Statistical Test-Based Evolutionary Segmentation of Yeast Genome

Abstract

Segmentation algorithms emerge observing fluctuations of DNA sequences in alternative homogeneous domains, which are named segments [1]. The key idea is that two genes that are controlled by a single regulatory system should have similar expression patterns in any data set. In this work, we present a new approach based on Evolutionary Algorithms (EAs) that differentiate segments of genes, which are represented by its level of meiotic recombination. We have tested the algorithm with the yeast genome [2][3] because this organism is very interesting for the research community, as it preserves many biological properties from more complex organisms and it is simple enough to run experiments. We have a file with about 6100 genes, divided into sixteen yeast chromosomes (N). Each gene is a row of the file. Each column of file represents a genomic characteristic under speci.c conditions (in this case, only the activity of meiotic recombination). The goal is to group consecutive genes properly differentiated from adjacent segments. Each group will be a segment of genes, as it will maintain the physical location within the genome. To measure the relevance of segments the Mann–Whitney statistical test has been used.