Shental, N. | Search bioRxiv

bioRxiv2017

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.

microbiology↗

bioRxiv2017

Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling

BackgroundMost of our knowledge about the remarkable microbial diversity on Earth comes from sequencing the 16S rRNA gene. The use of next-generation sequencing methods has increased sample number and sequencing depth, but the read length of the most widely used sequencing platforms today is quite short, requiring the researcher to choose a subset of the gene to sequence (typically 16-33% of the total length). Thus, many bacteria may share the same amplified region and the resolution of profiling is inherently limited. Platforms that offer ultra long read lengths, whole genome shotgun sequencing approaches, and computational frameworks formerly suggested by us and by others, all allow different ways to circumvent this problem yet suffer various shortcomings. There is need for a simple and low cost 16S rRNA gene based profiling approach that harnesses the short read length to provide a much larger coverage of the gene to allow for high resolution, even in harsh conditions of low bacterial biomass and fragmented DNA.\n\nResultsThis manuscript suggests Short MUltiple Regions Framework (SMURF), a method to combine sequencing results from different PCR-amplified regions to provide one coherent profiling. The de facto amplicon length is the total length of all amplified regions, thus providing much higher resolution compared to current techniques. Computationally, the method solves a convex optimization problem that allows extremely fast reconstruction and requires only moderate memory. We demonstrate the increase in resolution by in silico simulations and by profiling two mock mixtures and real-world biological samples. Reanalyzing a mock mixture from the Human Microbiome Project achieved about two-fold improvement in resolution when combing two independent regions. Using a custom set of six primer pairs spanning about 1200bp (80%) of the 16S rRNA gene we were able to achieve ~100 fold improvement in resolution compared to a single region, over a mock mixture of common human gut bacterial isolates. Finally, profiling of a Drosophila melanogaster microbiome using the set of six primer pairs provided a ~100 fold increase in resolution, and thus enabling efficient downstream analysis.\n\nConclusionsSMURF enables identification of near full-length 16S rRNA gene sequences in microbial communities, having resolution superior compared to current techniques. It may be applied to standard sample preparation protocols with very little modifications. SMURF also paves the way to high-resolution profiling of low-biomass and fragmented DNA, e.g., in the case of Formalin-fixed and Paraffin-embedded samples, fossil-derived DNA or DNA exposed to other degrading conditions. The approach is not restricted to combining amplicons of the 16S rRNA gene and may be applied to any set of amplicons, e.g., in Multilocus Sequence Typing (MLST).

bioinformatics↗