On the Scalability of Supervised Learners in Metagenomics

Abstract

Metagenomics deals with the study of microorganisms such as prokaryotes that are found in samples from natural environments. The samples obtained from the environment may contain DNA from many different species of micro-organisms including bacteria and archea. Microorganisms are responsible for most of the symbiotic activity on earth. They are also responsible for the complex chemical reactions which take place on the surface of the earth, which help maintain earth's ecological balance. With the increase in genome sequencing projects there has been a considerable increase in the amount of assembled sequencing data. In this article, we apply supervised learners namely decision trees, Bayesian networks and decision tables to see how the performance degrades when the number of species present in the metagenomic sample increases. We also try to see how the performance of the metagenomic sample changes as the percentage of unknown sequences in the metagenomic sample is varied.