Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
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A plethora of biologically useful information lies obscured in the genomes of organisms. Encoded within the genome of an organism is the information about its evolutionary history. Evolutionary signals are scattered throughout the genome. Bioinformatics approaches are frequently invoked to deconstruct the evolutionary patterns underlying genomes, which are difficult to decipher using traditional laboratory experiments. However, interpreting constantly evolving genomes is a non-trivial task for bioinformaticians. Processes such as mutations, recombinations, insertions and deletions make genomes not only heterogeneous and difficult to decipher but also renders direct sequence comparison less effective. Here we present a brief overview of the sequence comparison methods with a focus on recently proposed alignment-free sequence comparison methods based on Shannon information entropy. Many of these sequence comparison methods have been adapted to construct phylogenetic trees to infer relationships among organisms.