The most promising of current methods for computational gene prediction are those that utilize comparative information by incorporating evidence from the genomes of one or more additional organisms, or from expression data such as ESTs or proteins, or even by comparing and combining the predictions of other gene-finding systems (Guigó et al., 2006). In this chapter we review the various comparative methods which have been successfully employed to improve gene-finding accuracy, as well as several emerging techniques at the forefront of current gene-finding research.

In Figure 9.1 we summarize the major approaches to comparative gene finding that have been applied to date, and that we will be considering shortly. As it may be argued that all scientific and engineering endeavors tend to advance in an evolutionary manner – i.e., by modifying an existing theory or method so as to obtain an incremental improvement in descriptive, predictive, or manipulative power – we draw attention to the similarity of Figure 9.1 to a phylogeny or family tree (section 1.2), in which case we can view the paths through the figure as evolutionary lineages – whether lineages of software, or of ideas embedded in software. Furthermore, as it is certainly the case that comparative methods are at this point in time still evolving, the following exposition will necessarily be less dogmatic than our descriptions of ab initio methods given in the foregoing chapters, relying to a correspondingly greater degree on the presentation of case studies to illustrate the various methods that have been put forth.