Accurate and high-resolution classification of synthetic and natural community amplicon data

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Over the past decade, numerous computational methods for the analysis of amplicon data have been developed and successfully apply to the study of microbial communities associated with plants. Despite improvements in speed and accuracy, current computational methods suffer from a number of limitations, the most severe of which are associated with the use of the artificial constructs designated Operational Taxonomic Units (OTUs). These groups of sequences, estimated to originate from the same microbial species, are based on arbitrary sequence similarity thresholds which do not correspond to meaningful functional biological entities. Here we propose to implement an accurate and precise algorithm specifically designed to process amplicon data obtained from experiments with synthetic communities by taking advantage of our precise knowledge of the input microbial strains. We will also extend this algorithm for processing natural community amplicon data by using a recursive classification approach and a dynamic database scheme. Finally, we will perform experiments with mock synthetic communities to validate these algorithms and optimize their implementation.