Search Results - EGA European Genome-Phenome Archive

Dataset developed for use with EOSC4Cancer of synthetic colorectal cancer tumor/normal pairs.

This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. The NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) was utilized to synthetize these 10 pairs of tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) were learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aimed to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient was down-sampled randomly, representing 50% germline variants of a given patient. These were mixed with the other 50% in silico germline variants that were modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learnt from a real CRC patient was added to the germline variant profile used for creating the normal synthetic WGS data of the same patient, consisting of the variants for tumor sample. Neither copy number profile nor structural variation profile was introduced into the tumor synthetic WGS data. Tumor content and ploidy were assumed to be 100% and 2, respectively. For mapping/variant detection, the Sarek pipeline v3.1.2 (https://nf-co.re/sarek/3.1.2) was used, specifically: 1. BWA v0.7.17-r1188 for read mapping 2. GATK v4.3.0.0 for pre-processing BAM file (including markduplicates and recalibration). 2. Mutect2 (GATK v4.3.0.0) for somatic variant calling 3. Strelka2 v2.9.10 for germline and somatic variant calling Metadata information of 10 CRC patients used for the generation of synthetic normal and tumor WGS data: Patient_id Tumor_barcode Normal_barcode Age Sex Tissue Cancer SIM007 SIM007_T SIM007_N 71 F Rectal Primary CRC SIM008 SIM008_T SIM008_N 45 F Colon Neuroendocrine Metastasis CRC SIM010 SIM010_T SIM010_N 62 M Colon Metastasis CRC SIM011 SIM011_T SIM011_N 55 M Colon Neuroendocrine Metastasis CRC SIM012 SIM012_T SIM012_N 57 M Rectal Metastasis CRC SIM013 SIM013_T SIM013_N 69 M Colon Metastasis CRC SIM014 SIM014_T SIM014_N 68 M Colon Neuroendocrine primary CRC SIM015 SIM015_T SIM015_N 58 F Colon Primary CRC SIM016 SIM016_T SIM016_N 49 M Colon/Rectal Primary CRC SIM017 SIM017_T SIM017_N 78 M Colon Neuroendocrine primary CRC

Dataset EGAD50000000564

Biomarkers for Noise-Induced Sleep Disruption

This project aims to investigate gene expression biomarkers for noise-induced sleep disruption and cognitive changes. The project is a sub-study, part of the larger ASCENT COE project 86 designed to test the ability of broadband pink noise to serve as a countermeasure for sleep loss from simulated aircraft noise. Gene expression research was sponsored by the Federal Aviation Administration.

Study phs003932

RNA004 DRS METTL5 variant of patient sample

This dataset contains RNA004 DRS data for a single patient sample with heterozygous inactivating SNPs for the methyltransferase METTL5. We provide 1) all reads in a GRCh38 aligned bam file + unaligned reads and 2) filtered reads just for the relevant part of chrR. Both bam files have been called with dorado 0.7.2 to contain the neccessary m6A methylation information. Companion datasets are 1) EGAS50000001201 for DRS of a healthy control with intact METTL5 and 2) PRJEB74238 for IVT 18S rRNA vector data as unmodified control. Please note, that both the companion datasets contain pod5 data, to faciliate re-basecalling.

Dataset EGAD50000001882

Capture-based Methods for Transcriptomic Profiling of FFPE material

This dataset includes all FASTQ files for 11 samples where different capture-based methods for transcriptome profiling have been tested. Specifically, we have the 'traditional' RNA-seq experiment with fresh frozen (FF) material, and 3 different capture methods for the matching formalin-fixed paraffin-embedded samples: Agilent (AGI), IDT, and Twist Biosciences (TBS). In total, there are 43 samples with paired-end FASTQ files (1 sample did not have sufficient material to test all methods). Samples are identified by the R01-R11 IDs with a suffix that indicates the capture method used (or FF for fresh frozen).

Dataset EGAD00001007655

March 2019 data update for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

March 2019 data update for cord blood CD34+CD38-, CMP, GMP, MEP, monocyte, erythroid precursor, B cell and primary AML total blasts reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium. This dataset contains data for samples: CEMT0158 CEMT0159 CEMT0160 CEMT0161 CEMT0162 CEMT0163 CEMT0164 CEMT0165 CEMT0166 CEMT0167 CEMT0168 CEMT0169 CEMT0170 CEMT0171 CEMT0172 CEMT0189

Dataset EGAD00001004950

Human Blastocyst 10X Single Cell RNA Sequencing

Recent advances in human blastoids have opened new avenues for modeling early human development and implantation. One limitation of our first protocol for human blastoid generation was relatively low efficiency. We now report an optimized protocol for the efficient generation of large quantities of high-fidelity human blastoids from naive pluripotent stem cells. This enabled proteomics analysis that identified phosphosite-specific signatures potentially involved in the derivation and/or maintenance of the signaling states in human blastoids. Additionally, we uncovered endometrial stromal effects in promoting trophoblast cell survival, proliferation, and syncytialization during co-culture with blastoids and blastocysts. Side-by-side single-cell RNA sequencing revealed similarities and differences in transcriptome profiles between pre-implantation blastoids and blastocysts, as well as post-implantation cultures, and uncovered a population resembling early migratory trophoblasts during co-culture with endometrial stromal cells. Our optimized protocol will facilitate broader use of human blastoids as an accessible, perturbable, scalable, and tractable model for human blastocysts. In addition, this study is the first to use the 10X Genomics platform for human blastocysts, and we anticipate this resource will be widely used by the community as a reference for future studies.

Study phs003122

noninvasive lung cancer subtyping

Accurate diagnosis of lung cancer is important for treatment decision-making. Currently, the gold standard for diagnosing histological subtypes of lung cancer relies on tumor biopsies. Recently, liquid biopsy, particularly cell-free DNA (cfDNA), has shown promising results in cancer detection and classification. In this study, we investigated the potential of cfDNA methylome for the noninvasive classification of lung cancer histological subtypes. Specifically, we focused on the two most prevalent lung cancer subtypes, lung adenocarcinoma and lung squamous cell carcinoma. Using a fragment-based marker discovery approach, we identified robust subtype-specific methylation markers from tumor samples. These markers were successfully validated in independent cohorts and associated with subtype-specific transcription activity. Leveraging these markers, we constructed a subtype classification model using cfDNA methylation profiles, achieving an AUC of 0.808 in the cross-validation and an AUC of 0.747 in the independent validation. Additionally, tumor copy number variations inferred from cfDNA methylome analysis revealed potential implications for treatment selection. In summary, our study demonstrates the potential of cfDNA methylome analysis for noninvasively lung cancer subtyping, offering insights for cancer monitoring and early detection.

Study EGAS00001007717

Accurate detection of tumor-specific fusion genes reveals strongly immunogenic personal neo-antigens

Fusion genes arising from cancer-associated somatic mutations are a potential rich source for highly immunogenic neo-antigens. However, their exploitation as targets for personalized cancer immunotherapy is currently limited by the lack of computational tools allowing transcriptome-wide identification of unique fusion genes in an accurate and sensitive manner. Here, we present EasyFuse, a computational pipeline, to detect individual and cancer-specific fusion genes in next-generation-sequencing transcriptome data obtained from human cancer samples. Using machine learning, EasyFuse predicts personal fusion genes with high precision and sensitivity and outperforms previously described approaches as qualified by an unprecedented ground-truth dataset of >1500 verification experiments in relevant patient samples. By testing immunogenicity with autologous blood lymphocytes from cancer patients we detected pre-established CD4+ and CD8+ T cell responses for 10 of 21 (48%), and for 1 of 30 (3%) of identified fusion genes, respectively. In conclusion, we demonstrate accurate detection of cancer-specific fusion genes. The high frequency of T cell responses detected in cancer patients support the relevance of private fusion genes as neo-antigens for personalized immunotherapies, especially for tumors with low point mutation burdens.

Study EGAS00001004877

DATA FILES FOR SJPHALL

Dataset EGAD00001000163

This DAC takes care of requests for data for the Swiss epigenetic colorectal cancer cohort study, SWEPIC

Dac EGAC00001003471

Dataset for the spanish node

Dataset EGAD50000000884

DATA FILES FOR SJCPC-WGS

Dataset EGAD00001001065

WGS minibam files for SJLIFE

Dataset EGAD00001003396

NHLBI TOPMed: Heart and Vascular Health Study (HVH)

Objectives The Heart and Vascular Health Study (HVH) is a case-control study of risk factors for the development of myocardial infarction (MI), stroke, venous thrombosis (VT), and atrial fibrillation (AF). The study setting is Group Health, an integrated health care delivery system in Washington State. Only VT cases and early-onset AF cases are included as part of TOPMed. Background The HVH study originated in 1988 with the examination of risk factors for MI. Over the ensuing years, the study has been funded by a series of grants which have added case subjects with stroke, VT, and AF. Study aims focused on the associations of medication use with cardiovascular events, and starting in 1997, the study aims expanded to include genetic associations with cardiovascular disease. Participants recruited in 2009 or later who provided blood samples for genetic analysis were asked for consent to deposit genetic and phenotypic data in dbGaP. Design As part of the HVH study, case subjects were identified by searching for ICD-9 codes consistent with MI, stroke, VT, or AF, and medical records were reviewed to confirm the diagnosis. Control subjects were identified at random from the Group Health enrollment and were matched to MI cases. All subjects have an index date. For cases, the index date was assigned as the date that the cardiovascular event (MI, stroke, VT, or AF) came to clinical attention. For controls, the index date was a random date within the range of the case index dates. For both cases and controls, information was collected from the inpatient and outpatient medical record, by telephone interview with consenting survivors, and from the Group Health pharmacy and laboratory databases. Consenting participants provided a blood specimen. Subjects Only VT and early-onset AF cases from HVH are included in TOPMed. Within the HVH study, VT and AF cases were diagnosed in both inpatient and outpatient settings, and only incident cases are eligible for inclusion in TOPMed. Genetic Research Genetic factors underlying cardiovascular disease are studied using DNA isolated from the blood samples. Phenotype data for HVH study participants are available through dbGaP phs001013.

Study phs000993

An essential role for MYB in driving oncogenic EVI1 expression in enhancer-rearranged leukemias

In acute myeloid leukemia (AML) with inv(3)(q21;q26) or t(3;3)(q21;q26), a translocated GATA2 enhancer drives oncogenic expression of EVI1. We generated an EVI1-GFP AML model and applied an unbiased CRISPR/Cas9 enhancer scan to uncover sequence motifs essential for EVI1 transcription. Using this approach, we pinpoint a single regulatory element in the translocated GATA2 enhancer that is critically required for aberrant EVI1 expression while being dispensable for GATA2 expression from its endogenous locus. This element contains a DNA binding motif for the transcription factor MYB that heavily occupies this site specifically at the translocated allele. MYB knockout as well as peptidomimetic blockade of p300-dependent MYB function resulted in downregulation of EVI1 but not of GATA2. Targeting MYB or mutating its DNA-binding motif within the GATA2 enhancer resulted in myeloid differentiation and cell death, suggesting that interference with MYB-driven EVI1 transcription provides a potential entry point for therapy of inv(3)/t(3;3) AMLs.

Study EGAS00001004839

Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification (H021)

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

Study EGAS00001006706

DAC for present-day Central Asia genotype data at the Centre for GeoGenetics, Natural History Museum of Denmark.

Dac EGAC00001000894

DAC for "The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia"

Dac EGAC00001001501

Navarrabiomed DAC for XPAND project

This DAC is created for the XPAND project by the Translational Bioinformatics unit.

Dac EGAC50000000462

Leukemia Data Access committee for the deposited data. The committee consists of the principal investigators for the data set.

Dac EGAC00001003565

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