MCaVoH: A toolkit for mining, classification and visualization of human microbiota

Human body is the home for a large number of microbes. The complexity of enterotype depends on the body site. Microbial communities in various samples from different regions are being classified on the basis of 16S rRNA gene sequences. With the improvement in sequencing technologies various computational methods have been used for the analysis of microbiome data. Despite several available machine learning techniques there is no single platform available which could provide several techniques for clustering, multiclass classification, comparative analysis and the most significantly the identification of the subgroups present within larger groups of human microbial communities. We present a tool named MCaVoH for this purpose which performs clustering and classification of 16S rRNA sequence data and highlight various groups. Our tool has an added facility of biclustering which produces local group of communities present within larger groups (clusters). The core objective of our work was to identify the interaction between various bacterial species along with monitoring the composition and variations in microbial communities. MCaVoH also evaluates the performance and efficiency of different techniques using comparative analysis. The results are visualized through different plots and graphs. We implemented our tool in MATLAB. We tested our tool on several real and simulated 16S rRNA data sets and it outperforms several existing methods. Our tool provides a single platform for using multiple clustering, classification algorithms, local community identification along with their comparison which has not been done so far. Tool is available at https://sourceforge.net/projects/mcavoh/.