Heuristics based on Adjacency Graph Packing for DCJ Distance Considering Intergenic Regions

TL;DR

This paper introduces heuristics based on adjacency graph packing, incorporating intergenic regions and genome complexities, to improve DCJ distance estimation in genome comparison, demonstrating superior results over random strategies.

Contribution

It presents novel heuristics, including a greedy method and a genetic algorithm, tailored for the adjacency graph packing problem considering complex genomic features.

Findings

Heuristics outperform random strategies in artificial genome tests.

Genetic algorithm heuristic achieves better results than greedy approach.

Incorporating intergenic regions improves DCJ distance estimation.

Abstract

In this work, we explore heuristics for the Adjacency Graph Packing problem, which can be applied to the Double Cut and Join (DCJ) Distance Problem. The DCJ is a rearrangement operation and the distance problem considering it is a well established method for genome comparison. Our heuristics will use the structure called adjacency graph adapted to include information about intergenic regions, multiple copies of genes in the genomes, and multiple circular or linear chromosomes. The only required property from the genomes is that it must be possible to turn one into the other with DCJ operations. We propose one greedy heuristic and one heuristic based on Genetic Algorithms. Our experimental tests in artificial genomes show that the use of heuristics is capable of finding good results that are superior to a simpler random strategy.