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Taxonomy Tree Alignment

A common use case in Bioinformatics is given a set of samples from an experiment, a taxonomy tree is produced depending on the similarity between them. There can be many such taxonomy trees produced from different sources using different experimental methods. We attempt to study the similarities/differences between these taxonomy based graph databases and try to align them. This alignment would eventually lead to a global merged database which contains the knowledge acquired from multiple taxonomy tree databases. This paper focuses on the study of two popular taxonomy datasets Greengenes and RDP databases. Next we propose an approach to tackle the tree alignment problem and show the statistics of our alignment algorithm. 1. Problem Introduction Taxonomy trees provide us with a great way to study the relationship between various types of gene sequences and understand the similarities in their behavior at different levels. They can also be used to group different samples into groups based on the information available by clustering similar samples into same/closer groups. There are various different sources for locating taxonomy trees generated over same/similar samples. Each of these might use different methodologies, experiments to isolate gene sequences from the cultures and then perform different types of clusterings or other techniques to generate a taxonomy tree. Hence, it's possible that these databases might have significantly different taxonomic information about the same gene sequences. The problem we are trying to solve here is finding a match between these databases and merging into a common database which captures the combined wisdom of input sources. This problem, as explained in detail in later sections, faces a lot of challenges due to structural differences in the taxonomies of different databases and we believe that identifying the right set of parameters would be the key to aligning taxonomy trees efficiently and accurately.