Towards a highly efficient diversity census of the prokaryotic biosphere: a group testing approach

Exploring the microbial biosphere has grown exponentially in recent years, although we are far from understanding its entirety. We present the adiversity censusa problem of exploring all bacterial species in a large cohort of specimens, and detecting a specimen that contains each species. The naive approach to this problem is to sequence each specimen, thus requiring costly sample preparation steps.\n\nWe suggest an orders of magnitude more efficient approach for diversity censusing. Specimens are pooled according to a predefined design and standard 16S rRNA sequencing is performed over each pool. For each bacterial species, from the ultra-rare to the most common, the algorithm detects a single specimen that contains the bacterial species. The approach can be applied to large cohorts of monomicrobial cultures or to complex samples containing a mixture of organisms.\n\nWe model the experimental procedure and show via in silico simulations that the approach enables censusing more than 95% of the species while taking 10 - 70 fold less resources. Simulating experiments using real samples display the utility in censusing large cohorts of samples.\n\nDiversity censusing presents a novel problem in the mathematical field of group testing that may also be applied in other biological problems and in other domains.