A cubic algorithm for the generalized rank median of three genomes

• Published in: Algorithms for molecular biology Vol. 14; no. 1; p. 16
• Main Authors: Chindelevitch, Leonid, La, Sean, Meidanis, Joao
• Format: Journal Article
• Language: English
• Published: London BioMed Central 26.07.2019; BMC
• Subjects: Algorithms; Ancestral genome reconstruction; Bioinformatics; Biomedical and Life Sciences; Cellular and Medical Topics; Comparative genomics; Computational Biology/Bioinformatics; Life Sciences; Phylogenetics; Physiological; Rank distance; RECOMB-CG; Comparative genomics; Phylogenetics; Ancestral genome reconstruction; Rank distance
• ISSN: 1748-7188; 1748-7188
• DOI: 10.1186/s13015-019-0150-y

Background The area of genome rearrangements has given rise to a number of interesting biological, mathematical and algorithmic problems. Among these, one of the most intractable ones has been that of finding the median of three genomes, a special case of the ancestral reconstruction problem. In this work we re-examine our recently proposed way of measuring genome rearrangement distance, namely, the rank distance between the matrix representations of the corresponding genomes, and show that the median of three genomes can be computed exactly in polynomial time O ( n ω ) , where ω ≤ 3 , with respect to this distance, when the median is allowed to be an arbitrary orthogonal matrix. Results We define the five fundamental subspaces depending on three input genomes, and use their properties to show that a particular action on each of these subspaces produces a median. In the process we introduce the notion of M -stable subspaces. We also show that the median found by our algorithm is always orthogonal, symmetric, and conserves any adjacencies or telomeres present in at least 2 out of 3 input genomes. Conclusions We test our method on both simulated and real data. We find that the majority of the realistic inputs result in genomic outputs, and for those that do not, our two heuristics perform well in terms of reconstructing a genomic matrix attaining a score close to the lower bound, while running in a reasonable amount of time. We conclude that the rank distance is not only theoretically intriguing, but also practically useful for median-finding, and potentially ancestral genome reconstruction.