Hierarchical and spatially explicit clustering of DNA sequences with BAPS software

Area of Science:

• Population genetics
• Bioinformatics
• Molecular variation analysis

Background:

• DNA sequencing technologies have made phylogeographical analyses widely accessible.
• Bayesian model-based clustering is effective for identifying patterns and subtle differences in molecular variation data.
• Existing tools may lack capabilities for spatially explicit or hierarchical clustering of genetic data.

Purpose of the Study:

• To introduce two significant upgrades to the Bayesian Analysis of Population Structure (BAPS) software.
• To enable spatially explicit modeling of DNA sequence variation.
• To facilitate hierarchical clustering for revealing nested genetic population structures.

Main Methods:

• Development of upgraded BAPS software incorporating spatially explicit modeling.
• Implementation of hierarchical clustering algorithms for DNA sequence data.
• Integration with Google Maps for visualizing spatial clustering results via http://www.spatialepidemiology.net/.

Main Results:

• Demonstrated spatially explicit modeling using Borrelia burgdorferi sequence data.
• Illustrated the utility of hierarchical clustering for analyzing metapopulation structure in bacteria with high horizontal gene transfer.
• Provided freely available software at http://www.helsinki.fi/bsg/software/BAPS/.

Conclusions:

• The upgraded BAPS software offers powerful new capabilities for phylogeographical and population structure analyses.
• Spatially explicit and hierarchical clustering methods enhance the detection of genetic patterns and population structures.
• These advancements facilitate a deeper understanding of molecular variation and evolutionary processes in diverse organisms.