Sequence alignment, mutual information, and dissimilarity measures for constructing phylogenies.

ABSTRACT:

Background

Existing sequence alignment algorithms use heuristic scoring schemes based on biological expertise, which cannot be used as objective distance metrics. As a result one relies on crude measures, like the p- or log-det distances, or makes explicit, and often too simplistic, a priori assumptions about sequence evolution. Information theory provides an alternative, in the form of mutual information (MI). MI is, in principle, an objective and model independent similarity measure, but it is not widely used in this context and no algorithm for extracting MI from a given alignment (without assuming an evolutionary model) is known. MI can be estimated without alignments, by concatenating and zipping sequences, but so far this has only produced estimates with uncontrolled errors,

SUBMITTER: Penner O 

PROVIDER: S-EPMC3014950 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature