet, a substantial proportion of patients still progressed to a critical condition or succumbed to the disease, despite timely initiation of glucocorticoid treatment. No molecular or cellular correlate of this beneficial treatment response has been defined. Here, we identify distinct cellular and molecular changes in circulating immune cells in patients with COVID-19 elicited in response to dexamethasone treatment. The most profound transcriptional changes in response to dexamethasone treatment were noted in monocytes and in B cells. In monocytes, we observed a reversal of hallmark signatures previously associated with COVID-19 severity, and the induction of a specific monocyte substate enriched in glucocorticoid response gene expression which we here refer to as dexamethasone response cluster. Molecular responses to dexamethasone treatment were directly linked to clinical outcome, since the reversal of pathogenic signatures and induction of glucocorticoid response genes were enriched in patients who survived the disease in response to dexamethasone treatment. Cellular responsiveness of circulating monocytes was thus identified as a correlate of clinical response to dexamethasone treatment. Changes in monocyte transcriptomes could also be linked to epigenetic alterations highlighting early differences in monocytes of dexamethasone responders and non-responders. Further, monocyte single-cell transcriptome-derived signatures were enriched in whole blood transcriptomes from patients with fatal outcome in two independent cohorts, highlighting the potential clinical value for early identification of non-responders who are refractory to dexamethasone treatment. Overall, our findings indicate that the life-saving effects of dexamethasone in COVID-19 patients are linked to a specific immunomodulatory effect based on the reversion of monocyte dysregulation. Our study elucidates the potential mechanism of action of one of the most efficient and cost-effective drugs for the treatment of COVID-19. It further highlights the potential of single-cell omics for monitoring target engagement of immunomodulatory drugs and for the stratification of patients for precision medicine approaches.

  Study EGAS00001007461

• The Familial Intracranial Aneurysm Linkage Study (FIA)

  Our long-term objective is to identify susceptibility genes that are related to the formation of intracranial aneurysms (IA). Rupture of IAs occurs in 16,000 to 17,000 persons in the U.S. annually and nearly half of affected persons are dead within the first 30 days. An additional 6,000 to 7,000 persons with unruptured IAs are identified each year. Accumulated evidence indicates that a genetic component plays an important role in the development of IAs, but specific loci affecting the risk of IA have not been identified. The primary hypothesis of this study is that there are specific human chromosomal regions that are associated with an increased risk of IAs. Specific Aims of are: Recruitment of 400 (475) families with multiple individuals who have an IA through 23 (25) referral centers throughout North America, Australia, and New Zealand that represent 35 (40) recruitment sites. Ascertainment of interviews and blood samples from all affected family members as well as their first-degree relatives. White blood cells from living persons with an IA will be cryopreserved at Coriell Institute for Medical Research for future immortalization of cells lines as indicated. Identification of unruptured IAs by obtaining MRAs in selected asymptomatic siblings (of affected individuals). Completion of a 10 cM genome series in persons with IAs as well as the spouses and children of persons with an IA who are deceased. We will perform finer mapping of chromosomal regions with suggestive evidence of linkage in the genome screen. Performance of a nonparametric (allele sharing) linkage analysis, including relevant environmental factors such as smoking, to identify chromosomal regions linked to IA. Reconstruction of the genotypes of deceased affected family members will be performed. Identification of individuals who are genetically at high risk for the development of IAs would enable targeted and effective screening/prevention/treatment strategies to reduce the substantial mortality and morbidity associated with this devastating type of stroke. Only a multidisciplinary, collaborative effort to identify, accrue, and genotype FIA families will be successful in identifying sufficient high-risk families to characterize the genetic underpinnings of IA.

  Study phs000293

• National Institute of Mental Health (NIMH) Duke Cognition Cohort

  The overall goal is to identify genetic variants influencing normal variation between humans, with a focus on cognitive and neurosensory traits. Because of the focus of funding agencies on disease, very few studies of the genetics of non-disease traits have been performed. The genetic variation underlying the vast majority of differences between humans therefore remains not only undiscovered, but also unexplored. We have assessed >1,200 healthy volunteers for >70 diverse, non-disease phenotypes, with a focus on cognitive and neurosensory traits that are understudied and seem to have a strong biological basis. Our study includes measurements of face recognition, night vision, cognitive performance, auditory pitch discrimination, contagious yawn susceptibility, time perception, sense of direction, taste preferences, personality, sexual behaviors, sleep habits, acne, headache locations, synesthesia, and teeth sensitivity. This method is very different from the focus of most studies on collecting cohorts of patients with disease and controls with minimal phenotypic information. Because we phenotype the same healthy volunteers for multiple highly variable yet heritable traits, we can perform genetic analyses on a large number of phenotypes by just investing once in sequencing the DNA of these participants. An additional >2,000 participants in our cohort have been measured just for cognitive performance instead of for the more extensive phenotyping. Most of the genetic studies in this cohort have been about cognitive perfromance. The number of participants with genetic data generated who have undergone the more extensive phenotyping is limited. Our studies of the more novel traits like contagious yawning have therefore focused more on correlations between different phenotypes until we have a sufficient sample size to truly attempt to make genetic discoveries. . The dataset also includes two small challenge studies. In one study, the cognitive performance of 150 healthy volunteers was measured before and after an acute dose of the antiepileptic drug topiramate. In the other study, the cognitive performance of 13 healthy volunteers was measured before and after 24 hours of sleep deprivation.

  Study phs001406

• Submitting array based metadata

  Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

  Documentation submission/metadata/submission/array

• Myelodysplastic Syndrome Follow Up Series

  Agilent whole exome hybridisation capture was performed on genomic DNA derived from MDS and matched normal DNA from the same patients. Next Generation sequencing performed on the resulting exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes. Now we aim to discover the prevalence of our findings using bespoke pulldown methods and sequencing the products from a larger set of patient DNA.

  Dataset EGAD00001000283

• MassArray1-80

  234 samples genotyped at 40 loci using the MassArrayiPLEX genotyping assay using the iPLEX Gold genotyping kit (Agena Biosciences, cat. 10148-2) Gabriel et al. (2009) [Gabriel, S. , Ziaugra, L. and Tabbaa, D. (2009), SNP Genotyping Using the Sequenom MassARRAY iPLEX Platform. Current Protocols in Human Genetics, 60: 2.12.1-2.12.18. doi:10.1002/0471142905.hg0212s60]). Products were detected on a MassArray mass spectrophotometer and data were acquired in real time with MassArray RT software 4.0.0.2 (Agena Biosciences). SNP clustering and validation was carried out with Typer 4.0.26.75 software (Agena Biosciences).

  Dataset EGAD00010001906

• Methylome profiling of Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) and a patient derived cell-line model

  Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. This study established and characterized a novel SFT/HPC patient-derived cell line called SFT-S1 using the twist human methylome panel.

  Study EGAS50000000026

• Heterogeneity and evolution of DNA mutation rates in microsatellite stable colorectal cancer. Higher mutation rates (MR) in metastatic tumours

  Building on the observation that MR appears to be a unique and inheritable feature of each cancer cell, we explored whether MR is subject to selective pressure during cancer progression. These data suggest that a higher MR is selected during CRC metastatic progression.

  Study EGAS50000000147

• University of Alabama at Birmingham (Xenotransplantation Project) - DAC

  This is a Data Access Committee (DAC) developed to govern access to sequence level data produced by the Porrett Lab, University of Alabama at Birmingham (Xenotransplantation Project), and headed by Dr. Jayme Locke, and Dr. Porrett M. Paige, Principal, and Co-Principal Investigators respectively. These data were largely used in the generation of the manuscript: “Spatiotemporal immune atlas of the first clinical-grade gene-edited pig-to-human kidney xenotransplant”. Access to these data will be granted to qualified investigators for appropriate use following data use approval from members of the DAC.

  Dac EGAC50000000188

• Bulk Iso-Seq from brain tissue and exosomes isolated from brain tissue using long-read PacBio sequencing of poly-adenylated transcripts

  Human brain sEVs were isolated from postmortem human brain tissue. RNA was extracted from both the source brain tissue and sEVs and prepared for long-read sequencing. cDNA was sequenced on the PacBio Sequel II. The data provided have undergone processing using the tool ccs (v6.0.0) to generate circula consensus reads. For further processing, it is recommended to follow the guidelines at https://isoseq.how/, starting with using lima to remove cDNA primers.

  Dataset EGAD50000000043

• Metaplastic breast cancer in a patient with neurofibromatosis type 1 and somatic loss of heterozygosity

  Targeted genetic sequencing was performed on a 41-year-old female with metaplastic breast cancer (MBC) and neurofibromatosis type I (NF1). A custom panel capturing 279 cancer related genes was used to identify a germline pathogenic SNV in NF1, as well several other somatic alterations.

  Study phs001566

• Small-RNA sequencing of pheochromocytoma and paraganglioma arising from germline SDHB mutations

  Single-end 75 basepair small-RNA sequencing of 90 SDHB-deficient non-metastatic and metastatic pheochromocytoma and paraganglioma. Samples were prepared using the NEXTFLEX® Small RNA-Seq Kit v3 (Bioo Scientific ). Samples were sequenced at the molecular genomics core (Peter MacCallum Cancer Centre) using 50bp single end sequencing on the Illumina NextSeq 500 (Illumina, USA). This dataset contains raw sequencing reads in FASTQ format.

  Dataset EGAD50000000501

• ATAC sequencing raw data files of human Treg cell subsets

  Assay for Transposase-Accessible Chromatin using sequencing (ATAC) sequencing of human CD38+, HLA-DR+, CD38+ HLA-DR+, and CD38- HLA-DR- regulatory T (Treg) cell subsets sorted from peripheral blood pooled from 4 healthy donors. After isolation and purification of Treg cell subsets with flow cytometry, samples were frozen and transferred to Active Motif for ATAC sequencing. The dataset contains one raw sequencing file (fastq) of accesible regions for each of the above-mentioned Treg cell subsets.

  Dataset EGAD50000000664

• Transcriptomic changes in placenta associated with anesthesia, delivery mode and maternal diabetes

  Samples were from 34 mothers, 6 with gestational diabetes, 14 with type 1 diabetes and 14 without diabetes history with either vaginal (N=17) or cesarean section (n=17) delivery, comprising primary planned section (N=9) or secondary emergency section (N=8). Anesthesia methods during delivery included spinal (N=11), epidural (N=11), or local anesthesia (N=6), with unknown anesthesia information for six cases. Samples from each other were from the Villous and the Decidua part of the Placenta

  Dataset EGAD50000000701

• Base modification analysis using single molecule real-time sequencing

  This dataset was generated using single-molecule real-time sequencing for both training and testing purposes. The DNA samples isolated from human buffy coats or placenta of healthy individuals (n=4,) were prepared in a way that four types of base modifications were incorporated, including unmodified controls, 5mC, 5hmC, and 6mA. The sequence data for each sample consists of subreads produced by single-molecule real-time sequencing and stored in a bam format. Details of the specific experiments conducted in this study are outlined in our manuscript.

  Dataset EGAD50000000541

• Homologous recombination repair status in metastatic prostate cancer by next-generation sequencing and functional tissue-based immunofluorescence assays

  - WES files from primary or metastatic prostate cancer biopsies, from patients with metastatic prostate cancer. - This dataset includes those cases profiled by WES and included in the manuscript Homologous recombination repair status in metastatic prostate cancer by next-generation sequencing and functional tissue-based immunofluorescence assays, Arce-Gallego et al, Cell Rep Med 2024 - The dataset includes WES for 80 tumor biopsies from 65 different patients, including paired tumor/normal samples.

  Dataset EGAD50000001018

• 41 fibroblast RNAseq samples of pediatric patients with childhood epilepsy and developmental delay

  We used RNA sequencing of patient fibroblasts to find a genetic diagnosis in patients with exome-negative childhood epilepsy. RNA extracted from 41 individuals' cultured fibroblasts (passage 3-6) was collected and sequenced at a depth of 90-110 paired reads per sample.

  Study EGAS50000000792

• Sequencing data of the ampulla and fimbriae of the fallopian tube in pre-menopausal women

  A total of 10 pre-menopausal patients were recruited for this study. Sequencing of both the fimbriae and ampullary region of the fallopian tubes was performed using 10x Genomics single-cell RNA-seq and ATAC-seq. For one patient, data were sequenced across 4 lanes, while the remaining patients' data were sequenced in 2 lanes each, resulting in a total of 44 sequencing datasets for each platform.

  Dataset EGAD50000000889

• Spatial and temporal transcriptome analysis on human skeletal muscle regeneration

  Skeletal muscle carries a unique ability for adaptation as well as regeneration that highly depends on a supportive cellular microenvironment. Here we show that spatial and single-cell transcriptomic analysis in human skeletal muscle is a unique approach to illuminate cellular interactions in the microenvironment.

  Study EGAS50000000182

• Multi-omic dataset utilized in the analysis of a phase II study of epigenetic priming followed by nivolumab in lung cancer

  This dataset includes sequence files from whole exome sequencing and bulk RNA sequencing of tissue and blood biospecimens from a phase II clinical trial investigating epigenetic priming followed by immune checkpoint blockade in non-small cell lung cancer (NSCLC; NCT01928576). There are 78 whole exome sequencing files and 36 bulk RNA sequencing files included in this dataset.

  Dataset EGAD50000001336

• (ATAC-seq) Shifted assembly and function of mSWI/SNF family subcomplexes underlie targetable dependencies in endometriod carcinomas

  Raw and processed ATAC-seq data from the VOA1066 undifferentiated endometrial carcinoma cell-line (10 samples). Samples were treated with DMSO, dBRD9, or compound 12, without doxycycline; some samples instead received doxycycline-induced ARID1A treatment, or doxycycline alone. Samples were sequenced on Illumina NextSeq 500 using 37 bp paired-end sequencing parameters. Raw fastq fastq files, bigwig files giving normalized genomic coverage per region, and called broad peaks are included for each sample.

  Dataset EGAD50000001472

• Single-cell RNA-seq, single-cell TCR-seq, single-cell ATAC-seq, and CITE-seq of human tonsillar CD4+ T cells

  To describe human tonsillar CD4+ T cells, especially IL10+ Foxp3- follicular CD4+ T cells (Tfr1, T follicular regulatory type 1), human tonsillar CD4+ T cells were analyzed with single-cell RNA-seq, single-cell TCR-seq, single-cell ATAC-seq, and CITE-seq.

  Study JGAS000805

• RNA-Seq of Myeloid Cells in Dendritic Cell Therapy Response

  RNA libraries were prepared from 100 ng of total RNA using the Illumina Stranded Total RNA Prep with Ribo-Zero Plus kit, then sequenced (2×150 bp) on the Illumina NextSeq550. Transcript-level quantification was done with kallisto, followed by gene-level summarization (tximport) and differential expression analysis (DESeq2). Quality control included PCA, hierarchical clustering, and deconvolution with CIBERSORTx to exclude non-myeloid genes. Final analyses identified transcriptomic differences among healthy donors and three patient subgroups based on response to dendritic cell therapy.

  Dataset EGAD50000001681

• RNA, ATAC, ChIP datasets from iPSC Derived Macrophages with and without TNFRSF1A intronic deletion using CRISPR

  This dataset consists of functional genomic data from iPSC derived Macrophages with or without CRISPR deletions and controls. It contains 21 paired end fastq files consisting of 6 total RNA-seq samples and 9 ATAC samples, for ChIP there are 2 H3K4me3 samples, 2 H3K27ac samples along with 2 paired input samples. The samples were sequenced on the Illumina HiSeq4000 amd Illumina NextSeq500 platforms.

  Dataset EGAD50000000984

• cfDNA sWGS BAM — Healthy donors

  Paired-end shallow whole-genome sequencing (sWGS) of plasma cfDNA from healthy donors recruited at EFS Montpellier (France). Libraries: DSP (NEBNext Ultra II) and/or SSP (IDT/Swift Accel-NGS 1S Plus). Sequencing by IntegraGen (France, 2022–2024). Reads aligned to GRCh38 (hg38 canonical) with BWA-MEM; BAMs coordinate-sorted and indexed; submission contains primary mapped reads only (unmapped/secondary/supplementary/duplicates removed; typical filter “-F 3332”, MAPQ threshold as noted in analysis records). Each BAM/BAI pair is linked to its Sample and Experiment. Data Use: controlled access.

  Dataset EGAD50000001880

• Whole-genome and single-cell sequencing of lethal metastatic castration resistant prostate cancer

  Contains a total of 9 patients with whole-genome sequencing of 30 tumours or germline samples, 10X single-cell multiome RNA-seq + ATAC-seq of 24 tumours, 10X 5' single-cell RNA-seq of 10 tumours, and snPATHO-seq of 2 primary tumours. All single-cell experiments have matched whole-genome sequencing. When matched tumour (and germline) whole-genome sequencing is missing, it can be found under EGAS00001006598.

  Dataset EGAD50000001869

• ovarian cancer sample exome seq

  Tissue samples were collected from 18 ovarian cancer patients. After mechanical and enzymatic dissociation, a part of these tissues underwent growth factoromics analysis. For four cases, single-cell sequencing analysis was conducted under conditions with or without estradiol. Additionally, the remains of the tissues were frozen and subsequently subjected to exome sequencing on 16 of these samples. Sixteen cases were diagnosed as HGSOC, while two samples were clear cell or mucinous type (Extended Data Table 1). All cases were grade 3, and none had received neoadjuvant chemotherapy.

  Dataset EGAD50000002057

• Epigenome mapping in purified cells of the digestive and urogenital organs: Participation in International Human Epigenome Consortium (IHEC) (RNA-Seq)

  The aim of this study is to participate in International Human Epigenome Consortium (IHEC, http://ihec-epigenomes.org/), through disclosure of quality epigenome profiles of various normal cell lineages and diseased cells purified from surgically resected materials of multiple Japanese people.

  Study JGAS000028

• single-cell RNA sequencing and RNA sequencing of normal uterine cervix

  We designed this study to identify molecular markers of HPV18 early promoter. In this study, we developed patient-derived organoids from squamous-columular junction of normal uterine cervix and infected HPV18 into those organoids to elucidate the molecular biological characteristics of HPV18-activated cells.

  Study JGAS000640

• Epigenome mapping in purified cells of the digestive and urogenital organs: Participation in International Human Epigenome Consortium (IHEC) (WGBS)

  The aim of this study is to participate in International Human Epigenome Consortium (IHEC, http://ihec-epigenomes.org/), through disclosure of quality epigenome profiles of various normal cell lineages and diseased cells purified from surgically resected materials of multiple Japanese people.

  Study JGAS000026

• Epigenome mapping in purified cells of the digestive and urogenital organs: Participation in International Human Epigenome Consortium (IHEC) (ChIP-seq)

  The aim of this study is to participate in International Human Epigenome Consortium (IHEC, http://ihec-epigenomes.org/), through disclosure of quality epigenome profiles of various normal cell lineages and diseased cells purified from surgically resected materials of multiple Japanese people.

  Study JGAS000027

• Metagenomic data of patients with bipolar disorder or schizoprhenia spectrum disorder

  Metagenomic data of stool samples from 111 patients with either bipolar disorder or schizophrenia spectrum disorder. Data includes fq.gz files, mostly 1 forward and 1 reverse read per sample. Samples analysed in multiple lanes have 4 (paired) fastq files, which will have the same patient ID (example: 048_1 and 048_2, or 088_L1 and 088_L2. DNA was extracted from using the QIAamp Fast DNA Stool Mini Kit (Qiagen) following the manufacturer’s instructions. Shotgun metagenomic sequencing was carried out using llumina Novaseq 6000.

  Dataset EGAD50000001414

• Whole-exome sequencing and RNA-seq of paired normal-tumour samples from MMR-proficient early-onset colorectal cancer patients

  This dataset contains fastq files obtained from paired samples of normal colonic mucosa and tumour tissue, collected from 19 patients diagnosed with MMR-proficient colorectal cancer at an age of 50 years or younger. It includes both whole-exome sequencing and RNA-seq data, generated on the Illumina HiSeq 3000 platform. Additionally, the dataset includes phenotypic information from the patients.

  Dataset EGAD50000001844

• snRNA-seq data of 11 regionally sampled GBM tissue for 4 patients

  This dataset contains snRNA-seq data of 11 regionally sampled GBM tissue (peritumoral region, tumor edge, and tumor core). Regionally sampled GBM patient tissue was dissociated and nuclei were processed in an unbiased manner without any sorting procedure. Nuclei were dissociated from frozen tissue using Chromium Nuclei Isolation Kit. Nuclei barcoding, cDNA preparation, and library construction were performed following the Evercode WT or WT mini User Manual, by combinatorial barcoding to assign a unique barcode to each cell.

  Dataset EGAD50000000778

• Enzymatic methylation sequencing of rectal mucus from patients suspected to have colorectal cancer

  This dataset contains comprises enzymatic methylation sequencing (EM-Seq) data generated from 208 rectal mucus samples from patients suspected to have colorectal cancer and healthy controls. Targeted CpG sites were enriched using a panel of genes implicated in colorectal cancer, and libraries were prepared with NEBNext EM-Seq prior to Illumina sequencing. Sequencing was performed on the Illumina NovaSeq6000 platform, producing paired-end FASTQ files. These data support analyses of DNA methylation patterns in CRC detection and progression

  Dataset EGAD50000001871

• Error-corrected targeted sequencing of rectal mucus from patients suspected to have colorectal cancer

  This dataset contains error-corrected next-generation sequencing (ecNGS) data generated from 161 rectal mucus samples. These samples belong to colorectal cancer, adenoma, and control participants. Genomic DNA was extracted and processed using a hybrid-capture panel targeting 50 colorectal cancer–associated genes, incorporating unique molecular identifiers (UMIs) for duplex consensus error correction. Libraries were sequenced on an Illumina NovaSeq 6000 (paired-end 150 bp) producing paired-end FASTQ files. These data support analyses of DNA mutations in CRC detection and progression.

  Dataset EGAD50000002032

• Comparison of HCC cell lines and primary HCCs

  There are concerns about whether cancer cell lines could faithfully represent the matched primary cancer cells. Comparison of the HCC cell lines and primary HCCs demonstrated that, during long-term in vitro culture, cell lines retain the genetic landscape of the matched primary HCCs.

  Study EGAS00001001678