dmedic_uuk2pt

Project Summary The cells of the immune system coordinate the body’s response to infection and injury.  An understanding of these cells at the molecular level can facilitate the elucidation of underlying mechanisms and improve patient treatment.  We aim to characterize the immune response to trauma at the protein level to determine if differences in the […]

 Project Summary We propose and test a method for the absolute quantitation of RNA molecules in RNA-Seq gene expression studies. Prior to any PCR amplification, cDNA fragments are generated and randomly labeled with a diverse set of “stochastic labels” comprising of nucleic acid barcodes. After amplification and sequencing, the number of stochastic labels observed for

Description Time-course microarray experiment is capable of capturing the dynamic profile of genomic response to treatment factors. The profile contains valuable information for researchers to identify possible genetic factors that lead to different clinical outcomes, which can help directing future investigation. We developed a general statistical method to extract gene-specific temporal patterns to the interaction

GG-H Array: Glue Grant Human Transcriptome ArraysA high-throuhgput and cost-effective platform for clinical genomics The Human Genomic Response to Severe Traumatic InjuryAn interactive website for exploring gene expression in white blood cells of trauma patients and their selected clinical attributes. JETTA: Junction and Exon array Toolkit for Transcriptome AnalysisA software package for gene expression calculation

Introduction and Background Human survival from injury requires an appropriate inflammatory and immune response. We describe the circulating leukocyte transcriptome after severe trauma and show that these severe stresses produce a global reprioritization affecting >80% of the cellular functions and pathways, a truly unexpected “genomic storm.” In severe blunt trauma, the early leukocyte genomic response

Purpose Time course gene expression profiling is increasingly applied in biomedical research to monitor the progression of diseases and effects of drug treatments. An important goal of the computational analysis is to predict clinical outcomes based on microarray data of patients over time. . However, most existing approaches of prediction use data at one time

The research of our computational genomics group at Stanford Genome Technology Center aims at pushing the boundaries of genomics technology from base pairs to bedside. At the center, our group is closely involved in the development of biotechniques from their early stage pilot studies to the demonstration applications. We use computation in experimental design, data