12055 results for "cancer rna-seq"

in 17.85 milliseconds.

• Single cell sequencing: Capturing the origin and dynamics of chromosomal copy-number heterogeneity

  Study EGAS00001003812

• STM4 - Mouliere et al, 2018. Enhanced detection of circulating tumor DNA by fragment size analysis

  Study EGAS00001004418

• BRCA2, ATM, and CDK12 defects differentially shape prostate tumor driver genomics and clinical aggression

  Study EGAS00001004800

• Genomic Analysis of Focal Nodular Hyperplasia with Associated Hepatocellular Carcinoma Unveils its Malignant Potential

  Study EGAS00001005313

• Evaluating CRISPR-based Prime Editing for cancer modeling and CFTR repair in organoids

  Study EGAS00001005358

• Genomic profiling of metastatic basal cell carcinoma reveals candidate drivers of disease and therapeutic targets

  Study EGAS00001006148

• Mechanism of action and resistance to Trastuzumab Deruxtecan in patients with metastatic breast cancer

  Study EGAS00001007372

• CUPiD, a cfDNA methylation-based tissue-of-origin classifier for Cancers of Unknown Primary

  Study EGAS00001007445

• The WID-EC test for the detection and risk prediction of endometrial cancer

  Study EGAS00001005033

• The DNA methylome of cervical cells and risk of ovarian cancer

  Study EGAS00001005045

• Characterization of UV DNA damage in B-cell precursor acute lymphoblastic leukemia

  Study EGAS00001008128

• The data access committee for "Validation of cfDNA fragmentome analyses for early detection of liver cancer"

  Dac EGAC00001003543

• An Integrated Approach to Patient Stratification and Therapy Selection in Acute Myeloid Leukemia

  Study EGAS00001004655

• A renal cell carcinoma tumorgraft platform to advance precision medicine

  Study EGAS00001005516

• Subgroup-specific gene expression profiles and mixed epistasis in chronic lymphocytic leukemia (HIPO, H005)

  Study EGAS00001006361

• FFPE_whole_genome_pilot

  To assess feasibility of whole genome sequencing

  Study EGAS00001001967

• Angiosarcoma_whole_exome

  Extension of angiosarcoma whole genome sequencing study

  Study EGAS00001000588

• Angiosarcoma_RNA_sequencing

  Extension of angiosarcoma whole genome sequencing study

  Study EGAS00001000590

• Angiosarcoma_targeted_pulldown_cancer_gene_panel

  Extension of angiosarcoma whole genome sequencing study

  Study EGAS00001000589

• Deep_sequencing_of_melanoma_for_driver_mutations

  Deep sequencing of melanoma for driver mutations

  Study EGAS00001000857

• PPCG UK BAMs

  Pan Prostate Cancer Group UK BAM files

  Dataset EGAD00001006742

• NECC WXS

  Whole exome sequencing of neuroendocrine cervical cancer

  Dataset EGAD00001006391

• Angiosarcoma targeted pulldown cancer gene panel

  Extension of angiosarcoma whole genome sequencing study

  Dataset EGAD00001001064

• NINDS Deep Sequencing for the Detection of Viral Sequences in Primary Progressive Multiple Sclerosis Brains

  Deep sequencing allows for a rapid, accurate characterization of microbial DNA and RNA sequences in many types of samples. Deep sequencing (also called next generation sequencing or NGS) is being developed to assist with the diagnosis of a wide variety of infectious diseases. In this study, seven frozen brain samples (n = 7) from deceased subjects with recent encephalitis were investigated. RNA from each sample was extracted, randomly reverse transcribed, and sequenced. The sequence analysis was performed in a blinded fashion and confirmed with pathogen-specific PCR. This analysis successfully identified measles virus sequences in two brain samples and herpes simplex virus type-1 sequences in three brain samples. No pathogen was identified in the other two brain specimens. These results were concordant with pathogen-specific PCR and partially concordant with prior neuropathological examinations, demonstrating that deep sequencing can accurately identify viral infections in frozen brain tissue (Chan et al., 2014). Data from the primary progressive multiple sclerosis (PPMS) brain specimens (n = 14) and Normal Control brain specimens (n = 14) are also submitted here. All the subjects studied here (encephalitis, PPMS, and controls) were selected from existing collections at the Rocky Mountain and UCLA brain banks. These selections were performed by the directors at the two centers (Dr. Corboy at RM and Nagra at UCLA) based on clinic information provided at the time of collection. All the specimens are from deidentified deceased individuals. The submitting investigators have no identifying information on any of these subjects. To avoid confusion, the dbGaP Subject IDs are identical to the brain bank identifying numbers.

  Study phs000715

• Integration of Genomics and Transcriptomics in unselected Twins and in Major Depression

  Our goals are to develop a comprehensive understanding of the genomics of transcription in a population based unselected sample and to discover DNA and RNA biomarkers for major depressive disorder (MDD). This work is essential to developing a more complete understanding of the biological basis of MDD, a common complex trait associated with considerable morbidity, mortality, and personal/societal cost. All biological samples have been collected from well-defined populations, and are now available. First, we conduct a "genetical genomics" or eQTL study of ~800 MZ and ~800DZ twin pairs. Each subject has been assayed for genome-wide SNPs and CNVs and gene expression from peripheral blood sampled under standardized conditions. We determine the genetic architecture (genetic and non-genetic proportions of variance via twin analyses) for every transcript, and the genome-wide associations (i.e., SNP-transcript eQTL pairs). These analyses will be expanded to consider transcriptional modules. The key deliverable is a detailed catalogue of the general and specific architecture of transcription plus raw intensity files. Second, we seek to discover DNA and RNA biomarkers relevant to MDD, capitalizing on the results of a large MDD study with repeated clinical and biological assessments; we have previously shown that PB is a reasonable proxy for CNS expression and employ an advanced modelling framework: (a) Using baseline data, we identify biomarkers for MDD by comparing ~1000 controls with ~1400 MDD cases via comparisons of SNP, CNV, expression transcripts, and transcriptional modules. (b) Using longitudinal data, we contrast gene expression signatures assessed at baseline and two years later in ~200 controls and ~500 MDD cases.

  Study phs000486