High-throughput technologies have established themselves as indispensable tools for the study of biological systems, from gene expression level changes and protein concentrations, to their modifications and interactions in complex diseases and systems such as cells and complete organisms. The Bioinformatics Core assists with computational needs of CMTV projects, including statistical and high-throughput analyses, experimental design, data standardization and normalization, data pathway and other confirmatory analyses, data storage, sharing and management, and integration of the project-related data into public databases.
Three goals of the Bioinformatics Core are:
1. SERVICE: create a structure to support CMTV investigators.
2. RESEARCH AND DEVELOPMENT: develop new techniques and algorithms that supplement the currently available off-the shelf and open-source packages.
3. EDUCATION: introduce and train young and established investigators in the technologies available in the field of bioinformatics.
Our goal is to incorporate aspects of high-throughput and high-performance computing with knowledge discovery approaches through the application of neural networks, probability, and statistics to support and enhance each of the participating projects and the Molecular Analysis Core. The Bioinformatics Core is based at the LSU-Shreveport campus, and is an important component of the interdisciplinary collaboration between LSU-S and LSUHSC-Shreveport.
Figure 1. (a) Scatter plot of a microarray data set representing polysomal behavior in C. elegans. (b) 3D visualization of the same data set after we processed it using our neural-network-enhanced visualization. The overlapped records that appeared to be the same in (a) are now placed at different locations along the z-axis according to all (or a subset, if so desired) dimensions in the data set.
a) b) c)
Figure 2: VizSOM Radviz with a sample microRNA data set and 50-node stack of SOM output nodes; (a) 3D view from side, Radviz projection base at the bottom; (b) bottom projection; (c) top projection, all with color scale based on selected variable.