<p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> <p>Bioinformatics, as a relatively young discipline, has grown up in a world of high-throughput
large volume data that requires automatic analysis to enable us to
stay on top of it all. As a response, the bioinformatics discipline has developed
strategies to find patterns in a ‘low signal : noise ratio’ environment. While the need
to process large amounts of information and extract hypotheses is both laudable
and inescapable, the pressures that such requirements have introduced can lead
to short cuts and misapprehensions. This is particularly the case with reference to
assumptions about the underlying evolutionary theories that are implicitly invoked
by the algorithms utilised in the analysis pipelines. The classic example is the misuse
of the term ‘homologous’ to mean ‘similar’ or even ‘functionally similar’, rather
than the correct definition of ‘having the same evolutionary origin’, which may
or may not imply similarity of function. In this review, we outline some of the
common phylogenetic questions from a bioinformatics perspective that can be better
addressed with a deeper understanding of evolutionary principles and show, with
examples from the amidohydrolase and Toll families, that quite different conclusions
can be drawn if such approaches are taken. This review focuses on the importance
of the underlying evolutionary biology, rather than assessing the merits of different
phylogenetic techniques. The relative merits of <italic>a priori</italic> and <italic>a posteriori</italic> inclusion of
biological information are discussed.</p>
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Comp Funct Genomics. 2004 Mar; 5(2):128-146