Whole-Genome Phylogeny by virtue of Unic Subwords

With the progress of modern sequencing technologies a number of complete genomes is now available. Traditional motif discovery tools cannot handle this massive amount of data, therefore the comparison of complete genomes can be carried out only with ad hoc methods. In this work we propose a distance function based on subword compositions, which extends the Average Common Subword approach (ACS) of Ulitsky et al. [17]. ACS is closely related to the cross entropy estimated between two entire genome sequences, and thus to some set of “independent” subwords, namely the irredundant common subwords. Then, we filter the irredundant common subwords by means of underlying-paired motifs, which relate to each other regions of two genome sequences. This set of motifs is, by construction, linear in the size of input and without overlap; we call the selected motifs, underlying-paired irredundant common subwords, or simply "unic" subwords. Preliminary results show the validity of our method, and suggest novel computational approaches for analyzing the evolution of genomes.