Whole-genome sequencing of disease-causing organisms provides an unabridged examination of the genetic content of individual pathogen isolates, enabling public health laboratories to benefit from comparative analyses of total genetic content. Combining this information with sample metadata such as temporal, geospatial, morbidity, and mortality can greatly increase the efficacy of genomics analysis. However, with the vast amount of data generated by such techniques, meaningful, rapid, and accurate analysis that interprets and correlates nucleotide polymorphisms for public health practice presents many challenges. To this end we have created a modular genomics analysis toolkit that can easily integrate diverse data streams and couple analysis with an array of visualization platforms.
Objective
To develop a modular approach to infectious disease genomic analysis that can easily integrate with public health analytics systems. Using dynamic approaches to genomic sequence analysis, relevant whole genome data can be quickly and accurately visualized and correlated, using a minimum of computational resources. We propose to develop visualization modules that integrate disparate data sources including integrate geospatial location metadata with associated epidemiological factors to enable faster outbreak identification and enhance surveillance.