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• SCRNA10X_SA_CHIP0141_004

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0141_004 1 samples; filetype=fastq

  Dataset EGAD00001006463

• TENX068

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX068 1 samples; filetype=fastq

  Dataset EGAD00001006482

• TENX069

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX069 1 samples; filetype=fastq

  Dataset EGAD00001006483

• WTCCC case-control study for Type 1 Diabetes - Combined Controls

  WTCCC genome-wide case-control association study for Type 1 Diabetes (T1D) using six disease collections together with the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000015

• WES and RNAseq dataset

  This dataset includes WES and RNAseq for 7 patients with metastatic melanoma (4), non-small cell lung adenocarcnioma (1), cervix adenocarcinoma (1), and Epidermoid nasophaeyngeal carcinoma (1), enrolled in phase I clinical trials (NCT03475134m NCT04643574 and NCT05195619). WES was performed on matched cancer and healthy tissues, whereas RNAseq was performed on cancer tissues, using Illumina HiSeq 4000/6000 and Illumina NextSeq 500/550 systems.

  Dataset EGAD50000000337

• single cell data from HPV-positive head and neck cancer patients receiving induction CTLA-4 and PD-1 immune checkpoint blockade

  Baseline and on-treatment tumor samples from human papillomavirus-positive oropharynx cancer patients receiving anti-CTLA-4 and anti-PD-1 immune checkpoint blockade were analyzed by single-cell RNA and TCR sequencing.

  Study EGAS50000000335

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_LPS_T=1hr, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_LPS_T=1hr sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002502

• BLUEPRINT release August 2016, Bisulfite-Seq for precursor B cell, on genome GRCh38

  Bisulfite-Seq data for 1 precursor B cell sample(s). 6 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002330

• BLUEPRINT release August 2016, Bisulfite-Seq for macrophage - T=6days B-glucan, on genome GRCh38

  Bisulfite-Seq data for 1 macrophage - T=6days B-glucan sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002410

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - T=0days, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - T=0days sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002405

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_T=4hrs, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_T=4hrs sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002394

• BLUEPRINT release August 2016, Bisulfite-Seq for macrophage - T=6days untreated, on genome GRCh38

  Bisulfite-Seq data for 1 macrophage - T=6days untreated sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002373

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_LPS_T=24hrs_RPMI_T=5days, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_LPS_T=24hrs_RPMI_T=5days sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002324

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_T=1hr, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_T=1hr sample(s). 15 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002447

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_T=6days, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_T=6days sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002371

• BLUEPRINT release August 2016, Bisulfite-Seq for Acute Promyelocytic Leukemia - ATRA, on genome GRCh38

  Bisulfite-Seq data for 1 Acute Promyelocytic Leukemia - ATRA sample(s). 9 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002472

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_BG_T=4hrs, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_BG_T=4hrs sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002301

• Bulk RNASeq on CD8+CD69+CD103+ and CD8+CD69+CD103- T cells form breast cancer.

  CD8+CD69+CD103+ and CD8+CD69+CD103- T cells were flow sorted from 1 primary triple negative breast cancer, 1 primary HER2 amplified breast cancer, and 1 triple negative liver metastasis. Prior to flow sorting, fresh tumor samples were digested to produce single cell homogenates on the day of surgery. Following RNA extraction, RNASeq was performed using polyA selection.

  Dataset EGAD00001003964

• RNA-seq of multiple myeloma patient samples

  RNA from CD138+ plasma cells from patients with a plasma cell dyscrasia were sequenced using the TruSeq Stranded Total RNA Ribo-zero Gold kit (Illumina). Paired-end 75bp reads were generated on a NextSeq500 or HiSeq4000 (Illumina). The dataset consists of 1 MGUS sample, 5 SMM samples, 69 newly diagnosed myeloma samples, 1 relapsed myeloma sample, 1 previously treated myeloma samples, and 4 PCL samples. Matching whole genome sequencing data are available for these samples under study EGAS00001003164.

  Dataset EGAD00001004543

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_T=24hrs, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_T=24hrs sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002335

• BLUEPRINT September 2016, Bisulfite-Seq Acute Lymphocytic Leukemia for precursor B cell from bone marrow, on Genome GRCh38

  Bisulfite-Seq data for 1 sample(s) Acute Lymphocytic Leukemia for precursor B cell from bone marrow, on Genome GRCh38. 3 run(s), 1 experiment(s), 0 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001002969

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_BG_T=1hr, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_BG_T=1hr sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002327

• Circulating tumor DNA dynamics reveal KRAS G12C mutation heterogeneity and response to treatment with the KRAS G12C inhibitor divarasib in solid tumors

  Purpose: To inform prognosis, treatment response, disease biology, and KRAS G12C mutation heterogeneity, we conducted exploratory circulating tumor DNA (ctDNA) profiling on 134 patients with solid tumors harboring a KRAS G12C mutation treated with single-agent divarasib (GDC-6036) in a phase 1 study. Experimental design: Plasma samples were collected for serial ctDNA profiling at baseline (Cycle 1 Day 1 prior to treatment) and multiple on-treatment time points (Cycle 1 Day 15 and Cycle 3 Day 1). Results: KRAS G12C ctDNA was detectable from plasma samples in 72.9% (43/59) and 92.6% (50/54) of patients with non-small cell lung cancer and colorectal cancer, respectively, the majority of whom were eligible for study participation based on a local test detecting the KRAS G12C mutation in tumor tissue. Baseline ctDNA tumor fraction was associated with tumor type, disease burden, and metastatic sites. A decline in ctDNA level was observed as early as Cycle 1 Day 15. Serial assessment showed a decline in ctDNA tumor fraction associated with response and progression-free survival. Except for a few cases of KRAS G12C sub-clonality, on-treatment changes in KRAS G12C variant allele frequency mirrored changes in the overall ctDNA tumor fraction. Conclusion: Across tumor types, the KRAS G12C mutation likely represents a truncal mutation in the majority of patients. Rapid and deep decline in ctDNA tumor fraction was observed in patients responding to divarasib treatment. Early on-treatment dynamics of ctDNA were associated with patient outcomes and tumor response to divarasib treatment.

  Study EGAS50000000315

• Advancing fast in the analysis of circulating tumour DNA

  In cancer patients, solid tumours also release DNA fragments into the blood, and they are called Circulating Tumours DNA (ctDNA). Nowadays, the standard procedure for cancer monitoring is analysis of surgically obtained biopsies. They generate a big amount of material, but the operation is highly invasive and provide medical doctors with a single-time snapshot of the tumours, which are often highly changeable. Analysis of ctDNA, on the other hand, can provide continuous information regarding the tumour itself without the need of invasive procedures. This is why interest in this method is growing, and with it, the need for its technological optimization. Guanhua Zhu and colleagues, at the Genome Institute of Singapore, developed a methodology to quantify ctDNA burden, which means to estimate the amount of tumours derived circulating DNA among all cell-free DNA in the blood of individuals. Their work, published in Nature Communications in april 2021 and summarized in a sketch below, uses state-of-the art methods to analyse cfDNA degradation patterns at promoters and exon regions, to discern it from the DNA coming from the tumours. The bright point of their approach is that simultaneously enable for profiling of actionable cancer mutations and quantitative estimation of ctDNA burden. Their sequencing data of ctDNA has been deposited in the EGA under the accession number EGAS00001004657. This study sums up to a total of 37 of cfDNA and 36 about ctDNA present up to date in the EGA archive. The existence of cfDNA was firstly described back in 2002, while the use of ctDNA analysis in the oncological field exploded only after 2016. The approach is rapidly evolving, moving in parallel with the ever-improving sequencing technologies. Especially, the possibility to obtain high quality sequences from minimal amount of material is a crucial factor. Being able to alleviate cancer patients of disturbing and often slow-recovery procedures, while at the same time improving the quality of diagnosis and disease monitoring seems like a lot to ask. Thankfully, we are blessed with brilliant and dedicated scientists from all around the world who collaborate to make this a reality, sharing their results, knowledge and data.

  Blog advancing-fast-in-the-analysis-of-circulating-tumour-dna

• Clonal hematopoiesis is associated with adverse outcomes in patients with COVID-19

  Clonal hematopoiesis of indeterminate potential (CHIP) is defined as the occurrence of an expanded proportion of mature blood cells derived from a mutant hematopoietic precursor without evidence of hematological malignancies. The principle behind this is that the somatic mutation confers a fitness advantage to the cell in which it arose. Different clinical consequences are linked with this expansion. Early evidence of an association with higher mortality risk was provided. This was not related to higher rates of cancer but was associated in particular with increased cardiovascular mortality. Mechanistically, inflammatory processes are not only related to the development of clonal hematopoiesis, but in turn it is also a driver of inflammation. Besides pulmonary symptoms, COVID-19 evokes complex extra-pulmonary manifestations driving the pathophysiology. Among them, both inflammatory and cardiac-associated mechanisms have been deciphered. With the aim of assessing the impact of clonal hematopoiesis on the pathophysiology of COVID-19, hospitalized patients with severe or critical course were evaluated for the presence of CHIP driver mutations and, more importantly, the association with the clinical picture.

  Study EGAS00001006218