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 "diversity census" problem of exploring all bacterial species in a large cohort of specimens, and detecting a specimen that contains each species (see Figure 1). The naive approach to this problem is to sequence each specimen, thus requiring costly sample preparation steps. We 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 (see a cartoon in Figure 2). We 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. Diversity 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. The manuscript is under review and a preprint appeared as http://www.biorxiv.org/content/early/2017/07/23/167502 Figure 1: A bacterial diversity census The left column presents cartoons of the bacterial identities in a cohort of 16 monomicrobial cultures (upper subplot) or samples (lower subplot). This ground truth (shown as opaque) is, of course, provided only for demonstration purposes, and is unknown in practice. The right column shows the required output of a diversity census of each cohort of specimens. A. Diversity census of a monomicrobial culture repository results in detecting a single culture that contains each bacterium in the cohort (depicted by the bacterial species in the center of each dish). Since the cohort contains four bacterial species diversity censusing should detect four cultures. Cultures that contain the same bacterium are `symmetric' for the purpose of diversity censusing, e.g. each of the eight cultures that contain the `green-bacilli-like' bacterium may be equally detected. B. Diversity censusing a repository of samples, where each sample contains many species, should detect a sample for each bacterial species across the cohort. For each bacterium, censusing should preferably detect a sample in which the bacterium abundance is high, and hence samples that contain the same bacterium are not 'symmetric'. There are eight 'species' in this cartoon and seven detected samples, since two bacteria were detected in the same sample. Figure 2: A diagram of the suggested approach A. Diversity censusing culture repositories. The input is a set of cultures ($C1-C4$), where each culture contains a single unknown species (i), species in each culture appear as opaque to designate that their identities are unknown). Equal amounts of material from each culture are pooled according to a predefined design (ii), then DNA is extracted and 16S rRNA NGS is performed (iii). The sets of bacteria identified in each pool (iv) are analyzed to detect one culture for each bacterial species, independent of the their abundance (v). B. Diversity censusing repositories of samples, where each sample (S1-S4) contains many species (i). The flowchart is similar to the case of cultures. The approach detects a representative sample for each bacterial species that appears in at least one sample and whose abundance is higher than some predefined threshold (v). The assigned sample to the 'triangle' and 'square' bacteria was the same one (S3).