The completion of sequencing the human genome in 2001 marked a transition in biomedical research. However, the most transformative aspect was not the completion of the genome sequence itself, or the catalog of genes that it produced. Rather, it was the technologies that were spawned by the genome project, including DNA microarrays, proteomics, metabolomics, and more recently. Next Generation DNA sequencing methods and high-throughput epigenetics platforms, which have transformed our approach to fundamental questions in basic, translational, and clinical research. Rather than looking at a single gene at a time, these technologies have given us the ability to explore the epigenetic marks and gene expression patterns of all of the transcripts in the genome and the proteins they encode, to survey the spectrum of variation in populations, or to look at genome-wide patterns of epigenetic variation, and to use these to search for patterns that correlate with disease. With the maturation of these technologies, the challenge is no longer generating the data, but rather, effectively collecting, managing, analyzing, and interpreting it. The goal of the Systems Biology, Bioinformatics, and Biostatistics Core is to provide a collaborative structure in which experienced statisticians and bioinformatics scientists will work together to support the various PPG Projects to develop testable hypotheses about genes associated with COPD and their functional roles in COPD pathogenesis. Many of these statisticians and bioinformatics scientists in Core A are included as co-investigators in the Projects, and they will work closely with the highly talented clinical, laboratory, and population scientists in each Project within this proposed PPG. Core A will serve a dual role, providing support for the collection, management, analysis and interpretation of the SNP, gene expression, and methylation profiling data using """"""""conventional"""""""" methods in bioinformatics while applying new systems-biology-based methods for analysis and interpretation of the data. The rationale behind creating a separate core is to supplement the strong quantitative analytical teams working on each of the various projects with bioinformatics and systems-based modeling expertise and to work to integrate data across projects.
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