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• A96199A

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96199A 843 samples; filetype=bam

  Dataset EGAD00001005348

• ESGI - Whole Genome Sequencing of samples from the Cilentogen isolates (2019-08-19)

  Deep whole genome sequencing of sampels from the Cilento isolates. The samples are sequenced using the Illumina HiSeq X Ten system. . This dataset contains all the data available for this study on 2019-08-19.

  Dataset EGAD00001005266

• 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

• RaCHseq data

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

  Dataset EGAD00001008365

• sWGS of cfDNA from metastatic CRC patients

  Paired end shallow whole genome sequencing (sWGS) data (FASTQ) for the identification of genomewide somatic copy number alterations (SCNA) and the estimation of tumor fractions.

  Dataset EGAD00001010293

• Exome chip data 943 PDAC cases and 3,908 controls

  The illumina exome chip genotyping data for 943 PDAC cases and 3,908 controls in the Chinese population. Genotypes were called by the Illumina GenomeStudio software, and the selected variants were re-called by zCall. Standard quality control were performed.

  Dataset EGAD00001004168

• 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

• RNAseq - Colorectal organoids and tumoroids (2017-05-04)

  Transcriptome sequencing of tumour tissue, adjacent normal tissue and derived organoids/tumoroids from colorectal cancer This dataset contains all the data available for this study on 2017-05-04.

  Dataset EGAD00001003320

• RNA-Seq files for SJHGG study

  RNA-Seq files accompanying the paper titled "Somatic Histone H3 Mutations in Diffuse Intrinsic Pontine Gliomas and Non-Brainstem Paediatric Glioblastomas".

  Dataset EGAD00001003905

• NKI-AvL CRC-OVC DNA-seq

  The dataset “NKI-AvL CRC-OVC DNA-seq" includes 4 normal and 4 tumor BAM files from paired-end whole exome sequencing on Illumina HiSeq2500 and Illumina NovaSeq6000 for 2 colorectal cancer and 2 ovarian cancer patients.

  Dataset EGAD00001004340

• Prognostic factors in prostate cancer: deep sequencing pilot project TAPG

  This is a pilot project to determine whether the TAPG FFPE DNA's are suitable for deep sequencing. If successful an investigation of SNP distribution in a larger cohort will follow.

  Dataset EGAD00001001237

• Integrative genomic study of CML patients

  This dataset contains WES and RNA-Seq fastq files for 65 CML patient samples at various stages of disease progression.

  Dataset EGAD00001004179

• 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

• NKI-AvL OpACIN RNA-seq of stage III melanoma patients

  The dataset “NKI-AvL OpACIN RNA-seq of stage III melanoma patients" includes 18 FASTQ files from single-end total RNA sequencing on Illumina HiSeq2500 for 18 stage III melanoma patients.

  Dataset EGAD00001004216

• 184-hTERT-L2 direct library preparation (DLP) single-cell genomes

  Microfluidic direct library preparation (DLP) single-cell whole-genome BAM files for near-diploid immortalized breast epithelial cell line 184-hTERT-L2.

  Dataset EGAD00001003152

• Mutational Analysis of Colorectal PDX models (2016-01-06)

  Targeted exome sequencing of patient derived xenografts from primary colorectal tumours and liver metastases. This dataset contains all the data available for this study on 2016-01-06.

  Dataset EGAD00001001872

• 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

• SA501X3F direct library preparation (DLP) single-cell genomes

  Microfluidic direct library preparation (DLP) single-cell whole-genome BAM files for third-passage patient-derived primary triple-negative breast cancer xenograft SA501X3F.

  Dataset EGAD00001003149

• SA501X4F direct library preparation (DLP) single-cell genomes

  Microfluidic direct library preparation (DLP) single-cell whole-genome BAM files for fourth-passage patient-derived primary triple-negative breast cancer xenograft SA501X4F.

  Dataset EGAD00001003150

• Mutational Analysis of Colorectal PDX models (2017-05-11)

  Targeted exome sequencing of patient derived xenografts from primary colorectal tumours and liver metastases. This dataset contains all the data available for this study on 2017-05-11.

  Dataset EGAD00001003334

• RNAseq following LDC67 or JQ1 treatment

  For RNA-Seq total RNA was isolated following LDC67 or JQ1 treatment. 3’RNAseq libraries were prepared with QUANT SEQ FWD 3´mRNA-Seq Kit (Lexogen, Austria), sequenced on an Illumina HiSeq 4000

  Dataset EGAD00001003464

• RRBS - MBD4-deficient AML

  Reduced Representation Bisulfite Sequencing for WEHI-AML-1 and WEHI-AML-2. RRBS libraries were made with the NuGEN Ovation RRBS Methyl-Seq System. Bisulfite conversion was performed with the Qiagen Epitect kit. Sequencing was performed on an Illumina HiSeq2500.

  Dataset EGAD00001003567

• WGS data of XPC-ko human small intestinal organoid cultures

  Whole genome sequencing (WGS) data of human small intestinal organoid cultures, which were deleted for the XPC gene using CRISPR-Cas9. Contains WGS data of 1 clone and 1 subclone.

  Dataset EGAD00001003779

• DEEP-IHEC-release-2017

  Raw data files for the German Epigenome Project (DEEP), IHEC/EpiRR submission of 2017. metadata available at: http://deep.dkfz.de/#/experiments

  Dataset EGAD00001003974

• TCS validation of 11 lung adenos

  This is the targeted capture deep sequencing (TCS) data for validation of the mutations discovered in the WES step. There are 58 bam files of TCS data including 48 tumor samples and 10 peripheral blood WBC samples.

  Dataset EGAD00001000985

• Meso RNA-seq data (SSA cell line study)

  RNA-seq data for mesothelioma cell lines after spliceostatin (SSA) or control (DMSO) treatment.

  Dataset EGAD00001001914

• Single-cell ATAC-Seq (Chromium Next GEM Single Cell ATAC kit v1.1 from 10X Genomics )

  Human data for chromatin accessibility (scATAC-Seq) in CD34+ B cell precursors.

  Dataset EGAD00001010910

• WGS Dataset of Organoid drug profiling identifies methotrexate as a therapy for SCCOHT, a rare pediatric cancer

  We generated and characterized tumoroids from small cell carcinoma of the ovary, hypercalcemic type. Furthermore, we identified a drug that is selective and effective against SCCOHT tumoroids.

  Dataset EGAD00001015442

• Additional RNAseq data of metastatic breast cancer samples

  Paired-end RNA sequencing data from 33 metastatic breast cancer samples. Sequencing was performed on the Illumina NovSeq 6000 using NEBNext Single Cell and Low Input RNA Library Prep Kit for Illumina.

  Dataset EGAD00001015678

• Genomic and transcriptomic determinants of host susceptibility, protection, and viral mutation in experimental SARS CoV-2 infection: RNA (2025-08-11)

  Single-cell profiling of healthy adult volunteers that were inoculated with SARS-CoV-2. . This dataset contains all the data available for this study on 2025-08-11.

  Dataset EGAD00001015679

• Dataset for ARHGAP11A knockout and control organoid cells.

  This dataset contains 9 RNA sequencing samples of hiPSC-derived forebrain organoid ARHGAP11A knockout and hiPSC-derived forebrain organoid control cells. Sequencing was performed on Illumina NovaSeq 6000. The sequencing was always paired.

  Dataset EGAD00001015706

• PRADO trial - Personalized response-directed surgery and adjuvant therapy after neoadjuvant ipilimumab plus nivolumab in stage III melanoma

  The dataset includes RNA sequencing data on PRE- treatment biopsies of lymph node metastasis (n=80) The technology used for sequencing is llumina HiSeq 2500

  Dataset EGAD00001012116

• Indonesian Genome Diversity Project 2

  This dataset reports whole genome sequences for 82 individuals from different populations from Mentawai, New Guinea, Sumatra and Sumba islands.

  Dataset EGAD00001005059

• H3Africa TrypanoGEN Main WGS

  This dataset is for TrypanoGEN Phase 1: Variant discovery, and includes 233 samples sequenced to approximately 10X coverage. Samples are from Guinea, Cote D’Ivoire, DRC and Uganda using Illumina HiSeq 2500.

  Dataset EGAD00001005076

• Chromatin Profiling in Twins (2019-08-21)

  50 lymphoblastoid cell lines of adult female Twins which are part of the MuTHER study, processed with the FAIRE assay in order to generate maps of open chromatin. . This dataset contains all the data available for this study on 2019-08-21.

  Dataset EGAD00001005273

• PETAL trial Whole Exome Sequencing (WES) from Tumor Samples

  Associated with Molecular profiling of DLBCL patients treated in the PETAL trial. Whole Exome Sequencing (WES) of 224 Tumor Samples. WES was used for Molecular Profiling by simultaneous screening of mutations. Data is Complementary to sWGS.

  Dataset EGAD50000001172

• A96146A

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96146A 1195 samples; filetype=bam

  Dataset EGAD00001005338

• A96172B

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96172B 1694 samples; filetype=bam

  Dataset EGAD00001005340

• A96199B

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96199B 1170 samples; filetype=bam

  Dataset EGAD00001005353

• A96211C

  Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A96211C 1397 samples; filetype=bam

  Dataset EGAD00001005354

• SF11956 snATAC Seq GBM

  IDHR132H Wildtype GBM. Male, 63 Single Nuclei ATAC seq data from high grade human glioma samples. NovaSeq6000 was used for ATAC seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005406

• SF9259S snRNA-Seq Primary GBM

  Single Nuceli Primary GBM 73 Male. Single Nuclei RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005428

• GATCI whole exome somatic variants (MuTect)

  Exome sequences were aligned to the GRCH38 reference genome. Aligned sequence was analyzed with GATK/MuTect, to generate somatic variant calls. Somatic variant calls are in VCF format. Details for the mutect call can be found in the vcf header.

  Dataset EGAD00001005917

• GATCI whole exome somatic variants (SomaticSniper)

  Exome sequences were aligned to the GRCH38 reference genome. Aligned sequence was analyzed with GATK/SomaticSniper, to generate somatic variant calls. Somatic variant calls are in VCF format. Details for the mutect call can be found in the vcf header.

  Dataset EGAD00001005918

• combined ChIPseq (H3, modifications and TF)

  ChIPseq data for 31 CLL samples (12 controls, 19 tumor) of the CancerEpiSys-PRECiSe project; containing histone H3, histone modifications and transcription factor binding sites (CTCF, EBF1).

  Dataset EGAD00001005969

• Mutation analysis using a custom SureSelect panel

  All baseline samples and when available EOT were processed for high-resolution mutation analysis. We designed a SureSelectXT-HS custom panel (Agilent) covering 68 genes with a total size of 260kb using the Agilent SureDesign platform.

  Dataset EGAD00001006386

• RNA sequencing data pre-treatment and post-treatment for NABUCCO cohort 1

  NABUCCO cohort 1 sequencing data. The dataset includes RNA sequencing pre-treatment on tumor samples (n=18) and RNA sequencing post-treatment on bladder tumor samples (n=18).

  Dataset EGAD00001006855

• Transcriptomic profiling of the Enteric Nervous System in Hirschsprung Disease (2025-07-31)

  This study involves atlasing the development of the postnatal gut nervous system in order to elucidate the pathogenic mechanisms of Hirschsprung disease. . This dataset contains all the data available for this study on 2025-07-31.

  Dataset EGAD00001015667

• MASS_Pilot: Muscle and Ageing Science Study - Collaboration with Wellcome Sanger Institute to characterise skeletal muscle ageing using Human Cell Atlas approaches (2025-07-31)

  Transcriptomic analysis of skeletal muscle, to understand the mechanisms of muscle ageing. . This dataset contains all the data available for this study on 2025-07-31.

  Dataset EGAD00001015670

• California Pacific Medical Center Research Breast Health Cohort

  Description of Cohort: The California Pacific Medical Center (CPMC) Breast Health Cohort is a cohort study based at CPMC and is linked to the San Francisco Mammography Registry, one of the sites of the NCI-funded Breast Cancer Screening Consortium (U01CA063740). CPMC is a community hospital in San Francisco, which has one of the highest volumes for mammography in San Francisco. Between September 2004 and June 2007, >90,000 mammograms were performed at CPMC. The CPMC breast health cohort collects demographic and risk factor data on women receiving mammography through participation in the San Francisco Mammography Registry, as part of the Breast Cancer Screening Consortium (U01CA063740). The SFMR database collects information from all sources, including a questionnaire on demographic and risk factor information, the clinical results of the breast examination, the measures of breast density by Dr. John Shepherd and the women who agreed to donate a blood sample. By merging these various sources of information we have very efficiently developed a large sample of women who have donated blood and have had a measure of mamographic density. Blood Collection: Dr. Steve Cummings is leading an effort to collect and archive blood samples from women who are receiving mammography screening. All women who are sent for a screening mammogram at CPMC are considered eligible. Since the cohort began collecting blood samples in July 2004 until June 2007, samples have been collected from over 11,000 women. Measurement of Breast Density: Dr. John Shepherd is currently measuring breast density in a large fraction of the cohort using an automated approach with single X-ray absorptiometry. Dr. Shepherd has established a link with the CPMC mammography center that allows him to collect routine digital mammography information. Using the data from the mammogram, Dr. Shepherd and his group have developed the single X-ray absorptiometry (SXA) technique for measuring density which is described in more detail below. The table demonstrates the distribution of demographic variables and some breast cancer risk factors of women who donated blood and had a breast density measurement in the CPMC breast health cohort. Nearly 80% of the participants are Caucasian and most of the women are post-menopausal with a median age of ~52. Since it will be difficult to accrue a large enough sample from each ethnic group, our study will focus only on Caucasian women. Table: Demographic variables, reproductive history and family history of breast cancer among 2962 women participating in the CPMC cohort study who contributed blood samples between 1994-1997. Variable Median/Percentage Age (Median/IQR) 52 (46-59) Ethnicity Caucasian/White 0.76 Asian/Pacific Islander 0.141 Hispanic 0.029 Mixed Race/Ethnicity 0.039 African American/Black 0.022 American Indian 0.001 Other 0.009 First degree relative with breast cancer 0.17 Age at first birth Nulliparous 0.39 Age<20 0.043 Age>40 0.032 Age<30, ≥20 0.251 Age>30, ≤40 0.282 Measurement of Breast Density in Cohort: Measurement of breast density is accomplished using an automated technique for all mammograms obtained by Dr. Shepherd using Single X-Ray absorptiometry (SXA). SXA measurement of breast density is done on approximately 30% of all screening mammograms. Below we describe the method for measurement of breast density by SXA by Dr. Shepherd's group and its validation and association with breast cancer. As we demonstrate below, breast density, as measured by SXA, is an automated, highly reproducible measure of the density of breast tissue and is associated with breast cancer risk. SXA for Quantifying Breast Density: Single x-ray absorptiometry (SXA) was initially developed for measuring bone density. SXA can determine the fraction of each of two densities simultaneously using the fact the sample is a constant thickness, the thickness in known, and the total attenuation is known. In applying this technique to breast density, we assume a two compartment model: fat and non-fatty (fibroglandular tissue). We use a reference material composed of various concentrations of two materials: one which is the same density as fat and another which is the same density as fibroglandular tissue. The reference material (phantom) is placed in the X-ray field with each mammogram. We have been able to implement this in a way that is unintrusive to the patient and technologist at CPMC. Assuming this two-compartment model and a constant known breast thickness, we can then calculate the percent density at any region of the breast based on the assumption that % pixel grey-scale is proportion to the mass fractions of breast fat and lean tissue. If reference materials (a phantom) of fat and fibroglandular tissue are imaged with the patient's breast and the reference materials have the same thickness as the patient's breast, then the breast's grey-scale values can be converted to fat/fibroglandular mass fractions by interpolating between those two references. The total percent density is found by averaging the volume fraction over all breast pixels. The phantom being used for breast density assessment at CPMC began to be used in September 2004. The phantom does not have to be manipulated by the technologist and stays attached on the mammography device during standard craniocaudal (CC) views. Thus it creates minimal to no interference with the clinical mammogram. Reproducibility of breast density measures: Traditional measures of mammographic density require some human interpretation. A human reader outlines the area perceived to be dense and a computer then calculates the percent area outlined as a percent of the entire image. Thus, while traditional mammographic density is associated with breast cancer risk, it has some limitations. In a study by Drs. Shepherd, Kerlikowske, et al., the correlation coefficient (Pearson's R) between different readers was 0.8-0.9. In contrast to the traditional mammographic density measurement, the SXA measurement is fully automated and, therefore, the reproducibility of the measurement is higher. Dr. Shepherd and colleagues have performed a replication study of SXA as a measurement of breast density. They have estimated the correlation coefficient of the SXA measurement of breast density to be >0.98. Thus, as expected for an automated measure, SXA is a highly reproducible measure of mammographic breast density. Drs. Shepherd and Kerlikowske have recently analyzed the association between breast cancer risk and breast density as measured by SXA (Shepherd et al., Cancer Epi Biomarkers and Prev, 2011, PMID: 21610220). They found that women in the highest quintile of % volumetric density had an odds ratio of 4.1 (95% CI: 2.3 - 7.2) for breast cancer risk compared to women in the lowest quintile of volumetric density. Thus volumetric density appears to be a highly reproducible, automated measure of breast cancer.

  Study phs000395