15028 results for "cancer studies using rna-seq"

in 16.88 milliseconds.

• Breast Cancer Whole Genome Sequencing

  Breast Cancer Whole Genome Sequencing

  Dataset EGAD00001000121

• Breast cancer SNP6 arrays_more

  Affymetrix SNP6.0 array breast cancer data

  Dataset EGAD00010001079

• genome-wide cfDNA methylation analysis

  To characterise cfDNA methylome in metastatic prostate cancer

  Study EGAS00001003958

• Genome-wide association study of esophageal squamous cell cancer identifies shared and distinct risk variants in African and Chinese populations

  Study EGAS00001007477

• NCI COVID-19 in Cancer Patients Study (NCCAPS): A Longitudinal Natural History Study

  NCCAPS is a longitudinal prospective cohort study designed to assess patient, disease and treatment factors associated with short- and long-term outcomes in patients with cancer who develop COVID-19. We also aimed to describe COVID-19 symptoms and treatments, and to describe disruptions in cancer treatment due to COVID-19. The study enrolled patients with cancer on active treatment or with a history of stem cell transplant or CAR-T cell therapy with new, active COVID-19, defined as being within 14 days of their first positive SARS-CoV-2 test. The study found that receipt of chemotherapy and baseline history of stroke, atrial fibrillation or pulmonary embolism were associated with a higher risk of hospitalization for COVID-19. Vaccination for SARS-CoV-2 was associated with a lower risk of hospitalization. Death due to COVID-19 was highest in patients with lymphoma, intermediate in those with acute leukemia and lung cancer, and lowest in those with other solid tumors and other hematologic cancers. Disruptions of cancer treatment were frequent in the first 30 days after SARS-CoV-2 infection.

  Study phs004178

• Genome-wide cell-free DNA fragmentation in patients with cancer

  Cell-free DNA (cfDNA) in the blood provides a noninvasive diagnostic avenue for patients with cancer. However, characteristics of the origins and molecular features of cfDNA are poorly understood. We developed an approach to evaluate fragmentation patterns of cfDNA across the genome and found that cfDNA profiles of healthy individuals reflected nucleosomal patterns of white blood cells, while patients with cancer had altered fragmentation profiles. We applied this method to analyze fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers and 245 healthy individuals. A machine learning model incorporating genome-wide fragmentation features had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity, with an overall AUC of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation based cfDNA analyses detected 91% of cancer patients. The results of these analyses highlight important properties of cfDNA and provide a proof of principle approach for screening, early detection, and monitoring of human cancer.

  Study EGAS00001003611

• SNP array karyotyping of upper urinary tract urothelial carcinoma

  Genome-wide copy number analysis of upper urinary tract urothelial carcinoma using GeneChip Human Mapping 250K Nspl

  Dataset EGAD00010002098

• RealSeqS ovarian amplicon counts

  Dataset contains text files that describe the chromosome, position, and read count for an amplicon using the RealSeqS assay.

  Dataset EGAD50000000013

• H3Africa NEEDI SNPs and INDELs

  SNPs and INDELs of novel hereditary neurological disease genes in Mali using Beckman 8800, ABI 3730/3730xl.

  Dataset EGAD00001006295

• Various_Cancer_Fusion_Gene_Sequencing

  Genomic libraries (500 bps) will be generated from total genomic DNA derived from 50 commomon epithelial cancer samples and subjected to total of eight lanes of 37 bp, paired end sequencing on the llumina GA. Paired reads will be mapped to build 37 of the human reference genome to facilitate the generation of genome wide copy number information, and the identification of novel rearranged cancer genes and gene fusions.

  Study EGAS00001000012