  Background: Alignment of biological sequences such as DNA, RNA or proteins is
one of the most widely used tools in computational bioscience. All existing
alignment algorithms rely on heuristic scoring schemes based on biological
expertise. Therefore, these algorithms do not provide model independent and
objective measures for how similar two (or more) sequences actually are.
Although information theory provides such a similarity measure -- the mutual
information (MI) -- previous attempts to connect sequence alignment and
information theory have not produced realistic estimates for the MI from a
given alignment.
  Results: Here we describe a simple and flexible approach to get robust
estimates of MI from {\it global} alignments. For mammalian mitochondrial DNA,
our approach gives pairwise MI estimates for commonly used global alignment
algorithms that are strikingly close to estimates obtained by an entirely
unrelated approach -- concatenating and zipping the sequences.
  Conclusions: This remarkable consistency may help establish MI as a reliable
tool for evaluating the quality of global alignments, judging the relative
merits of different alignment algorithms, and estimating the significance of
specific alignments. We expect that our approach can be extended to establish
further connections between information theory and sequence alignment,
including applications to local and multiple alignment procedures.
