Genedata - Statement of Need.pdf

GeneData

Statement of Need

This is a follow-on purchase of a bioinformatics analysis program currently used by the Viral Pathogenesis and Evolution Section (VPES) to perform comprehensive statistical modeling of ‘multi-omics’ data and advanced visualizations for target-of-interest, biological network characterization, and diagnostic/prognostic biomarker identification in genomics/transcriptomics/proteomics data sets from animal model and human clinical samples derived from vaccine and infection studies performed by the VPES, as part of its mission to develop novel universal influenza vaccines. Integration of a diverse set of data normalization, statistical, data visualization, and proprietary data mapping tools are critical VPES research on influenza viral diseases and completion of its mission. Moreover, VPES has in excess of 100,000,000 gene expression data points from over 8 years of clinical and experimental vaccine development studies, that are critical for on-going long-term, clinical immunology and universal vaccine testing studies.

Genedata Analyst utilizes rigorous data normalization, statistical algorithms, machine learning and interactive data analysis and visualization tools. Genedata Analyst also includes proprietary mapping features for integration of diverse, complex data from both public domain data with proprietary experiments and combination of genomics profiling and phenotype data correlation. Specifically, Genedata Analyst provides the following features. (1) Data normalization tools, including linear normalization to standardize experiments and remove artifacts and nonlinear normalization methods like LOWESS, Quantile Normalization and Median Polish. (2) Statistical tools using both parametric and non-parametric tests, including t-Test, mixed linear models (ANOVA and ANCOVA), Principal Components Analysis (PCA), multiple testing corrections controlling false positives from high-throughput experiment and trend identification and time series analyses that support complex clinical metadata-based analysis. (3) Machine learning analysis tools including modeling and prediction Decision Trees, Partial Least Square analysis (PLS), Linear Discriminant Analysis, feature selection methods like ANOVA and Recursive Feature Elimination and model validation using leave-one-out and Monte Carlo cross validation. (4) Interactive data visualization, including histograms, bar charts, box plots, heat maps, scatter plots, trees, maps, and parallel coordinate plots, and proprietary specialized viewers for understanding the results of complex statistical analyses and biological interpretation. All data visualizations in Analyst are fully interactive and feature shared selections between all graphs to facilitate and support interactive data exploration.

Integration of a diverse set of data normalization, statistical, data visualization, and proprietary data mapping tools are critical VPES research on influenza viral diseases and completion of its mission. Moreover, VPES has in excess of 100,000,000 gene expression data points from over 8 years of clinical and experimental vaccine development studies, that are critical for on-going long-term, clinical immunology and universal vaccine testing studies.