Abstract
The computation of genomic distances has been a very active field of computational comparative genomics over the past 25 years. Substantial results include the polynomial-time computability of the inversion distance by Hannenhalli and Pevzner in 1995 and the introduction of the double cut and join distance by Yancopoulos et al. in 2005. Both results, however, rely on the assumption that the genomes under comparison contain the same set of unique markers (syntenic genomic regions, sometimes also referred to as genes). In 2015, Shao et al. relax this condition by allowing for duplicate markers in the analysis. This generalized version of the genomic distance problem is NP-hard, and they give an integer linear programming (ILP) solution that is efficient enough to be applied to real-world datasets. A restriction of their approach is that it can be applied only to balanced genomes that have equal numbers of duplicates of any marker. Therefore, it still needs a delicate preprocessing of the input data in which excessive copies of unbalanced markers have to be removed. In this article, we present an algorithm solving the genomic distance problem for natural genomes, in which any marker may occur an arbitrary number of times. Our method is based on a new graph data structure, the multi-relational diagram, that allows an elegant extension of the ILP by Shao et al. to count runs of markers that are under- or over-represented in one genome with respect to the other and need to be inserted or deleted, respectively. With this extension, previous restrictions on the genome configurations are lifted, for the first time enabling an uncompromising rearrangement analysis. Any marker sequence can directly be used for the distance calculation. The evaluation of our approach shows that it can be used to analyze genomes with up to a few 10,000 markers, which we demonstrate on simulated and real data.
Highlights
The study of genome rearrangements has a long tradition in comparative genomics
In 2015, Shao et al relax this condition by allowing for duplicate markers in the analysis. This generalized version of the genomic distance problem is NP-hard, and they give an integer linear programming (ILP) solution that is efficient enough to be applied to real-world datasets
We present an algorithm solving the genomic distance problem for natural genomes, in which any marker may occur an arbitrary number of times
Summary
The study of genome rearrangements has a long tradition in comparative genomics. A central question is how many (and what kind of) mutations have occurred between the genomic sequences of two individual genomes. Our ILP formulation is based on the one from Shao et al (2015), but with an efficient extension that allows to count runs of markers that are underor over-represented in one genome with respect to the other, so that the pre-existing model of minimizing the distance allowing DCJ and indel operations (Braga et al, 2011) can be adapted to our problem. With this extension, once we have the genome markers, no other restriction on the genome configurations is imposed. This article is an extended version of earlier work that was presented at RECOMB 2020 (Bohnenkamper et al, 2020)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have