Towards a Taxonomical Consensus: Diversity and Richness Inference from Large Scale rRNA gene Analysis

23 Population analysis is persistently challenging but important, leading to the 24 determination of diversity and function prediction of microbial community members. 25 Here we detail our bioinformatics methods for analyzing population distribution and 26 diversity in large microbial communities. This was achieved via (i) a homology based 27 method for robust phylotype determination, equaling the classification accuracy of the 28 Ribosomal Database Project (RDP) classifier, but providing improved associations of 29 closely related sequences; (ii) a comparison of different clustering methods for 30 achieving more accurate richness estimations. 31 Our methodology, which we developed using the RDP vetted 16S rRNA gene 32 sequence set, was validated by testing it on a large 16S rRNA gene dataset of 33 approximately 2300 sequences, which we obtained from a soil microbial community 34 study. We concluded that the best approach to obtain accurate phylogenetics profile of 35 large microbial communities, based on 16S rRNA gene sequence information, is to 36 apply an optimized blast classifier. This approach is complemented by the grouping of 37 closely related sequences, using complete linkage clustering, in order to calculate 38 richness and evenness indices for the communities. 39 3 INTRODUCTION 40