A Novel Approach to Mine for Genetic Markers via Comparing Class Frequency Distributions of Maximal Repeats Extracted from Tagged Whole Genomic Sequences

The cost to extract one new biomarker within genomic sequences is very huge. This chapter adopts a scalable approach, developed previously and based on MapReduce programming model, to extract maximal repeats from a huge amount of tagged whole genomic sequences and meanwhile computing the similarities of sequences within the same class and the differences among the other classes, where the types of classes are derived from those tags. The work can be extended to any kind of genomic sequential data if one can have the organisms into several disjoint classes according to one specific phenotype, and then collect the whole genomes of those organisms. Those patterns, for example, biomarkers, if exist in only one class, with distinctive class frequency distribution can provide hints to biologists to dig out the relationship between that phenotype and those genomic patterns. It is expected that this approach may provide a novel direction in the research of biomarker extraction via whole genomic sequence comparison in the era of post genomics.