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• Sequencing data for oesophageal and related samples - ICGC DCC release 26 (WGS)

  WGS sequencing for 303 cases (620 samples) from the ICGC ESAD-UK project Tumours 50x Normals 30x HiSeq X BAM files These samples are all available in ICGC release 26

  Dataset EGAD00001003580

• Whole Genome sequencing of individuals from Val Borbera, Italy

  This study includes Phase 2 whole-genome sequencing data (at 4x depth)of 100 individuals from an Italian genetic isolate population (Val Borbera, abbreviated VBI) of the Italian Network of Genetic Isolates (INGI). The INGI-VBI_SEQ2 project aims to combine available extensive genetic and phenotypic data to the latest high-throughput genome sequencing technology and ad hoc statistical analysis to identify new rare genetic variants underlying complex traits.

  Dataset EGAD00001000731

• Low-pass nanopore whole genome sequencing of brain tumors

  Dataset EGAD00001003382

• Exome Sequencing of Esophageal Adenocarcinoma

  The incidence of esophageal adenocarcinoma (EAC) has risen 600% over the last 30 years. When coupled to the five-year survival rate of only 15% for this disease, identification of new therapeutic targets for EAC is of great importance. Here, we analyze the mutational spectra identified from the whole genome sequencing of 16 EACs and complete exome sequencing of 149 EAC tumors and matched germline samples. We identify a novel mutation signature marked by high prevalence of A to C transversions at AA*(N) trinucleotides. Notably, the development of EAC is preceded by pathologic intestinal metaplasia of the lower esophagus, Barrett's esophagus, itself a response to injury from gastric-esophageal reflux disease (GERD). Thus, we hypothesize to represent these AA*(N) mutations to a novel signature of GERD-induced mutagenesis. Analysis of the exome data identified 26 genes subject to statistically significant recurrent mutation. Of these 26 genes, only TP53, CDKN2A, SMAD4, and PIK3CA had been previously implicated in EAC. Novel mutations include those targeting chromatin modifying factors and novel candidate contributors to EAC: SPG20, TLR4, ELMO1 and DOCK2. Of these, ELMO1 and DOCK2 are notably direct dimerization partners that act to activate Rac1 to enhance cellular invasiveness, thus generating new hypotheses about the potential for the Rac1 pathway to be a novel target for therapy of EAC. "Reprinted from 'Unraveling the mutational complexity of esophageal adenocarcinoma', with permission from Nature Genetics."

  Study phs000598

• Nanopore sequencing from liquid biopsy: analysis of copy number variations from cell-free DNA of lung cancer patients

  Molecular cancer paper (https://doi.org/10.1186/s12943-021-01327-5): This dataset contain shallow whole-genome sequencing (sWGS) of plasma cell-free DNA from cancer patients and healthy subjects, obtained with both Nanopore and Illumina technology. A total of 6 cancer patients and 5 healthy subjects have been sequenced with Nanopore; 4 of the cancer patients have been also sequenced with Illumina. In addition, genomic DNA from white blood cells of one healthy subjects, genomic and 160bp DNA from HEK cells have been sequenced with Nanopore. Genome Biology paper: 3 additional healthy samples have been sequenced (HU), two different bioinformatic pipeline were applied. 2019: Fastqs from the molecular cancer paper were re-demultiplexed and adapter-trimmed (using guppy for multiplex samples, and porechop for singleplex) preserving 5' ends to allow fragmentomics analysis. HAC: All the samples were basecalled with the same updated High Accuracy model (the latest at the time of the analysis) and post-processed as the 2019 dataset. Raw FAST5 are currently available upon request, but will be uploaded soon.

  Dataset EGAD00001006888

• Diseased heart analysis: RNA Adult (2025-10-14)

  Samples for this project are taken from adult human hearts which have been preserved using 2 different methods (cold static storage on ice, and hypothermic perfusion). Samples have been acquired at various time points during post-preservation normothermic perfusion. The aim of the project is to assess the transcriptional changes caused by these differing methods of preservation. . This dataset contains all the data available for this study on 2025-10-14.

  Dataset EGAD00001015738

• RNA-seq after drug perturbation of primary lymphoma samples

  3'-end low-depth RNA sequencing data from 108 patients with chronic lymphocytic leukemia and 8 patients with lymphoma. Each primary patient sample was treated in vitro with DMSO as a control and with up to ten small-molecule inhibitors (ibrutinib, duvelisib, everolimus, trametinib, nutlin-3a, I-BET762, MK2206, selinexor, compound 26, and a combination of ibrutinib and compound 26). Sequencing was performed on the Illumina HiSeq4000. The dataset contains FASTQ files.

  Dataset EGAD50000002166

• Multi-omics datasets (WES, RNA-seq) of mesothelioma MIST4 study, a phase II PD-L1/VEGF blockage clinical trial

  This study comprises 26 mesothelioma patients with whole exome and RNA sequencing, designed to examine the efficacy and correlates of response to dual inhibition of programmed death-ligand 1 and vascular endothelial growth factor inhibition.

  Study EGAS50000001818

• miRNA data for aSAH patients with or without subsequent vasospasm

  These files contain the normalized and raw count abundances miRNA for aSAH patients. These abundances were obtained using Next-Generation Sequencing after selection of the miRNA in the RNA biobank. In total, there are 28 VSP- and 28 VSP+ patients for two-time points. Normalized data were obtained by applying size factor and VSN normalizations as described in Pulcrano-Nicolas et al. Stroke 2018. Raw count data corresponding to the raw abundances of miRNA in aSAH patients.

  Dataset EGAD00001004185

• Peripheral blood DNA methylome in adalimumab-treated patients with rheumatoid arthritis

  Background and aims Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade such as anti-TNFα therapy. Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood Methods DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of adalimumab (ADA) was determined using Illumina HumanMethylationEPIC BeadChip Array. After 6 months, treatment response was assessed according to the Alliance of Associations for Rheumatology (EULAR) criteria for disease response. Patients were classified as responders (DAS28<3.2 or decrease of 1.2 points) or as non-responders (DAS28>5.1 or decrease of less than 0.6 points). Machine learning models were built with gradient boosting and stability selection models to predict response prior to ADA treatment with predictor DNA methylation markers. Results We demonstrated a 27-feature panel of DNA methylation markers or CpG classifiers that predict response in RA patients treated with ADA. Forty-nine patients were assigned as responder and 43 patients assigned as non-responders. We differentiate responders from non-responders with a high sensitivity (AUC 0.76). The predictor CpGs annotated to genes involved in immunological- and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology and angiogenesis. Conclusion Our findings indicate that DNA methylation signatures discriminate responders and non-responders to ADA treatment and can serve as a tool for therapy prediction.

  Study EGAS00001007578