Abstract

It is well known that elements lying outside the coding regions of the human genome are involved in many human diseases. Therefore, the efforts to detect and characterize functional elements in the non-coding regions are rapidly increasing. Among many types of non-coding DNA, pseudogenes are sequences that share some similarities with their parental genes but have lost their ability to code for proteins. In this paper, we propose a methodology for detection and analysis of pseudogenes, based on transition probabilities of the nucleotides and their occurrences. The 1000 base pairs length downstream region of each potential pseudogene is analyzed in order to find a polyA tail and a polyadenylation signal. We implemented a Hidden Markov Model with the Viterbi algorithm to decode the upstream regions of the previously detected pseudogenes in order to search for CpG islands. In order to identify motif signals in the selected pseudogenes, we implemented the Gibbs sampling algorithm and we executed it on the flanking regions of some pseudogenes. Results demonstrate that the proposed methodology is an efficacious solution to detect new potential loci, especially when the query coverage of the alignment is shorter than the coding sequence. These loci can be classified to pseudogene fragments.