Bacterial Community Reconstruction Using A Single Sequencing Reaction

TL;DR

This paper introduces a novel method using compressive sensing to reconstruct bacterial community composition from a single sequencing reaction, enabling broad and efficient bacterial profiling.

Contribution

It presents a new approach leveraging compressive sensing theory for bacterial mixture reconstruction from minimal sequencing data.

Findings

Simulations show accurate reconstruction with a few hundred base-pairs of 16S rRNA gene.

Method can identify tens of species in mixtures with thousands of known bacteria.

Initial experimental results are promising for practical applications.

Abstract

Bacteria are the unseen majority on our planet, with millions of species and comprising most of the living protoplasm. While current methods enable in-depth study of a small number of communities, a simple tool for breadth studies of bacterial population composition in a large number of samples is lacking. We propose a novel approach for reconstruction of the composition of an unknown mixture of bacteria using a single Sanger-sequencing reaction of the mixture. This method is based on compressive sensing theory, which deals with reconstruction of a sparse signal using a small number of measurements. Utilizing the fact that in many cases each bacterial community is comprised of a small subset of the known bacterial species, we show the feasibility of this approach for determining the composition of a bacterial mixture. Using simulations, we show that sequencing a few hundred base-pairs of the 16S rRNA gene sequence may provide enough information for reconstruction of mixtures containing tens of species, out of tens of thousands, even in the presence of realistic measurement noise. Finally, we show initial promising results when applying our method for the reconstruction of a toy experimental mixture with five species. Our approach may have a potential for a practical and efficient way for identifying bacterial species compositions in biological samples.