Dna as x: An information-coding-based model to improve the sensitivity in comparative gene analysis

Abstract

In recent studies [1–3], lots of hidden homology in DNA genome are not found by current comparative tools despite decades of research. Many scholars modeled the genome as a monotonous string, which limits and probably obstructs the discovery of some significant patterns. We propose an information-coding-based model called DNA As X (DAX) to improve the sensitivity in comparative genomic studies by integrating the principles and concepts of other disciplines including information coding theory and signal processing into genome analysis. The proposed DNA As X model uses character-analysis-free (CAF) techniques, where X is the intermediate for analysis that can be digit, code, signal, vector, tree, graph network and so on. It provides novel and comprehensive perspectives to further analyze and recognize the critical patterns hidden in DNA genomes. Comparing with traditional characteranalysis-based (CAB) methods, DAX not only enriches the tools and the knowledge library of computational biology but also extends the domain from 1-D character string analysis to 2-D spatial/temporal domain. Furthermore, by applying the DAX model to the issue of exon prediction as an evaluation, we illustrate the insights behind this model. The experimental results show that the DAX methodology can improve the sensitivity in genome analysis by using the novel information-coding techniques.