23083 results for "ega"

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• Whole exome sequencing data of peripheral blood mononuclear cells obtained from 2 ATL patients.

  Whole exome sequencing (WES) data of peripheral blood mononuclear cells (PBMCs) obtained from 2 ATL patients (3 samples).

  Dataset EGAD00001007016

• 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

• A95646A

  Whole genome sequencing for single cells for library A95646A 1071 cells; filetype=bam

  Dataset EGAD00001007106

• Genomic data of acute myeloid leukemia from the NGS-PTL european consortium

  Whole exome sequencing (WES) data of paired (germline and leukemic) samples of 60 adult patients affected by acute myeloid leukemia.

  Dataset EGAD00001007940

• Profiling Chromatin Accessibility in Humans Using Adenine Methylation and Long-Read Sequencing

  This dataset includes raw nanopore, base-called, and 6mA frequency data for EcoGII-treated NA12878 and MCF7 chromatin samples. It also includes raw nanopore data for the HG002 EcoGII-treated DNA.

  Dataset EGAD00001015374

• Targeted and shallow whole genome sequencing identifies therapeutic opportunities in p53abn endometrial cancers

  Shallow whole genome sequencing of 196 formalin-fixed paraffin-embedded p53abn endometrial cancers.

  Dataset EGAD00001015241

• Merkel sequencing

  Targeted DNA sequencing on 37 Merkel Cell Carcinomas from New Zealand with known Merkel cell polyomavirus status

  Dataset EGAD00001009808

• Single-cell RNA-sequencing of nivolumab-treated glioblastoma

  Single-cell RNA-sequencing of 8 patients, from primary and relapse tumour (total of 16 samples). Patients were treated with nivolumab prior to relapse surgery.

  Dataset EGAD00001010843

• Bulk RNA-sequencing of nivolumab-treated glioblastoma

  Bulk RNA-sequencing of 30 relapse tumour samples. 20 patients were treated with nivolumab prior to relapse surgery, while 10 patients are control patients.

  Dataset EGAD00001010844

• MS Risk Gene RNAseq

  MS Risk Gene RNAseq datasets of immune cell subsets (CD4, CD8, B cell, monocyte) from healthy controls and untreated MS cases.

  Dataset EGAD00001010879

• Identification of Long Non-coding RNA Biomarker of Human Lupus Nephritis Disease Activity - Dataset

  Total RNA paired-end sequencing was performed on whole blood samples from 74 Lupus nephritis (LN) patients and 20 healthy controls using Illumina NovaSeq 6000.

  Dataset EGAD00001010932

• Single-cell RNA sequencing of RCC patients

  Single-cell RNA sequencing was performed on viable frozen tumor dissociated cells from three RCC patients. The raw data is available as fastq files.

  Dataset EGAD00001011045

• EORTC-26101 sequencing data

  The dataset contains panel sequencing data of 170 genes from 380 patients of the EORTC-26101 trial. The corresponding methylation data is available via the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) repository with the accession number GSE237103.

  Dataset EGAD00001011160

• Human tumor single-cell RNA-seq

  We profiled 16 patient tumor samples by single-cell or single-nuclei RNA-seq using 10X Chromium 3'. It includes 4 low-grade gliomas and 12 ependymomas. The raw fastqs are provided.

  Dataset EGAD00001011315

• 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

• DNA WGS Long Read Sequence (PromethION) for manuscript titled: "Performance of Somatic Structural Variant Calling in Lung Cancer using Oxford Nanopore Sequencing Technology"

  DNA WGS Long Read Sequence (PromethION) for manuscript titled: "Performance of Somatic Structural Variant Calling in Lung Cancer using Oxford Nanopore Sequencing Technology"

  Dataset EGAD00001015400

• Transcriptomic data for Human proximal tubular epithelial cell interleukin-1 receptor signalling triggers cell cycle arrest during hypoxic kidney injury

  Transcriptomic data for manuscript titled: Human proximal tubular epithelial cell interleukin-1 receptor signalling triggers cell cycle arrest during hypoxic kidney injury.

  Dataset EGAD00001015460

• Global RNA sequencing data of human iPSC-derived microglia from frontotemporal dementia (FTD) patients

  Global RNA sequencing of RNA samples extracted from human iPSC-derived microglia from healthy controls and sporadic (non-genetic) and genetic C9orf72 hexanucleotide repeat expansion-carrying patients with FTD

  Dataset EGAD50000002423

• Genomic and transcriptomic profiling of soft tissue sarcoma

  We characterized soft tissue sarcoma by targeted gene panel sequencing (MSK-IMPACT) and transcriptome profiling to discover biological mechanisms under metastatic progression.

  Dataset EGAD50000000846

• Discordant_Monozygotic_Twins_ALS (Epigenetics)

  We conducted a multi-omics study on a monozygotic twin pair discordant for amyotrophic lateral sclerosis. Here, we submit epigenetic data from EPIC array.

  Study EGAS00001008053

• Dataset for "Characterizing the cfDNA fragmentome in patients with hepatocellular carcinoma"

  The dataset includes 63 BAM files from Oxford Nanopore PromethION whole-genome sequencing, generated by base calling POD5 files with Dorado (super-accurate, modified base model v5.0.0_5mCG_5hmCG@v3) and aligning reads to the hg19 reference genome.

  Dataset EGAD00001015823

• Data Quality Control

  Data Quality Control High-throughput sequencing techniques have become the leading method to study, decode and discover the genomic origins of biological phenomenons. EGA provides a secure archival of such identifiable genomics data with the purpose of data-upcycling, i.e. to re-use these data for research. High-quality data standards are essential to ensure the quality and credibility of the research. Moreover, a quality check report can assure a researcher beforehand about the data that they will request access, therefore saving time and effort. The EGA has developed a File Quality Control Report (QC Report) to provide generic quality control reports for Fastq, SAM/BAM/CRAM, and VCF files deposited at EGA. This QC Report will allow users to get information regarding the files submitted within a specific dataset. The data requesters will obtain information such as the quality of reads, mapped reads, number of variants, and other features before starting the requesting process, which will save the efforts and time.Accessing file quality control reportsIn each dataset page, the user can explore the files that it contains by clicking the "files" tab. The Quality Control report of a file has two sections. The first one, contains general information about the file, such as the inferred assembly, total reads, the dataset or study where it comes from, etc. The second section contains plots that summarise interesting information about the file, for example, the base coverage distribution, base quality or mapped reads. The description of each plot is accessible by clicking the "i" button at the top-right corner of each plot box. Technical Description For analysing the fastq, SAM/BAM/CRAM and VCF files, the EGA applies a set of tools widely used in the bioinformatics community. FASTQ: FastQC, recognised as the gold standard tool by the community.Per base sequence quality, per sequence quality scores, per base sequence content, per sequence GC content, sequence duplication levels, etc.SAM/BAM/CRAM: samtools, also the gold standard, generates results plots useful to get an overall idea of the quality of the file.base coverage distribution, base quality, % of mapped reads, % of both mates mapped, singletons, duplicates, etc.VCF: vcftools and bcftools, combined with a custom script to infer the genome assembly.site frequency distribution, Ts/Tv, base changes, indel distribution, etc.

  Documentation access/request-data/quality-control-reports

• Tumour evolvability metrics predict recurrence in advanced localised prostate cancer (normal data)

  There is a need for quantitative measurements of evolutionary metrics in controlled clinical trials with long term follow-up information. This is particularly true in advanced localised prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 tumour samples from 114 patients who took part in the IMRT and DELINEATE clinical trials, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using Deep Learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). This study shows that combining genomics with AI-aided histopathology in clinical trials leads to novel clinical biomarkers. This EGA repository contains data produced from tumour samples using low coverage whole genome sequencing and a prostate cancer specific gene panel data following compression of unique molecular identifiers.

  Dataset EGAD00001010295

• Tumour evolvability metrics predict recurrence in advanced localised prostate cancer (tumour data)

  There is a need for quantitative measurements of evolutionary metrics in controlled clinical trials with long term follow-up information. This is particularly true in advanced localised prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 tumour samples from 114 patients who took part in the IMRT and DELINEATE clinical trials, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using Deep Learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). This study shows that combining genomics with AI-aided histopathology in clinical trials leads to novel clinical biomarkers. This EGA repository contains data produced from tumour samples using low coverage whole genome sequencing and a prostate cancer specific gene panel data following compression of unique molecular identifiers.

  Dataset EGAD00001010274