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• Breast Cancer TNBC Single-Cell RNA-Seq Dataset

  Single-cell RNA-Sequencing of five TNBC primary breast cancers from Wu et al. (2020) EMBO J study. Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq 500 platform.

  Dataset EGAD00001006981

• Martin_Ravenscroft-AUS1

  Whole genome sequencing data on D19-0702 (AUS1), presented in Martin et al. 2020 (AUS1). WGS (Illumina HiSeq) was performed at Kinghorn Centre for Clinical Genetics, Garvan Institute of Medical Research. Data was analyzed using the Seave bioinformatic analysis pipeline (https://www.seave.bio).

  Dataset EGAD00001006276

• DKFZ-St.Jude Medulloblastoma - 9 SJMB tumor/control pairs, exome

  9 tumor/control exomes, SJMB samples, sequenced on Illumina machines from the paper "Germline Elongator mutations in Sonic Hedgehog medulloblastoma" (Waszak et al. 2020 Nature).

  Dataset EGAD00001006663

• Whole exome sequencing data for patients with Bosma arhinia microphthalmia syndrome (BAMS)

  Whole exome sequencing data for patients with Bosma arhinia microphthalmia syndrome (BAMS). The dataset includes 21 samples from 7 families with BAMS; see Gordon et al, Nature Genetics, 2017.

  Dataset EGAD00001003130

• exercise cfChIP-seq

  cfChIP-seq using an H3K4me3-specific antibody of 9 plasma samples collected after marathon run. cfChIP-seq was performed as described in Sadeh et al. 2021. dataset contains paired-end fastq files and BAM files of raw sequencing data.

  Dataset EGAD00001010284

• Institut Curie Neuroblastoma Whole Genome Sequencing Diagnosis Relapse

  Neuroblastoma, a clinically heterogeneous pediatric cancer, is characterized by distinct genomic profiles but few recurrent mutations. As neuroblastoma is expected to have high degree of genetic heterogeneity, study of neuroblastoma's clonal evolution with deep coverage whole-genome sequencing of diagnosis and relapse samples will lead to a better understanding of the molecular events associated with relapse. Samples were included in this study if sufficient DNA from constitutional, diagnosis and relapse tumors was available for WGS. Whole genome sequencing was performed on trios (constitutional, diagnose and relapse DNA) from eight patients using Illumina Hi-seq2500 leading to paired-ends (PE) 90x90 for 6 of them and 100x100 for two. Expected coverage for sample NB0175 100x100bp was 30X for tumor and constitutional samples. For the seven other patients expected coverage was 80X for tumor samples with PE 100x100, 100X in the other tumor samples and 50X for all constitutional samples (see table 1). Following alignment with BWA (Li et al., Oxford J, 2009 Jul) allowing up to 4% of mismatches, bam files were cleaned up according to the Genome Analysis Toolkit (GATK) recommendations (Van der Auwera et al., Current Protocols in Bioinformatics, 2013, picard-1.45, GenomeAnalysisTK-2.2-16). Variant calling was performed in parallel using 3 variant callers: GenomeAnalysisTK-2.2-16, Samtools-0.1.18 and MuTect-1.1.4 (McKenna et al., Genome Res, 2010; Li et al., Oxford J, 2009 Aug; Cibulskis et al., Nature, 2013). Annovar-v2012-10-23 with cosmic-v64 and dbsnp-v137 were used for the annotation and RefSeq for the structural annotation. For GATK and Samtools, single nucleotide variants (SNVs) with a quality under 30, a depth of coverage under 6 or with less than 2 reads supporting the variant were filter out. MuTect with parameters following GATK and Samtools thresholds have been used to filter our irrelevant variants. .SNVs within and around exons of coding genes overlapping splice sites.. Then,variants reported in more than 1% of the population in the 1000 genomes (1000gAprl_2012) or Exome Sequencing Project (ESP6500) have been discarded in order to filter polymorphisms. Finally, synonymous variants were filtered out. MuTect focuses on somatic by filtering with constitutional sample. Mpileup comparison between constitutional and somatic DNAs allowed us to focus also on tumor specific SNVs with GATK and Samtools. Finally, every SNV called by our pipeline and also supported in any constitutional samples were filtered our in order to prevent putative constitutional DNA coverage deficiency. Then we analyzed CNVs (copy number variants) with HMMcopy-v0.1.1 (Gavin et al., Genome Res, 2012) and control-FREEC-v6.7 (Boeva et al., Bioinformatics 2011) with a respective window of 2000bp and 1000 bp, and auto-correction of normal contamination of tumor samples for Control-FREEC. Finally we explored Structural variants (SVs) including deletions, inversions, tandem duplications and translocations using DELLY-v0.5.5 with standard parameters (Rausch et al., Oxford J, 2012). In tumors, at least 10 supporting reads were required to make a call and 5 supporting reads for the sample NB0175 with a coverage of only 40X (see table 2). To predict SVs in constitutional samples for subsequent somatic filtering, only 2 supporting reads were required in order not to miss one. To identify somatic events, all the SVs in each normal sample were first flanked by 500 bp in both directions and any SVs called in a tumor sample which was in the combined flanked regions of respective normal sample was removed (see graph 1). Deletions with more than 5 genes impacted or larger than 1Mb and inversions or tandem duplications covering more than 4 genes, were removed. We focused on exonic and splicing events for deletions, inversions, and tandem duplications. For translocation, we keep all SVs that occurred in intronic, exonic, 5'UTR, upstream or splicing regions. Bioinformatics detection of variations with Deep sequencing approach Once PE reads merged and adaptors trimmed by SeqPrep with default parameters, merged reads were aligned via the BWA (Li H. and Durbin R. 2009 PMID 19451168) allowing up to 1 differences in the 22-base-long seeds and reporting only unique alignments. Only reads having a mapping quality 20 or more have been further analysed. Variant calling software was not used, since we aimed to predict variations at low frequencies, observed in less than 1% of reads. Such variants require a custom approach. Using DepthOfCoverage functions of the Genome Analysis Toolkit (GATK) v2.13.2 (McKenna A, et al., 2010 Genome Research PMID: 20644199), we focused on high quality coverage of bases A, C, G and T at the targeted variant position. Depth of coverage of each base following a mapping quality higher than 20 and a base quality higher than 10 have been taken into account in order to focus only on high quality data. Aiming to determine the background level of variability at the studied regions, 10 control samples were included in the analysis. The same approach and filtering criteria have been applied as introduced above over the entire amplicons. In order to highlight variants, for each sample the frequencies of each bases at each amplicon position were then compared to those observed in the set of controls. Statistical analyses were performed with the R statistical software (http://www.R-project.org). Fisher’s exact two-sided tests with a Bonferroni correction were performed to compare percentages of bases between the data sets, i.e. for a given base between a case and the controls. Finally, significant variations were filtered-in once (i) a significant increase in the percentage of avariant base and (ii) a significant decrease in the percentage of it's reference base following our p.values criteria was observed (p.val < 0.05).

  Dataset EGAD00001001356

• International Multi-Center ADHD Gene Project (IMAGE) II Case Sample

  This sample represents a collection of cases across a range of sites. All of these samples were ascertained for ADHD with most meeting criteria for combined type ADHD. The collection sites span Europe and America. Further details on the source and inclusion and exclusion information can be found in Neale et al. "Case-Control Genome-Wide Association of Attention-Deficit / Hyperactivity Disorder" J Am Acad Child Adolesc Psychiatry. 2010 September; 49(9): 906-920 PMID20732627. For online access to this manuscript see: PMC2928577.

  Study phs000407

• Endometrial Cancer Association Consortium - OncoArray Genotypes

  The data come from 9 studies participating in the Endometrial Cancer Association Consortium (ECAC). ECAC data included in this dbGaP submission include OncoArray data from 4486 endometrial cancer cases genotyped at the Center for Inherited Disease Research (CIDR). Samples were genotyped using the Illumina OncoArray beadchip 500K SNP custom array. Details of the genotyping process and sample selection are included in O'Mara et al, Identification of nine new susceptibility loci for endometrial cancer. Nature Communications (PMID:30093612).

  Study phs001885

• Genome-Wide Analysis of Aberrant Position and Sequence of Plasma DNA Fragment Ends in Patients With Cancer

  This study investigated fragmentation characteristics in plasma cell-free DNA and evaluated the relevance of such analysis for cancer diagnostics. Specifically, we performed whole genome sequencing (WGS) of healthy individuals and cancer patients, and evaluated genome-wide coverage and sequences surrounding fragment ends. This was a retrospective study of archived plasma samples. The WGS data are available through this submission. Detailed study design and findings were published in Budhraja et al. Science Translational Medicine 2023.

  Study phs003170

• Testicular large B-cell lymphoma is genetically similar to PCNSL and distinct from nodal DLBCL

  Next generation sequencing (NGS) data analyzed in Rivas-Delgado, López, et al. HemaSphere 2024. Single nucleotide variants (SNVs) and short insertions /deletions (indels) were evaluated in 42 samples using a B-cell malignancy-oriented targeted panel covering 115 genes (SureSelectXT, Agilent Technologies and sequenced in a MiSeq instrument (Illumina; 2x150bp; mean coverage of 599x). The NGS data of 39 samples is provided here. The 3 additional samples were previously published. Please, see the original publication for additional details.

  Study EGAS50000000521

• Expression quantitative trait loci influence DNA damage-induced apoptosis in cancer

  The analysis of e2QTL allows for the identification of context-specific eQTL effects (Kim-Hellmuth et al. (2017), PMID: 28814792). To evaluate how inter-individual genetic variability influences the regulation of DNA damage-induced apoptosis, we performed e2QTL analysis of CD8+ T cells from 461 healthy European participants stimulated with high doses of 5 different carcinogens. These include Methyl-methanesulfonate (MMS), tert-butyl-hydroperoxide (TBOOH), benzo(a)pyrene-7,8-diol-9,10-epoxide (BPDE), 4-hydroxycyclophosphamide (HC) and UVC radiation.

  Study EGAS50000000666

• Clonal_expansion_of_mutated_cell_population_in_bladder_urothelium_

  1cm biospies of from patients undergoing bladder cystectomy will be collected. The underlying muscle and stroma will be removed and the remaining epithelia dissected into small sequential areas which will be sent for ultra-deep exome sequencing using a panel of known cancer and viral genes. Sequence analysis using similar methods to Martincorena I et al (Science 2015, 348:880) will provide an idea of the somatic mutational landscape in these patient samples. Individual patient muscle samples will also be sequenced as a reference.

  Study EGAS00001001687

• Colorectal adenomas and carcinomas NKI-AvL TGO series Gut2009

  Colorectal cancer (CRC) develops from normal epithelium, through a benign precursor lesion called adenoma, by accumulation of genetic alterations affecting oncogenes and tumour suppressor genes. These CRC-related genomic alterations often result in specific RNA-expression patterns. RNA isolated from fresh frozen specimen of 32 adenomas and 29 CRCs was obtained from the previously published study (Carvalho et al. 2009) and RNA-sequencing was performed with the aim to examine RNA expression changes in colorectal adenoma-to-carcinoma progression.

  Study EGAS00001002758

• WES sequencing of malignant peripheral nerve sheath tumours

  Investigate the genomic landscape of MPNST tumours. Cases where a diagnosis of MPNST had been considered in the differential diagnosis with a variable degree of confidence was included. The diagnosis of MPNST was excluded in 12/51 (49 cases had WES data) of the sequenced tumours following review of the pathology, new IHC, molecular tests and clinical information. Article: Lyskjær et al, 2020, J Pathol, "H3K27me3 expression and methylation status in histological variants of malignant peripheral nerve sheath tumours".

  Study EGAS00001004527

• Novel PARN mutations in Hoyeraal-HReidarsson syndrome patients.

  The deposited data correspond to the sequences of PARN gene obtained by whole exome sequencing in two patients carrying mutations in this gene as described in Benyelles et al. EMBO Mol Med 2019.. PARN is a poly(A)-specific ribonuclease that regulates the turnover of mRNAs and the maturation and stabilization of the hTR RNA component of telomerase. Data represent Sequences with PARN mutations that have been identified in two patients with Høyeraal-Hreidarsson (HH) syndrome, a rare telomere biology disorder.

  Study EGAS00001003623

• Blood RNA-seq from Mexican DMD patients and healthy controls

  These data are part of a study that aimed to assess the potential of blood RNA-seq to track non-invasively disease progression for DMD. In particular, here we focus on a study designed to compare gene expression in blood from DMD patients to that of healthy controls. These data are analysed in the following manuscript: Signorelli, M., Ebrahimpoor, M. et al. (accepted). Peripheral blood transcriptome profiling enables monitoring disease progression in dystrophic mice and patients. To appear in EMBO Molecular Medicine.

  Study EGAS00001004907

• Transcriptome profiling of three giant cell tumour of bone cell lines

  Transcriptome profiling of three established giant cell tumour of bone (GCTB) cell lines on the BGISEQ-500 platform (PE100). Cell lines consist of neoplastic "stromal" cells harboring a heterozygous H3F3A p.G34W mutation. Data was used in a study (Venneker et al., Histone deacetylase inhibitors as a therapeutic strategy to eliminate neoplastic “stromal” cells from giant cell tumors of bone, 2022) to determine H3F3A-WT/MT expression ratios, to identify pathogenic variants, and to examine the expression levels of epigenetic modifiers and regulators.

  Study EGAS00001006441

• Genome-wide SNP data of Fulani populations from the Sahel belt

  This project seeks to uncover the genetic structure and ancestry of the Fulani populations across the Sahel/Savannah Belt. The study includes two datasets (''WGA_Fulani_Database'' and ''Saliva_Fulani_Ddatabase''), and SNP-genotyping data was generated using the Illumina Infinium H3Africa Consortium array (BeadChip type: H3Africa_2019_20037295_B1) in two genotyping batches (TE-2559_201029 and TE-2559_201020). In total, 419 were genotyped for this study and the key results were presented in Fortes-Lima et al. 2024 (doi: 10.1101/2024.06.22.600206).

  Study EGAS50000000451

• Genetic Discovery and Application in a Clinical Setting: Continuing a Partnership (eMERGE Phase II)

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of ten participating sites funded by the NHGRI to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 19,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. The eMERGE Network brings together researchers with a wide range of expertise in genomics, statistics, ethics, informatics, and clinical medicine from leading medical research institutions across the country. Each center participating in the consortium is uniquely situated to provide critical resources to this highly collaborative and productive network. Each site combines a biobank or study cohort with extensive genomic data and access to clinical data derived from electronic medical records. Sites are geographically dispersed and have diverse patient populations, including two sites focusing specifically on pediatrics. The eMERGE Network is comprised of 9 sites and one coordinating center. Each site maintains its own biorepository where DNA specimens are linked to phenotypic data contained within EMRs. Children's Hospital of Pennsylvania, PI: Hakon Hakonarson, MD, PhD Cincinnati Children's Medical Center with Boston Children's Hospital, PI: John Harley, MD, PhD - CCHMC and Ingrid Holm, MD, MPH - BCH Geisinger Health System, PI: David Carey, PhD and Marc Williams, MD Group Health Cooperative with University of Washington, PI: Eric Larson, MD, MPH - GHC and Gail Jarvik, MD, PhD - UW Marshfield Clinic with Essentia Institute of Rural Health, PI: Catherine McCarty, PhD, MPH - Essentia and Murray Brilliant, PhD - Marshfield Mayo Clinic, PI: Christopher Chute, MD, DrPH and Iftikhar Kullo, MD Icahn School of Medicine at Mount Sinai, PI: Erwin Bottinger, MD Northwestern University, PI: Rex Chisholm, PhD and Maureen Smith, MS Vanderbilt University, PI: Dan Roden, MD eMERGE Coordinating Center - Vanderbilt University, PI: Paul Harris, PhD Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of red cell indices, white blood count (WBC) differential, diabetic retinopathy, height, serum lipid levels, specifically total cholesterol, HDL (high density lipoprotein), LDL (low density lipoprotein), and triglycerides, and autoimmune hypothyroidism as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes.

  Study phs000948

• Discrepancies in Tumour Mutation Burden (TMB) reporting from sequential Endobronchial ultrasound trans bronchial needle aspiration (EBUS TBNA) samples within single lymph node stations

  Introduction: Tumour Mutation Burden (TMB) is a potential biomarker for immune therapies. Identifying factors that may affect TMB prediction is key to its utility as biomarker for treatment options. Here we investigated parameters that might affect TMB using cytology smears obtained from endobronchial ultrasound transbronchial needle aspiration (EBUS TBNA)-sampled malignant lymph nodes, which are often the only diagnostic samples collected in patients with advanced lung cancer. Methods: Individual Diff-Quik cytology smears were prepared for each needle pass from the same enlarged malignant mediastinal lymph nodes. DNA extracted from smears of each pass underwent sequencing using a large gene panel sequencing (TSO500, Illumina). TMB was estimated for each individual needle pass using the TSO500 Local App v. 2.0 (Illumina). Results: Twenty patients had two or more Diff-Quik smears (total 45 smears) which passed sequencing quality control. Average TMB for all smears was 8.7 ± 5.0 mutations per megabase (Mb). Sixteen of the 20 patients had paired samples with minimal differences in TMB score (average difference 1.3 ± 0.85). Paired samples from 13 patients had concordant TMB in terms of scores being below or above a threshold of 10 mutations/Mb. Samples from seven patients had discrepant TMB, with four cases in particular exhibiting an average difference of 11.3 ± 2.7 mutations/Mb. Factors affecting TMB calling involved tumour content of the samples, the amount of DNA used in sequencing as well as bone fide intra tumour heterogeneity between paired samples. Conclusions: TMB assessment is feasible from EBUS-TBNA derived Diff-Quik slides from a single needle pass. Repeated samples of one or more lymph nodes have minimal variation in TMB in most cases (80%), suggesting accuracy of the test overall. To enhance accuracy of the result, care should be taken in relation to the tumour content of the cytology smear. Further, clinicians should be aware that even slight differences in lymph node site selection could give rise to variable genomics and discrepant TMB due to tumour heterogeneity. Therefore, it would be reasonable for multi-lymph node site analysis of TMB to be performed using EBUS TBNA aspirates from different easily accessible lymph nodes. Further investigations need to determine whether these samples should be combined as one sample or tested separately.

  Study EGAS00001007484

• Single cell RNAseq FASTQ files of three PDAC organoid lines (P28, P40, P47) using SORT-seq

  This dataset contains raw FASTQ files from single cell RNA sequencing of three patient-derived pancreatic ductal adenocarcinoma (PDAC) organoid lines (P28, P40, P47) cultured under standard conditions. For each line, single cells were FACS sorted into 384-well capture plates, with each well containing a 50nl droplet of barcoded primers. Plates were processed following an adapted SORT-seq protocol, and cDNA libraries were generated using CEL-Seq2 with TruSeq small RNA primers (Illumina). Sequencing was performed on an Illumina NextSeq500 platform using paired-end reads (read 1: 26 cycles, index read: 6 cycles, read 2: 60 cycles). Data are controlled-access and intended for single cell transcriptomic analysis of PDAC organoid heterogeneity and WNT pathway activity.

  Dataset EGAD50000002220

• Ultra-long whole-genome and Cas9-targeted nanopore sequencing of fibroblasts: FSHD, BAMS, healthy controls

  Data from the D4Z4End2End study (Xiao et al. 2025), for genetic and epigenetic analysis of the D4Z4 macrosatellite in health and disease.

  Dataset EGAD50000001551

• DKFZ-St.Jude Medulloblastoma - 218 CEF control exomes

  218 control exomes, CEF cohort, sequenced on Illumina machines from the paper "Germline Elongator mutations in Sonic Hedgehog medulloblastoma" (Waszak et al. 2020 Nature).

  Dataset EGAD00001006659

• DKFZ-St.Jude Medulloblastoma - 70 PAN-GATC control exomes

  Exome controls for 70 individuals, PAN-GATC cohort, sequenced on Illumina machines from the paper "Germline Elongator mutations in Sonic Hedgehog medulloblastoma" (Waszak et al. 2020 Nature).

  Dataset EGAD00001006661

• Sequencing data for oesophageal / related samples - Foley, Shorthouse et al (RNA)

  Data supporting: "SMAD4 and KCNQ3 Alterations are Associated with Lymph Node Metastases in Oesophageal Adenocarcinoma" RNAseq (FASTQ files) 6 samples

  Dataset EGAD00001011065