Quantitative comparison among microbiomes can link microbial beta-diversity to environmental features, thus enabling prediction of ecosystem properties or dissection of host-microbiome interaction. However, to compute beta-diversity, current methods mainly employ the entire community profiles of taxa or functions, which can miss the subtle differences caused by low-abundance community members that may play crucial roles in the properties of interest. In this work, I review the distance metrics and search engines that we developed to match microbiomes at a large scale based on whole-community-level similarities, as well as their limitations in tackling the microbiome changes caused by less abundant community features. Then I propose the concept of microbiome "local alignment," including an algorithm to measure microbiome similarity on specific fractions of biodiversity and an indexing strategy for rapidly fetching microbiome local-alignment matches from the data repository.
Keywords: beta-diversity; distance metrics; local alignment; microbiome; search engine.