Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Selected papers from the Seventh Asia-Pacific Bioinformatics Conference (APBC 2009)

Open Access Open Badges Research

Maximum independent sets of commuting and noninterfering inversions

Krister M Swenson1, Yokuki To2, Jijun Tang2 and Bernard ME Moret13*

Author Affiliations

1 Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, Switzerland

2 Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA

3 Swiss Institute of Bioinformatics, Lausanne, Switzerland

For all author emails, please log on.

BMC Bioinformatics 2009, 10(Suppl 1):S6  doi:10.1186/1471-2105-10-S1-S6

Published: 30 January 2009



Given three signed permutations, an inversion median is a fourth permutation that minimizes the sum of the pairwise inversion distances between it and the three others. This problem is NP-hard as well as hard to approximate. Yet median-based approaches to phylogenetic reconstruction have been shown to be among the most accurate, especially in the presence of long branches. Most existing approaches have used heuristics that attempt to find a longest sequence of inversions from one of the three permutations that, at each step in the sequence, moves closer to the other two permutations; yet very little is known about the quality of solutions returned by such approaches.


Recently, Arndt and Tang took a step towards finding longer such sequences by using sets of commuting inversions. In this paper, we formalize the problem of finding such sequences of inversions with what we call signatures and provide algorithms to find maximum cardinality sets of commuting and noninterfering inversions.


Our results offer a framework in which to study the inversion median problem, faster algorithms to obtain good medians, and an approach to study characteristic events along an evolutionary path.