Evolutionary segmentation of yeast genome

Segmentation algorithms differ from clustering algorithms with regard to how to deal with the physical location of genes throughout the sequence. Therefore, segments have to keep the original positions of consecutive genes, which is not a constraint for clustering algorithms. It has been proven that exist functional relations among neighbour-genes, so the localization of the boundaries between these functionally similar groups of genes has turned out an important challenge. In this paper, we present an evolutionary algorithm to segment the yeast genome

Statistical Test-Based Evolutionary Segmentation of Yeast Genome

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