15136 results for "cancer studies using rna-seq"

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• Leiden University Medical Center Adipose tissue RNA-seq

  Dac EGAC00001000503

• Board considering access to BELOB RNA-seq data

  Dac EGAC00001001686

• RNA-seq data from 27 glioblastoma samples

  Study EGAS00001005807

• Blood Gene Signatures Associated with Stiffness After TKA

  In this study, we used PAXgene Blood RNA tubes collected from patients undergoing total knee arthroplasty (TKA) for osteoarthritis. Samples were collected with IRB approval and patient consent. We identified 8 patients with knee stiffness after TKA (cases) and 10 patients without post-TKA stiffness (controls). We isolated RNA from cases and controls for RNA-seq analyses, comparing samples collected the day of surgery and samples collected at 24 hours after surgery.

  Study phs002927

• Tam-Seq of HGSOC samples for fixative comparison study

  Targeted amplicon sequencing of samples as part of the study "Methanol-based fixation is superior to buffered formalin for next-generation sequencing of DNA from clinical cancer samples. The amplicon panel consists of 48 amplicons in TP53, PTEN, EGFR, PIK3CA, KRAS and BRAF genes as described previously [Forshew, STM 2012]. All libraries were pooled and quantify using DNA 1000 kit on Agilent 2100 Bioanalyzer and KAPA SYBR FAST ABI Prism qPCR Kit (KAPA Biosystems) on 7900HT Fast Real-Time PCR System (Applied Biosystems) according to the supplier's recommendations. Reads were aligned using bwa-mem v0.7.12-r1039 to the 1000 genomes version of human genome build GRCh37, retaining duplicate reads.

  Dataset EGAD00001001937

• Genetic Studies of Homologous Recombination Deficiency in Hispanic Gastric Cancer

  Approximately 10% of gastric cancer cases show familial clustering and a hereditary cause is likely, however, only 1-3% cases result from known hereditary syndromes – which are predominantly associated with CDH1 and CTNNA1 genes. Hispanics are > 2 times more likely to be diagnosed and to die from gastric cancer compared to non-Hispanic whites. Not only does gastric cancer precision medicine lag behind other cancers – there is an urgent need to understand its etiology. Germline mutations in homologous recombination genes have been implicated as important risk factors for gastric cancer. The purpose of this study is to continue to generate knowledge to improve gastric cancer outcomes and disparities by 1) characterizing prevalence of germline mutations in homologous recombination genes and identify new genes associated with gastric cancer 2) perform molecular characterization of gastric tumors in Hispanic patients. Participants for germline analysis were enrolled from Chile, Colombia, Mexico, Portugal, Spain, Uruguay.Whole exome sequence capture was performed for all germline and somatic samples using Agilent SureSelect Human All Exon v7 Kit. All sequencing was performed on an Illumina NovaSeq 6000. Germline Analysis: Whole exome sequencing was performed on 536 Hispanic patients (290 males, 246 female)

  Study phs003251

• Human and rat skeletal muscle multi-omic profiling

  Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples.

  Study EGAS00001005730

• Association studies using the Metabochip array - Samples analysed by the WTCCC (1958 British Birth Cohort (58BC), Hypertension cohort (HT), Type 2 Diabetes Cohort (T2D) and Coronary Artery Disease (CAD) cohort)

  Genomewide association studies (GWAS) have proven a powerful hypothesis-free method to identify common disease-associated variants. Even quite large GWAS, however, have only at best identified moderate proportions of the genetic variants contributing to disease heritability. To provide cost-effective genotyping of common and rare variants to map the remaining heritability and to fine-map established loci, the Metabochip Consortium has developed a 200,000 SNP chip that has been produced in very large numbers for a fraction of the cost of GWAS chips. This chip provides a powerful tool for genetic studies of metabolic, cardiovascular and anthropometric traits (Voight et al., in press PLoS Genetics).

  Study EGAS00000000115

• RNA-sequencing of Non-muscle Invasive Bladder cancer (NMIBC)

  This RNA-sequencing cohort includes 78 Non-muscle Invasive Bladder cancer (NMIBC) samples and 7 Muscle Invasive Bladder cancer (MIBC) samples.

  Study EGAS00001004358

• Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

  Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via "mapping" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

  Study EGAS00001003452