  Microbial communities are widely studied using high-throughput sequencing
techniques, such as 16S rRNA gene sequencing. These techniques have attracted
biologists as they offer powerful tools to explore microbial communities and
investigate their patterns of diversity in biological and biomedical samples at
remarkable resolution. However, the accuracy of these methods can negatively
affected by the presence of contamination. Several studies have recognized that
contamination is a common problem in microbial studies and have offered
promising computational and laboratory-based approaches to assess and remove
contaminants. Here we propose a novel strategy, MI-based (mutual information
based) filtering method, which uses information theoretic functionals and graph
theory to identify and remove contaminants. We applied MI-based filtering
method to a mock community data set and evaluated the amount of information
loss due to filtering taxa. We also compared our method to commonly practice
traditional filtering methods. In a mock community data set, MI-based filtering
approach maintained the true bacteria in the community without significant loss
of information. Our results indicate that MI-based filtering method effectively
identifies and removes contaminants in microbial communities and hence it can
be beneficial as a filtering method to microbiome studies. We believe our
filtering method has two advantages over traditional filtering methods. First,
it does not required an arbitrary choice of threshold and second, it is able to
detect true taxa with low abundance.
