Automatic Discovery of Sub-molecular Sequence Domains in Multi-aligned Sequences: A Dynamic Programming Algorithm for Multiple Alignment Segmentation

Automatic identification of sub-structures in multi-aligned sequences is of great importance for effective and objective structural/functional domain annotation, phylogenetic treeing and other molecular analyses. We present a segmentation algorithm that optimally partitions a given multi-alignment into a set of potentially biologically significant blocks, or segments. This algorithm applies dynamic programming and progressive optimization to the statistical profile of a multi-alignment in order to optimally demarcate relatively homogenous sub-regions. Using this algorithm, a large multi-alignment of eukaryotic 16S rRNA was analyzed. Three types of sequence patterns were identified automatically and efficiently: shared conserved domain; shared variable motif; and rare signature sequence. Results were consistent with the patterns identified through independent phylogenetic and structural approaches. This algorithm facilitates the automation of sequence-based molecular structural and evolutionary analyses through statistical modeling and high performance computation.