Microbes exists everywhere. Current generation of genomic technologies have allowed researchers to determine the collective DNA sequence of all microorganisms co-existing together. In this paper, we present some of the challenges related to the analysis of data ob- tained from the community genomics experiment (commonly referred by metagenomics), advocate the need of machine learning techniques and highlight our contributions related to development of supervised and un- supervised techniques for solving this complex, real world problem. 1 Background Advances in genome-sequencing have transformed the manner of characterizing large populations of microbial communities, that are ubiquitous across several environments. The process of "metagenomics" involves sequencing of the genetic material of all organisms co-existing within ecosystems ranging from ocean, soil and the human body. (can be referred to as community genomics). Orthogonally, proteomics and mass spectrometry allow the study of bio-transformations due to these microbial communities in the form of metaproteomes and metabolomes, respectively. Several researchers and clinicians have embarked on studying the pathogenic role played by the microbiome (i.e., the collection of microbial or- ganisms within the human body) with respect to human health and disease conditions. In a similar effort, other groups of researchers are using the metage- nomics technology to characterize different ecological environments across the planet (also referred by "Earth Microbiome"). Annotating microbial sequences (reads or quasi-assembled contigs) within a sample is a challenging task due to the unknown, diverse and complex nature of microbial communities within the different environments. There is a critical need to develop mining methods that can characterize metagenome data in terms of taxonomy, function and metabolic potential, and correlate the multi-modal, microbial data to clinical or environmental metadata. We present our ongoing efforts that have lead to the development of novel supervised learning approaches and scalable clustering methods to solve these real world challenges.
['Huzefa Rangwala', 'Anveshi Charuvaka', 'Zeehasham Rasheed']
Machine Learning Approaches for Metagenomics