I'll be away from the interwebs for the next few weeks. Alejandro Weinstein, Ravi Kiran B., Sri Hari B. H., Thomas Arildsen and I are now co-managers of the LinkedIn Compressive Sensing group which means there should not be any disruption in terms of accepting new members to the group as managers now cover the main regions of the world (Asia, America, Europe). The group now counts more than 523 members, who will be the 1000th ?
I featured their work on Monday with Comseq, they are now coming up with a new preprint and give a new meaning to BCS: it's not Bryan-College-Station, nor Bayesian Compressive Sensing, it's Bacterial Compressed Sensing, a whole new program....Bacterial Community Reconstruction Using A Single Sequencing Reaction by Amnon Amir, Or Zuk. The abstract reads:
The attendant matlab BCS code is here.
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.