MIMOSA: Algorithms for Microbial Profiling

A significant goal of the study of metagenomes obtained from an environment is to find the microbial diversity and the abundance of each organism in the community. Phylotyping and binning methods which address this problem generally operate using either marker sequences or by classifying each genome fragment individually. However, these approaches might not use all the information contained in the metagenome. We propose an approach based on a Multiple Input Multiple Output (MIMO) communication system model. Results from two different implementations of this approach, one using DNA-DNA hybridization simulations and one using short read mapping are evaluated using simulated and actual metagenomes and compared with other methods of phylotyping. The proposed approaches generally performed better under different scenarios including pathogen detection tasks of community complexity and low and high sequencing coverage while being highly computationally effective. The resulting framework can be integrated to metagenome analysis pipelines for phylogenetic diversity estimation. The approach is modular so that techniques other than hybridization simulations and short read mapping may be integrated. We have observed that even for low coverage samples, the method provides accurate estimates. Therefore, the use of the proposed strategy could enable the task of exploring biodiversity with limited resources.