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• Whole genome sequencing files for St. Jude Clinical Pilot

  This dataset contains whole genome sequencing BAM files for 78 tumor-normal pairs (a total of 156 samples) used in the St. Jude Clinical Pilot. Mapping was performed using BWA. This dataset accompanies the paper "Clinical Cancer Genomic Profiling by Three-Platform Sequencing of Whole Genome, Whole Exome and Transcriptome"

  Dataset EGAD00001004290

• High Glucose Macrophage Exosomes Enhance Atherosclerosis by Driving Cellular Proliferation and Hematopoiesis

  In this study, we aimed to explore whether hyperglycemia alters inflammatory properties and microRNA levels in immune cells as well as their extracellular vesicles (EVs) as a mode of intercellular communication in cardiovascular disease. To this end, we isolated EVs from the plasma of diabetic mice as well as from human subjects that included 7 healthy individuals (control), 5 individuals with diagnosed peripheral arterial disease but no diabetes, 9 diabetic individuals with no vascular disease and 5 diabetic subjects with established vascular disease. The human plasma EVs were examined for the content of microRNA and their capacity to communicate signaling when incubated with cultured human THP1 macrophages. Subsequently, the plasma EV-exposed macrophages were examined for total RNA sequencing to identify inflammatory and molecular pathways targeted by the human EVs. 

  Study phs002401

• STimage: Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data

  Spatial transcriptomics (ST) links tissue morphology with gene expression values, opening new avenues for digital pathology. Deep learning models are used to predict gene expression or classify cell types directly from images, offering significant clinical potential but still requiring improvements in interpretability and robustness. We present STimage as a comprehensive suite of models to predict spatial gene expression and classify cell types directly from standard H\&E images. STimage enhances robustness by estimating gene expression distributions and quantifying both data-driven (aleatoric) and model-based (epistemic) uncertainty using ensemble approach with foundation models. Interpretability is achieved through attribution analysis at single-cell resolution integrated with histopathological annotations, functional genes, and latent representations. We validated STimage across diverse datasets, demonstrating its performance across various platforms. STimage-predicted gene expression can stratify patient survival and predict drug response. By enabling molecular and cellular prediction from routine histology, STimage offers a powerful tool to advance digital pathology.

  Study EGAS50000001503

• CCR8-targeted specific depletion of clonally expanded Treg cells in tumor tissues evokes potent tumor immunity with long-lasting memory

  Foxp3-expressing CD25+CD4+ regulatory T cells (Tregs) are abundantly infiltrating into tumor tissues, hindering effective anti-tumor immunity. Here, assuming that such tumor Tregs were clonally expanding by recognizing tumor-associated antigens, we attempted to characterize them by their gene expression profiling and T-cell receptor (TCR) clonotyping at the single-cell level. We found that they predominantly expressed the chemokine receptor CCR8: ~40% of tumor Tregs, less than 10% of Tregs in other tissues, and few tumor-infiltrating conventional T cells expressed CCR8 in mice and humans. CCR8+ tumor Tregs were phenotypically highly differentiated and functionally stable. One injection of anti-CCR8 mAb with high antibody-dependent cellular cytotoxic activity was indeed able to specifically deplete clonally expanded tumor Tregs and thereby eradicate established tumors in mice. The depletion predominantly expanded tumor-infiltrating CD8+ effector T cells, which were different in activated or exhausted patterns from those seen after immune checkpoint blockade by anti-PD-1 mAb. The treatment also evoked potent secondary immune responses against the same tumor cell line inoculated several months after tumor eradication, indicating that the anti-CCR8-induced memory-type tumor-reactive effector T cells were, once induced, resistant to recovered Treg suppression. In contrast to such evocation of potent tumor immunity, anti-CCR8 treatment scarcely elicited discernable autoimmunity in mice, contrasting with a similar treatment for systemic whole Treg depletion, which induced histologically and serologically evident autoimmunity. Thus, temporary and specific depletion of clonally expanded Tregs in tumor tissues by cell-depleting anti-CCR8 mAb is sufficient to evoke potent tumor immunity with long-lasting memory and without serious autoimmunity.

  Study JGAS000312

• Strand-specific RNA Sequencing of initial and recurrent gliomas

  Four RNA-sequencing datasets from two patients with initial low-grade glioma and copy number alteration at IDH1 upon recurrence. Data is provided as bam files.

  Dataset EGAD00001003764

• Fastq files for SAIF genome

  This dataset contain the raw files generated for SAIF genome project

  Dataset EGAD00001000254

• Targeted and Whole Exome Sequencing for Validation of PGDx elio tissue complete

  This dataset consists of 116 tumor and normal samples analyzed with whole exome sequencing on the HiSeq2500 instruments with 100bp paired-end reads as well as 760 tumor and normal samples analyzed with the PGDx elio tissue complete assay. The PGDx elio tissue complete assay is a hybrid capture approach targeting 500+ genes with sequencing on the NextSeq instruments with 150bp paired-end reads. The bam files provided have been adapter masked and contain duplicate reads.

  Dataset EGAD00001008099

• Rhode Island Child Health Study (RICHS)

  Mother and infant pairs were enrolled in the Rhode Island Child Health Study (RICHS) at the Woman and Infant's Hospital from 2009 through 2014. This population consists of singleton, term infants (≥37 weeks gestation) born without serious pregnancy complications or congenital or chromosomal abnormalities. Given a priority interest to study fetal growth, the RICHS population was oversampled for both large for gestational age (LGA, >90% 2013 Fenton Growth Curve) and small for gestational age (SGA, <10% 2013 Fenton Growth Curve) infants.

  Study phs001586

• ASD WGS DAC

  This data access committee will oversee the request for applications to whole genome sequencing data (WGS) generated at The Centre for Applied Genomics (TCAG), as part of our Autism Genome Project.

  Dac EGAC50000000279

• Somatic mutation and selection at epidemiological scale - TwinsUK_TargetedNanoSeq_Blood

  Utilising Targeted NanoSeq, we hope to explore the driver landscape in unprecedented detail in donors from TwinsUK. These donors have paired buccal swabs and data from this will help contextualise that work. As we age, many tissues become colonised by microscopic clones carrying somatic driver mutations. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of the clonal landscapes of human tissues, and their impact on cancer risk, ageing and disease, remains limited due to the challenge of detecting somatic mutations present in small numbers of cells. Here, we introduce a new version of nanorate sequencing (NanoSeq), a duplex sequencing method with error rates of less than 5 per billion base pairs, which is compatible with whole-exome and targeted gene sequencing. Deep sequencing of polyclonal samples with single-molecule sensitivity enables the simultaneous detection of mutations in large numbers of clones, yielding accurate somatic mutation rates, mutational signatures and driver mutation frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 samples of blood from a twin cohort, we found an unprecedentedly rich landscape of selection, with 46 genes under positive selection driving clonal expansions in the oral epithelium, over 62,000 driver mutations, and evidence of negative selection in some genes. The high number of positively selected mutations in multiple genes provides high-resolution maps of selection across coding and non-coding sites, a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how carcinogenic exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition and selection of somatic mutations. Accurate single-molecule sequencing has the potential to unveil the polyclonal landscape of any tissue, providing a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.

  Dataset EGAD00001015619