A0634
Title: Strain-GeMS: Optimization subspecies identification from microbiome data based on accurate variant modeling
Authors: Xinping Cui - University of California, Riverside (United States) [presenting]
Abstract: Subspecies identification is one of the most critical issue in microbiome studies, as it is directly related to the functions of the species as well as the whole microbial communities in response to the environmental stress and their feedbacks. However, identification of subspecies remains a challenge largely due to the small variation between different strains within the same species. Accurate identification of subspecies primarily rely on variant identification and categorization through microbiome data. However current SNP calling through microbiome data remain underdeveloped. We have proposed Strain-GeMS for subspecies identification from microbiome data, based on SNP calling with solid statistical model, as well as optimized subspecies identification procedure. Results on simulated, ab initio and in vivo datasets have shown that Strain-GeMS could always outperform other subspecies identification methods in terms of accuracy and coverage of the strains. With the rapidly increasing amount of microbiome samples, and the needs for subspecies identification, we believe that Strain-GeMS could become a key tool towards elucidating of subtle differences among subspecies in a microbial community.
