Olga Vasieva, Anatoly Sorokin, Marsel Murzabaev, Peter Babiak and Igor Goryanin*
In this case study, we validate a personalized approach to gut microbiome (GM) analysis and interpretation based on published association studies. We apply our ASAR data annotation and clustering package to a series of 10 sequenced GM’s from individuals of different ethnical and geographical backgrounds, age and health groups. The differentially presented and detectable taxonomic and functional signatures in each GM metagenome are used to predict the hosts’ characteristics via correlations established in published studies, and the predictions are being validated by available individual-associated metadata. We also test sensitivity of the routine annotation and data clustering pipeline to an individual and family-linked signatures in GM structure and functionalities, when applied to a limited number of varying samples. The number of samples was sufficient to demonstrate 2 main types of GM composition, based on Bacteroides or Prevotella as main abundant genera, limitation of a variety of taxa as a result of antibiotics application, clustering of family members’ GM metagenomes both in taxonomic and in functional space, individual signatures related to chronic diseases and pharmacological interventions, and elements of ethnicityrelated characteristics in the metagenomes. The method and logical algorithms of the analysis applied here may be utilized in rather computational pipeline for a personalized microbiome analyses, and their potential useful outputs and limitations are being discussed.
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Journal of Computer Science & Systems Biology received 2279 citations as per Google Scholar report
