Sung-Hwan Kim1, Onyeka Ezenwoye2, Hwan-Gue Cho1, Keith D Robertson3, Jeong-Hyeon Choi4. 1. School of Computer Science and Engineering, Pusan National University, Busan, South Korea. 2. Hull College of Business, Georgia Regents University, Augusta, GA, USA. 3. Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. 4. Cancer Center and Department of Biostatistics and Epidemiology, Medical College of Georgia, Georgia Regents University, Augusta, GA, USA.
Abstract
MOTIVATION: Tag density plots are very important to intuitively reveal biological phenomena from capture-based sequencing data by visualizing the normalized read depth in a region. RESULTS: We have developed iTagPlot to compute tag density across functional features in parallel using multicores and a grid engine and to interactively explore it in a graphical user interface. It allows us to stratify features by defining groups based on biological function and measurement, summary statistics and unsupervised clustering. AVAILABILITY AND IMPLEMENTATION: http://sourceforge.net/projects/itagplot/.
MOTIVATION: Tag density plots are very important to intuitively reveal biological phenomena from capture-based sequencing data by visualizing the normalized read depth in a region. RESULTS: We have developed iTagPlot to compute tag density across functional features in parallel using multicores and a grid engine and to interactively explore it in a graphical user interface. It allows us to stratify features by defining groups based on biological function and measurement, summary statistics and unsupervised clustering. AVAILABILITY AND IMPLEMENTATION: http://sourceforge.net/projects/itagplot/.
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