title: MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data.
abstract: As more than 90% of species in a microbial community could not be isolated and cultivated, the metagenomic methods have become one of the most important methods to analyze microbial community as a whole. With the fast accumulation of metagenomic samples and the advance of next-generation sequencing techniques, it is now possible to qualitatively and quantitatively assess all taxa (features) in a microbial community. A set of taxa with presence/absence or their different abundances could potentially be used as taxonomical biomarkers for identification of the corresponding microbial community's phenotype. Though there exist some bioinformatics methods for metagenomic biomarker discovery, current methods are not robust, accurate and fast enough at selection of non-redundant biomarkers for prediction of microbial community's phenotype. In this study, we have proposed a novel method, MetaBoot, that combines the techniques of mRMR (minimal redundancy maximal relevance) and bootstrapping, for discover of non-redundant biomarkers for microbial communities through mining of metagenomic data. MetaBoot has been tested and compared with other methods on well-designed simulated datasets considering normal and gamma distribution as well as publicly available metagenomic datasets. Results have shown that MetaBoot was robust across datasets of varied complexity and taxonomical distribution patterns and could also select discriminative biomarkers with quite high accuracy and biological consistency. Thus, MetaBoot is suitable for robustly and accurately discover taxonomical biomarkers for different microbial communities.
authors.affiliation: Bioinformatics Group of Single Cell Center, Shandong Key Laboratory of Energy Genetics and CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences , Qingdao, Shandong Province , People's Republic of China ; University of Chinese Academy of Sciences , Beijing , People's Republic of China.
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Wang
authors.affiliation: Bioinformatics Group of Single Cell Center, Shandong Key Laboratory of Energy Genetics and CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences , Qingdao, Shandong Province , People's Republic of China ; CUDA Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences , Qingdao, Shandong Province , People's Republic of China.
authors.forename: Xiaoquan
authors.initials: X
authors.lastname: Su
authors.affiliation: Bioinformatics Group of Single Cell Center, Shandong Key Laboratory of Energy Genetics and CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences , Qingdao, Shandong Province , People's Republic of China ; Department of Statistics, University of California , Riverside, CA , USA.
authors.forename: Xinping
authors.initials: X
authors.lastname: Cui
authors.affiliation: Bioinformatics Group of Single Cell Center, Shandong Key Laboratory of Energy Genetics and CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences , Qingdao, Shandong Province , People's Republic of China ; University of Chinese Academy of Sciences , Beijing , People's Republic of China ; CUDA Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences , Qingdao, Shandong Province , People's Republic of China.
authors.forename: Kang
authors.initials: K
authors.lastname: Ning
keywords: Biomarker; Bootstrap; Machine learning; Metagenomic; Taxonomical distribution pattern; mRMR