Statistical characterizations of genome dynamics

Recent studies of sequenced genomes have shown that the rates of different types of mutations tend to vary and co-vary along the nuclear
DNA. In order to provide a statistical characterization of this variation structure and of its association with features of the local genomic landscape, we have used a number of multivariate techniques on data from primate alignments (these include regression, linear and non-linear Principal Components, linear and non-linear Canonical Correlations). Most
recently we have used multivariate Hidden Markov Models to identify distinct mutational patterns and segment the human genome accordingly. Our
results provide crucial insights into genome dynamics, and the mutagenic role of various molecular mechanisms such as replication, recombination, repair and local chromatin environment. Moreover, we identify regions with
distinct mutational signatures whose locations, lengths and landscapes are evolutionarily meaningful.

This is joint work with Guru Ananda, Prabhani Kuruppummullage Don and Kateryna Makova of The Pennsylvania State University.