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• DAC for TFHL single-cell analysis project at Department of Hematology, University of Tsukuba

  DAC for study of tumor heterogeneity and immune-evasive microenvironment in T follicular helper cell lymphomas

  Dac EGAC50000000201

• Pancreatic Cancer NGS WGS data

  Whole genome sequencing data for Pancreatic Cancer Samples exploring the tandem duplicator phenotype

  Study EGAS50000001078

• Additional Methylation files for Roussel-ATRT-TM

  Additional Methylation files for Roussel-ATRT-TM paper titled "Atypical teratoid/ rhabdoid tumoroids reveal subgroup-specific drug vulnerabilities"

  Dataset EGAD00010002495

• Array data for oesophageal and related samples – kno_paper_methyl_release

  Array data for oesophageal and related samples – kno_paper_methyl_release

  Dataset EGAD00010001795

• Targeted sequencing analysis for MDS with HSCT

  Targeted capture sequencing for cases with MDS who were subjected to unrelated bone marrow transplantation via Japan marrow donor program

  Dataset EGAD00001003118

• Data files for CONSERTING

  Data files for CONSERTING (WGS)

  Dataset EGAD00001001352

• NHLBI TOPMed: Defining the Time-Dependent Genetic and Transcriptomic Responses to Cardiac Injury Among Patients with Arrhythmias

  The UMMS miRhythm Study is an ongoing study of adult patients undergoing an elective electrophysiology study or arrhythmia ablation procedure for a supraventricular or ventricular arrhythmia, including atrial fibrillation (AF). Atrial fibrillation is a major clinical and public health problem that is related to atrial pathologic remodeling. Few tools are available to quantify the activity or extent of this remodeling, rendering it difficult to identify individuals at risk for AF. Previous studies have suggested an important role for miRNA in cardiovascular disease through gene expression regulation, making this a promising avenue for studying AF mechanisms. The aim of the study is to determine the time-dependent changes to key circulating miRNAs in a model of planned atrial injury and remodeling via ablation. Such knowledge might provide additional insight into the biology and activity of the acute atrial injury response, and furthermore, inform new targets for development of preventative interventions or allow for better AF risk stratification. To assess pathways regulating atrial pathological remodeling, patient blood samples are collected prior to their ablation procedures and also at a regularly scheduled 1-month follow-up appointment. Plasma expression of miRNA is measured using high-throughput quantitative reverse transcriptase polymerase chain reaction (RT-qPCR), providing novel insights into the regulatory processes underlying AF, as well as acute atrial injury in vivo. Additionally, data collected from whole-genome sequencing (WGS) is used to supplement miRNA analyses and further explore new relations between genes and abnormal heart rhythm.

  Study phs001434

• Methylation differences in trisomy 21 using monozygotic twins

  We have hypothesized that methylation differences induced by trisomy 21 (T21) contribute to the phenotypic characteristics and heterogeneity in T21. In order to determine the methylation differences in T21 without the interference of the interindividual genomic variation, we have used fetal skin fibroblasts from monozygotic (MZ) twins discordant for T21. We also used skin fibroblasts from MZ twins concordant for T21, normal MZ twins without T21, and unrelated normal and T21 individuals. We applied Reduced Representation Bisulfite Sequencing (RRBS) to generate genome wide nucleotide resolution of DNA methylation based on high throughput sequencing between each pair of samples. RRBS revealed differentially methylated promoter regions (DMRs) in MZ twins discordant for T21 that have also been observed in unrelated normal and T21 individuals. The identified DMRs are enriched for genes involved in embryonic organ morphogenesis. These DMRs are maintained in iPS cells generated from this twin pair and are correlated with the gene expression changes. We have also observed an increase in DNA methylation level in the T21 methylome compared to the normal euploid methylome. This observation is concordant with the up regulation of DNA methyltransferase enzymes (DNMT3B and DNMT3L) and down regulation of DNA demethylation enzymes (TET2 and TET3) in the iPSC of the T21 versus normal twin. Additionally, two sets of T21 MZ twins discordant for heart defect highlighted DMRs in genes that are associated with heart development. In conclusion the study of DNA methylation differences in MZ twins discordant for genomic abnormalities is a promising approach to understand the molecular pathophysiology of aneuploidies.

  Study EGAS00001001051

• Comparison of the diagnostic yield of aCGH and NGS across different neurodevelopmental disorders

  Current recommendations for the genetic diagnosis of neurodevelopmental disorders (NDDs) do not include the use of next generation sequencing (NGS) and are still based on chromosomal microarrays, such as comparative genomic hybridization array (aCGH). The present study compares the diagnostic yield obtained by aCGH and clinical exome sequencing in NDD globally and its spectrum of disorders. To that end, 1412 patients clinically diagnosed with NDDs and studied with aCGH were classified into phenotype categories: Global developmental delay/intellectual disability (GDD/ID); autism spectrum disorder (ASD); and Other NDDs. These categories were further subclassified into subcategories, based on the most frequent accompanying signs and symptoms: isolated forms, forms with epilepsy; forms with micro or macrocephaly and syndromic forms. A subset of 245 patients of the 1412 were subjected to clinical exome sequencing. Diagnostic yield of aCGH and clinical exome sequencing, expressed as the number of solved cases, was compared for each phenotype category and subcategory. Clinical exome sequencing was superior than aCGH for all cases except for isolated ASD, with no cases solved by NGS. Globally, clinical exome sequencing solved 20% of cases (versus 5.7% by aCGH) and the diagnostic yield was highest for all forms of GDD/ID and lowest for Other NDDs (7.1% versus 1.4% by aCGH) and ASD (6.1% versus 3% by aCGH). In the majority of cases, diagnostic yield was higher in the phenotype subcategories than in the mother category. These results support the use of NGS as the first-tier test in the diagnostic algorithm of all NDDs followed by aCGH when necessary.

  Study EGAS00001004949

• Transcriptome_human_nasal_epithelium

  1. Odors are detected, firstly, by olfactory sensory neurons (OSNs) in the olfactory epithelium of the nose. This neurons then project directly to the olfactory bulb in the brain. Olfaction depends on cellular regeneration of the OE, olfactory bulb and hippocampus, and on their continual re-wiring. The olfactory neural pathway includes regions of the frontal, temporal and limbic brain, which in turn overlap with brain areas involved in brain disorders. OSNs are the only aspect of the human brain exposed to the external environment. This not only makes them vulnerable to environmental changes, but also accessible for biomedical studies.We have already sequenced and developed a protocol for analyzing the transcriptome of mouse main olfactory epithelium and single OSNs. We propose here to perform a similar study for samples from the human olfactory epithelium. We have developed a minimally invasive method for obtaining human OSNs, among other cells from the nasal epithelium. In this experiment, we have obtained cell samples from the olfactory epithelium, including OSN, from healthy volunteers. We would like to further characterize them by RNA sequencing. This will give us valuable insight into human olfaction. It will also provide a first step into a new avenue to study, and find biomarkers for, brain diseases though the analysis of these easily available neurons. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001001294