Abstract
Structural variation in genomes can be revealed by many (dis)similarity measures. Rearrangement operations, such as the so called double-cut-and-join (DCJ), are large-scale mutations that can create complex changes and produce such variations in genomes. A basic task in comparative genomics is to find the rearrangement distance between two given genomes, i.e., the minimum number of rearragement operations that transform one given genome into another one. In a family-based setting, genes are grouped into gene families and efficient algorithms have already been presented to compute the DCJ distance between two given genomes. In this work we propose the problem of computing the DCJ distance of two given genomes without prior gene family assignment, directly using the pairwise similarities between genes. We prove that this new family-free DCJ distance problem is APX-hard and provide an integer linear program to its solution. We also study a family-free DCJ similarity and prove that its computation is NP-hard.
Highlights
Genomes are subject to mutations or rearrangements in the course of evolution
To be more consistent with the comparative genomics literature, where distance measures are more common than similarities, here we propose a family-free DCJ distance
We propose an integer linear program (ILP) formulation to compute the family-free DCJ distance between two given genomes
Summary
Genomes are subject to mutations or rearrangements in the course of evolution. Typical large-scale rearrangements change the number of chromosomes and/or the positions and orientations of genes. Examples of such rearrangements are inversions, translocations, fusions and fissions. A classical problem in comparative genomics is to compute the rearrangement distance, that is, the minimum number of rearrangements required to transform a given genome into another given genome [1]. In order to study this problem, one usually adopts a high-level view of genomes, in which only “relevant” fragments of the DNA (e.g., genes) are taken into consideration. A pre-processing of the data is required, so that we can compare the content of the genomes
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