Classification and clustering in metagenomics with unified data management and computational framework

The new generation of genomic technologies have allowed researchers to determine the collective DNA of organisms co-existing as communities across different environments. There is a need for the computational approaches to analyze and annotate the large volumes of available sequence data from such microbial communities (metagenomes). In this work, we develop an efficient and accurate metagenome classification and clustering approaches that reduce the computational complexity associated with comparing sequences. Our empirical results show the strength of the developed approaches in comparison to state-of-the-art algorithms with regards to computational efficiency and accuracy. We are also developing a web based metabiome portal that contains different computational tools and is used for the scalable, accurate, and easy analysis of large volumes of data available for the human metabiome.