bioRxivBioinformaticsPreprint
WITOD: A Tool for Within-Taxon Operational Taxonomic Unit Diversity Analysis
Abstract
MotivationTaxonomic analysis of microbiomes based on 16S rRNA amplicons has so far been usually restricted to abundance analysis of operational taxonomic units (OTUs), which are usually mapped to genus, as the furthest level for a balance between accuracy and speed. Biodiversity within taxa has been little studied, due to lack of proper computational tools. Within-taxon diversity reflects genetic polymorphism within the taxa to reveal whether there are potentially more variants/lineages for some microbiome-associated traits, e.g., diseases or bioconversion efficiency. The diversity will aid diagnosis decisions.\n\nResultsHere we introduce a tool, WITOD, for the WIthin-Taxon Operational taxonomic unit Diversity analysis. WITOD works on the alignment of all the OTU sequences within a taxon to get a non-redundant alignment with consensus regions on both ends (due to the conservation of the 16S regions used). Then the relative abundance of identical OTUs is combined; more specific taxonomy is obtained through BLASTn of these OTU sequences against the Silva database. One of the outputs is an OTU table with these unique OTUs with new taxonomy, combined relative abundance. In addition, another table can be constructed which we call the diversity table that associates the number of OTUs for a specific Taxa and the relative abundance in that environment. This can be used to further discover the importance of differing diversity in a given environment.\n\nConclusionsIn this paper, we have introduced a python program WITOD which is a computational tool to analyze the within-taxon biodiversity of microbiome composition. It will be useful in revealing the genetic polymorphism in general and aiding identifying the causative human pathogens or environmental disease vectors. WITOD is available at https://github.com/johncava/WITOD
- assay: metagenomics
- research focus: benchmarking
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
- Citations
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