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• Long read mRNA sequencing of human neural retinal samples

  Long-read (PacBio) RNA sequencing dataset of three neural retinal samples. Files are raw BAM format files generated by a Sequel II machine. Additionally, the ccs3 BAM format files are included.

  Dataset EGAD50000000101

• LCM isolated buccal epithelial cell WGS of chimeric twins

  Buccal epithelial cells of chimeric twins were isolated using laser-capture microdissection. Cells were pooled (13-60 cells) per batch to create a genomic DNA library using an NEB low-input kit.

  Dataset EGAD50000000131

• Pacbio_methylation_cases

  The PacBio cases dataset for differential methylation analysis included 10 case samples with known KMT2A loss-of-function variants, 2 VUS samples. All samples were sequenced using PacBio Revio® HiFi reads at a matched coverage of 15X, and methylation was called using pb-cpg-tools on aligned BAM files.

  Dataset EGAD00010002807

• Siberia.Pakendorf

  The tar archive contains a) the txt file with the genotypes, b) illumina annotation file with info on SNPs, c) sample info file unfiltered illumina data, autosomes only data from Pugach et al MBE 2016 The Complex Admixture History and Recent Southern Origins of Siberian Populations

  Dataset EGAD00010002304

• Signatures of mismatch repair deficiency in cancer genomes

  DNA replication errors occurring in mismatch repair (MMR) deficient cells persist as mismatch mutations and predispose to a range of tumors. Here, we sequenced the first whole-genomes from MMR-deficient endometrial tumors.

  Dataset EGAD00001000641

• Identification of drug resistance genes in melanoma by Exome Sequencing

  The cell lines in this study are a combination of internally sequenced (cosmic) and externally sequenced cell lines known to be “double-wild-type” (lacking BRAF and NRAS somatic mutations). These sequences were realigned in this data set for consistency.

  Dataset EGAD00001003260

• Whole Exome Data of RFWD3 Fanconi anemia patient

  Dataset contains one sample derived from gDNA of human fibroblasts. Files are in FASTQ format and were generated using the Agilent SureSelect Human All Exon 50Mb Kit and followed by Next Generation Sequencing on a HighSeq2000 instrument (Illumina).

  Dataset EGAD00001003311

• Whole exome sequencing of SU- DIPG-XIII from different sites

  Whole exome sequencing of diffuse intrinsic pontine glioma (DIPG) cells isolated from the pons and from a sub-ventricular zone site of spread within the frontal lobe from the same individual (SU- DIPG-XIII)

  Dataset EGAD00001003563

• SLC9A3R1 variant associated with age-related hearing loss

  The dataset contains one BAM file that includes a SLC9A3R1 variant identified in two Italian patients affected by age-related hearing loss. Data have been produced by targeted re-sequencing, using Ion Torrent PGM platform.

  Dataset EGAD00001004171

• Exome sequencing files for "Minimal functional driver gene heterogeneity among untreated metastases"

  Files from whole exome sequencing of 14 tumors from two cancer patients (endometrial and lung cancer) along with a matched normal tissue per patient.

  Dataset EGAD00001004212

• Whole exome sequencing of melanomas from a Braf mutant mouse model UV radiation study

  Whole exome sequencing of 41 melanomas and normal DNA from Braf mutant mice: 15 tumours from UV exposed mice, 15 tumours from non-exposed mice and 11 from UV exposed, sunscreen-protected mice.

  Dataset EGAD00001000775

• Whole Genome (WG) sequencing data files for H_NO-JB001

  DNA was derived from the primary tumour, lung metastasis, and peri-aortic lymph node metastasis. DNA from the spleen was used as a normal control.WG sequencing produced ~30-fold (primary tumour, spleen normal)-50-fold (lung metastasis) coverage

  Dataset EGAD00001001253

• mFAST-SeqS of plasma-DNA

  Sequence data from L1-amplicon libraries prepared from plasma-DNA from a set of 24 female controls and 18 male controls without malignant disease and samples from patients breast (n= 28) and prostate cancer patients (n=61).

  Dataset EGAD00001001314

• Longitudinal RNA-seq in a twin cohort

  The dataset consists of 232 RNA-seq samples (whole blood) obtained from healthy female from the TwinsUK adult registry cohort. The samples were obtained at two time points separated on average by 22 months.

  Dataset EGAD00001002068

• MGRB dataset. GATK joint called variants of 2570 phase 2 samples.

  This dataset comprises a 2572 sample joint called vcf from the Medical Genome Reference Bank.

  Dataset EGAD00001005228

• DESIGN-NKI low coverage WGS

  Low-coverage whole genome sequencing data of 37 HPV-negative HNSCC specimens from patients treated at The Netherlands Cancer Institute. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for WGS to a depth of approx. 0.5X.

  Dataset EGAD00001005718

• DESIGN-VUMC low coverage WGS

  Low-coverage whole genome sequencing data of 37 HPV-negative HNSCC specimens from patients treated at the VU Medical Centre, Amsterdam. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for WGS to a depth of approx. 0.5X.

  Dataset EGAD00001005719

• RNAseq data

  Whole genome transcriptome poly-A selected strand specific 100bp paired-end RNA sequencing of post-mortem brain tissue from prefrontal cortex and orbitofrontal cortex were performed. Brain tissue samples were collected from four different biobanks in England and USA.

  Dataset EGAD00001005948

• RNA-seq analysis of megakaryocytes and granulocytes in Quebec platelet disorder

  RNA-seq was performed on cultured megakaryocytes and peripheral-blood derived granulocytes from individuals with QPD and a unaffected controls. Each group consisted of n=3 biological replicates.

  Dataset EGAD00001006053

• plasma dna fragmentations

  Data were generated by next-generation sequencing (Illumina) in a fastq format. This dataset involved sequencing data from pregnant women and patients with hepatocellular carcinoma (HCC). For HCC samples, the paired buffy coat and tumor DNA tissue samples were also sequenced.

  Dataset EGAD00001006054

• Metastases of a cancer of unknown primary (CUP)

  RNA seq analysis of 6 CUP metastases (each in triplicate), analysed by paired sequencing with NextSeq 500. Whole exome sequencing of 15 CUP metastases, analysed by paired sequencing with NextSeq 500.

  Dataset EGAD00001005963

• Performance assessment of total RNA sequencing of human biofluids and extracellular vesicles

  SMARTer Stranded Total RNA-Seq method of human platelet-rich plasma, platelet-free plasma, urine, conditioned medium, and extracellular vesicles (EVs) from these biofluids. Including a titration experiment with spikes.

  Dataset EGAD00001006150

• Matched FF and FFPE WGS from a metastatic prostate tumor

  This dataset contains three bam files. One normal blood sample and two matched FF and FFPE samples from the same metastatic prostate tumor.

  Dataset EGAD00001006180

• Epigenetic subtypes of neuroblastoma - ChIPseq

  In the absence of recurrent gene mutations, evidence accumulates that epigenetic deregulation plays a prominent role in neuroblastoma biology. Here we provide genome wide H3K27ac profiles in 60 primary neuroblastoma samples.

  Dataset EGAD00001006285

• Dataset of whole-exome sequencing of clonally related neuroblastoma and teratoma

  This dataset includes bam files of WES of clonally related neuroblastoma and teratoma as well as peripheral blood samples as a control. Neuroblastoma and teratoma samples were formalin-fixed paraffin embedded.

  Dataset EGAD00001007039

• HV31 - MGI standard short-read sequencing

  MGI standard short-read sequencing data for individual HV31 generated using DNA from peripheral blood mononuclear cells, to a sequencing depth of ~57×.

  Dataset EGAD00001007044

• HV31 - MGI CoolMPS short-read sequencing

  MGI CoolMPS short-read sequencing data for individual HV31 generated using DNA from peripheral blood mononuclear cells, to a sequencing depth of ~57×.

  Dataset EGAD00001007048

• Fastq files from target enrichment

  Fastq files generated during target sequencing of 10MB genomic region surrounding top hits in GWAS in a subset of 86 individuals in case families. Paired end sequencing performed on Illumina NextSeq.

  Dataset EGAD00001007801

• Cancer RNA-seq consisting of FASTQ single-end reads from colon cancer sample

  Cancer RNA-seq consisting of FASTQ single-end reads from 1 colon-cancer individual RNA-seq was performed on illumina This dataset contains reads from a single region.

  Dataset EGAD00001007949

• Gut microbiota in prediabetes and diabetes

  Here, we profiled the gut microbiota in a discovery (n = 1,011) and validation (n = 484) cohort comprising Swedish subjects naive for diabetes treatment and grouped by glycemic status.

  Dataset EGAD00001006351

• Rucaparib in patients presenting a metastatic breast cancer

  Whole genome sequencing was performed on 24 patients (tumor DNA paired to constitutional DNA). WGS libraries were subjected to paired-end (2 x 100 bp) sequencing on NovaSeq (Illumina). The 96 files are in FASTQ format.

  Dataset EGAD00001006458

• Cram files for study entitled Compound heterozygous variants in LAMC3 in association with posterior periventricular nodular heterotopia

  This dataset contains three cram files for paired end sequencing of a trio, sequenced with Illumina Hiseq 2500

  Dataset EGAD00001006578

• Fibromyalgia versus small fiber neuropathy: Diverse keratinocyte transcriptome signature dataset

  We provide a diverse keratinocyte transcriptome signature between SFN and FMS patients, which may hint towards distinct pathomechanisms of small fiber sensitization and lay the basis for advanced diagnostics in both entities

  Dataset EGAD00001006918

• 16 Year Life History and Genomic Evolution of an ER+ HER2- Breast Cancer

  Case report of an ER+ Her2- breast cancer patient. Whole exome and transcriptome sequencing at time of diagnosis and relapse, targeted DNA sequencing of a liver met

  Dataset EGAD00001006365

• EPC (eccrine porocarcinoma) WES samples

  Whole-exome sequencing (WES) in a well-characterized sample of 14 matched EP tumour/healthy surrounding tissue samples. The sequencing was done with paired EXOME sequencing on Illumina HiSeq 4000 using Agilent SureSelect XT HS + Human All Exon V7.

  Dataset EGAD00001006395

• Cam_121 RNA-Seq data

  This dataset contains RNA-Seq data from 204 primary melanomas and 177 regional lymph nodes. More details can be found in the manuscript: "Tumour gene expression signature in primary melanoma predicts long-term outcomes: A prospective multicentre study"

  Dataset EGAD00001006401

• IVF Retrospective Study

  This dataset contains BAM files for 9 samples from individuals involved in a retrospective IVF trial. The BAM files were derived from whole genome sequencing. The 9 individuals consist of three trios containing mother, father and child samples.

  Dataset EGAD00001008147

• Osteosarcoma capture-based RNA sequencing

  This dataset includes 23 specimens from osteosarcoma patients (primary, relapsed, metastatic). It contains bam files from RNA sequencing using a library in which coding regions of cDNA are captured and short-read, paired-end sequencing.

  Dataset EGAD00001008434

• RaCHseq data

  Captured single-cell long-read data of a cohort of CLL patients receiving VEN treatment for resistance study

  Dataset EGAD00001008365

• FLTseq data

  Single-cell Long read data of a cohort of CLL patients receiving Venetoclax treatment for VEN resistance study.

  Dataset EGAD00001008367

• Whole exome sequencing

  reference whole exome sequence serving as a reference of individuals. Includes the fastq files of each individual (labelled S1-S5) and the called variants in vcf format merge for all individuals.

  Dataset EGAD00001008728

• Characterizing the tumor immune microenvironment of ependymomas using targeted gene expression profiles and RNA-sequencing the pros and cons

  RNA sequencing data of a collection of 6 pediatric ependymoma cases

  Dataset EGAD00001009301

• Neuroblastoma deep-sequencing dataset part 1

  Dataset including paired tumor-normal whole-genome deep-sequenced samples from 18 neuroblastoma patients (part1 of a total of 36 patients).

  Dataset EGAD00001010059

• Towards standardized whole exome sequencing (WES) for cancer patients: lessons from a multicentric pilot study

  ZPM WES Pilot consisting of 30 samples paired tumor/normal analyzed with WES at four different laboratories in Germany.

  Dataset EGAD00001011087

• Illumina genome sequencing data for HICF2 craniosynostosis families (Genome Medicine)

  BAM files for two families recruited to the HICF2 genome sequencing project due to craniosynostosis. One family is a singleton and the other is an affected mother-daughter duo.

  Dataset EGAD00001011373

• RNA sequencing data from the lungNENomics project

  Raw RNA-seq data from the lungNENomics cohort, comprising 319 pairs of FASTQ files from 179 individuals. This dataset also includes a gene count matrix, gene TPM matrix and gene expression VST matrix from 244 samples.

  Dataset EGAD00001015524

• A study of the immune system in patients with peripheral inflammatory neuropathy (CIDP): RNA adult (2025-10-02)

  Leveraging single-cell sequencing technologies to shed light on the immune aetiology of chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) . This dataset contains all the data available for this study on 2025-10-02.

  Dataset EGAD00001015725

• NHLBI Cleveland Family Study (CFS) Candidate Gene Association Resource (CARe)

  The Cleveland Family Study is the largest family-based study of sleep apnea world-wide, consisting of 2284 individuals (46% African American) from 361 families studied on up to 4 occasions over a period of 16 years. The study was begun in 1990 with the initial aims of quantifying the familial aggregation of sleep apnea. NIH renewals provided expansion of the original cohort (including increased minority recruitment) and longitudinal follow-up, with the last exam occurring in February 2006. Index probands (n=275) were recruited from 3 area hospital sleep labs if they had a confirmed diagnosis of sleep apnea and at least 2 first-degree relatives available to be studied. In the first 5 years of the study, neighborhood control probands (n=87) with at least 2 living relatives available for study were selected at random from a list provided by the index family and also studied. All available first degree relatives and spouses of the case and control probands also were recruited. Second-degree relatives, including half-sibs, aunts, uncles and grandparents, were also included if they lived near the first degree relatives (cases or controls), or if the family had been found to have two or more relatives with sleep apnea. Blood was sampled and DNA isolated for participants seen in the last two exam cycles (n=1447). The sample, which is enriched with individuals with sleep apnea, also contains a high prevalence of individuals with sleep apnea-related traits, including: obesity, impaired glucose tolerance, and HTN. Phenotyping data have been collected over 4 exam cycles, each occurring ~every 4 years. The last three exams targeted all subjects who had been studied at earlier exams, as well as new minority families and family members of previously studied probands who had been unavailable at prior exams. Data from one, two, three and four visits are available for 412, 630, 329 and 67, participants, respectively. In the first 3 exams, participants underwent overnight in-home sleep studies, allowing determination of the number and duration of hypopneas and apneas, sleep period, heart rate, and oxygen saturation levels; anthropometry (weight, height, and waist, hip, and neck circumferences); resting blood pressure; spirometry; standardized questionnaire evaluation of symptoms, medications, sleep patterns, quality of life, daytime sleepiness measures and health history; venipuncture and measurement of total and HDL cholesterol. The 4th exam (2001-2006) was designed to collect more detailed measurements of sleep, metabolic and CVD phenotypes and included measurement of state-of-the-art polysomnography, with both collection of blood and measurement of blood pressure before and after sleep, and anthropometry, upper airway assessments, spirometry, exhaled nitric oxide, and ECG performed the morning after the sleep study. Data have been collected by trained research assistants or GCRC nurses following written Manuals of Procedures who were certified following standard approaches for each study procedure. Ongoing data quality, with assessment of within or between individual drift, has been monitored on an ongoing basis, using statistical techniques as well as regular re-certification procedures. Between and within scorer reliabilities for key sleep apnea indices have been excellent, with intra-class correlation coefficients (ICCs) exceeding 0.92 for the apnea-hypopnea index (AHI). Sleep staging, assessed with epoch specific comparisons, also demonstrate excellent reliability for stage identification (kappas>0.82). There has been no evidence of significant time trends-between or within scorers- for the AHI variables. We also have evaluated the night-to-night variability of the AHI and other sleep variables in 91 subjects, with each measurement made 1-3 months apart. There is high night to night consistency for the AHI (ICC: 0.80), the arousal index (0.76), and the % sleep time in slow-wave sleep (0.73). We have demonstrated the comparability of the apnea estimates (AHI) determined from limited channel studies obtained at in-home settings with in full in-laboratory polysomnography. In addition to our published validation study, we more recently compared the AHI in 169 Cleveland Family Study participants undergoing both assessments (in-home and in-laboratory) within one week apart. These showed excellent levels of agreement (ICC=0.83), demonstrating the feasibility of examining data from either in-home or in-laboratory studies for apnea phenotyping. Data collected in the GCRC were obtained, when possible, with comparable, if not identical techniques, as were the same measures collected at prior exams performed in the participants' homes. To address the comparability of data collected over different exams, we calculated the crude age-adjusted correlations ~3 year within individual correlations between measures made in the most recent GCRC exam with measures made in a prior exam and demonstrated excellent levels of agreement for BMI (r=.91); waist circumference (0.91); FVC (0.88); and FEV1 (0.86). As expected due to higher biological and measurement variability, 149 somewhat lower 3-year correlations were demonstrated for SBP (0.56); Diastolic BP (0.48); AHI (0.62); and nocturnal oxygen desaturation (0.60). NHLBI Candidate-gene Association Resource. The NHLBI initiated the Candidate gene Association Resource (CARe) to create a shared genotype/phenotype resource for analyses of the association of genotypes with phenotypes relevant to the mission of the NHLBI. The resource comprises nine cohort studies funded by the NHLBI: Atherosclerosis Risk in Communities (ARIC), Cardiovascular Health Study (CHS), Cleveland Family Study (CFS), Coronary Artery Risk Development in Young Adults (CARDIA), Cooperative Study of Sickle Cell Disease (CSSCD), Framingham Heart Study (FHS), Jackson Heart Study (JHS), Multi-Ethnic Study of Atherosclerosis (MESA), and the Sleep Heart Health Study (SHHS). A database of genotype and phenotype data will be created that includes records for approximately 50,000 study participants with approximately 50,000 SNPs from more than 1,200 selected candidate genes. In addition, a genome wide association study using a 1,000K SNP Chip will be conducted on approximately 9,500 African American participants drawn from the 50,000 participants in the nine cohorts. Some relevant CARe publications CARe Study: PMID 20400780 CVD Chip Design: PMID 18974833

  Study phs000284

• National Cancer Institute (NCI) Genome Wide Association Study (GWAS) of Lung Cancer in Never Smokers

  A genomewide study of lung cancer in never smokers Abstract and specific aims In the United States, lung cancer incidence and mortality rates have been steadily declining over the past decade, following decline in the prevalence of tobacco smoking. However, lung cancer remains the leading cause of cancer death, killing more patients than breast, colon, and prostate cancers combined. Although tobacco smoke is the predominant risk factor for development of lung cancer, some patients develop the disease without a history of tobacco smoking. About 10 - 15% of all lung cancers occur in lilfetime never smokers. This figure will increase as the proportion of never smokers increases in the population. Even at present rates, lung cancer in never smokers, if considered a separate disease, is 6th to 8th top cause of cancer death. The growing number of never smokers in the USA and other countries emphasizes the importance of understanding the epidemiology and biology underlying lung cancer in this group. Genetic polymorphisms associated with the risk of lung cancer in never smokers are expected to overlap with those associated with the risk of lung cancer in ever smokers only partially. Epidemiological, molecular and clinical data suggest that molecular mechanisms of LC may differ in smokers and non-smokers, implying that lung cancer in never smokers is a different disease compared to the lung cancer in smokers. One can expect that there should be stronger genetic component in the control lung cancer in never smokers because effects of the genetic factors in never smokers are unmasked by the lack of tobacco smoke exposure. The genetic epidemiology of lung cancer in never smokers has not been well explored, largely because of difficulties in accruing the needed sample size for association studies. We propose a multicenter (total 14 sites from the US and Europe) genomewide association study of lung cancer in never smokers with the following specific aims: Aim 1: To identify candidate SNPs influencing risk for lung cancer in never smokers using Discovery sample. In the Discovery phase we will genotype 1256 Caucasian cases and 1365 age- and gender-matched never smoker controls using the Illumina Human660W-Quad platform. In addition, we will include in the analysis 284 cases and 175 matched controls already genotyped on the 610Quad platform. In this phase we will only include the study sites that have collected blood specimens (MDACC, Mayo Clinic, Karmanos Cancer Institute, The University of Liverpool Cancer research Centre, Institute of Cancer Research in Sutton, and Lunenfeld Research Institute in Toronto, Canada). All the samples will be sent to the independent lab for genotyping, to reduce site-specific technical artifacts. The final sample will consist of 1540 cases and 1540 controls matched by study site. Aim 2: To perform the second phase (validation) analysis of significant SNPs identified in aim 1 using an independent set of cases and controls. SNPs associated with risk at the significance level of 0.01 or below in the discovery set will be included in the replication phase. The proposed threshold guarantees an adequate power to retain SNPs with the typical effect size of 1.3. We plan to carry 6000-7000 SNPs for validation. The independent replication set will include 800 cases and 800 controls, mostly from sites that collected tissue (Mayo Clinic, Karmanos Cancer Institute, UT Southwestern) or buccal specimens (UCLA), but also blood samples (Imperial College London, University of Pennsylvana, German Cancer Research Center, Heidelberg, National Research Center for Environment and Health, Neuherberg, Carmel Medical Center, Haifa). We will then perform a joint analysis to test the significance of the SNPs identified in the first stage using a stringent critical p-value of 10-7. There will be 2340 cases and 2340 controls in the joint set. Based on our experience with GWAS in smokers and assuming that genetic component in lung cancer risk in never smokers can be higher than genetic component in smokers, we expect to identify about 5-10 candidate regions associated with lung cancer risk in never smokers. Aim 3: To identify and explore pathways associated with the risk of lung cancer in never smokers. Results of the number of studies on the molecular mechanisms and drug response suggest that lung cancer in never smokers is a different disease and different pathways will be associated with lung cancer risk in non-smokers and smokers. To identify pathways and molecular functions associated with lung cancer risk in never smokers we will apply Ingenuity and DAVID bioinformatics tools. We will use at least 300 top candidate genes identified in joint and discovery analysis. The reason why we select rather large number of candidate genes for functional annotation is two-fold: 1. Both algorithms are looking for enrichment of pathways and function by most significant genes and they produce statistically robust results only when number of genes is relatively high. 2. Despite the fact that this study will be largest possible for never smokers we still are underpowered to detect SNPs with relatively small effect size. But though those SNPs will not reach genome wide level of significance they will tend to be on the top of the list. In other words genes from the gray zone (significant on individual level and non-significant for genome wide level) are expected to be enriched by true discoveries. True discoveries are likely to be associated with limited number of pathways / functions while false positives are expected to be uniformly distributed across functions and pathways. Therefore significant clustering of the gene to a given function will suggest that that those genes are true discoveries. This is the first GWAS aiming at identifying the genetic control of susceptibility to lung cancer in Caucasian never smokers. We will combine the available resources from the multiple sites to achieve the sample size sufficient for this study. The study will identify genetic architecture of the predisposition to the lung cancer in never smokers.

  Study phs000634

• Rapid Acceleration of Diagnostics - Digital Health Technologies (RADx-DHT): NIH Digital Health Solutions for COVID-19: SAFER-COVID - Integration of Testing and Digital Health

  SAFER-COVID provides a set of self-management tools to consumers to track symptoms, test results, vaccine record, and environmental factors, such as exposure to others. Consumers may choose to integrate data from wearables and electronic health records (EHR) for self management. These data are used to enable multiple use cases such as return-to-work, activity risk assessment and self-management within SAFER-COVID.DOI: https://rapids.ll.mit.edu/10.57895/cmt5-gh78

  Study phs002540