The Floor Is Lava: Halving Natural Genomes with Viaducts, Piers, and Pontoons

Bohnenkämper L. The Floor Is Lava: Halving Natural Genomes with Viaducts, Piers, and Pontoons. Journal of Computational Biology. 2024.Whole Genome Duplications (WGDs) are events that double the content and structure of a genome. In some organisms, multiple WGD events have been observed while loss of genetic material is a typical occurrence following a WGD event. The requirement of classic rearrangement models that every genetic marker has to occur exactly two times in a given problem instance, therefore, poses a serious restriction in this context. The Double-Cut and Join (DCJ) model is a simple and powerful model for the analysis of large structural rearrangements. After being extended to the DCJ-Indel model, capable of handling gains and losses of genetic material, research has shifted in recent years toward enabling it to handle natural genomes, for which no assumption about the distribution of markers has to be made. The traditional theoretical framework for studying WGD events is the Genome Halving Problem (GHP). While the GHP is solved for the DCJ model for genomes without losses, there are currently no exact algorithms utilizing the DCJ-Indel model that are able to handle natural genomes. In this work, we present a general view on the DCJ-Indel model that we apply to derive an exact polynomial time and space solution for the GHP on genomes with at most two genes per family before generalizing the problem to an integer linear program solution for natural genomes

Recombinations, chains and caps: resolving problems with the DCJ-indel model

Abstract One of the most fundamental problems in genome rearrangement studies is the (genomic) distance problem. It is typically formulated as finding the minimum number of rearrangements under a model that are needed to transform one genome into the other. A powerful multi-chromosomal model is the Double Cut and Join (DCJ) model.While the DCJ model is not able to deal with some situations that occur in practice, like duplicated or lost regions, it was extended over time to handle these cases. First, it was extended to the DCJ-indel model, solving the issue of lost markers. Later ILP-solutions for so called natural genomes, in which each genomic region may occur an arbitrary number of times, were developed, enabling in theory to solve the distance problem for any pair of genomes. However, some theoretical and practical issues remained unsolved. On the theoretical side of things, there exist two disparate views of the DCJ-indel model, motivated in the same way, but with different conceptualizations that could not be reconciled so far. On the practical side, while ILP solutions for natural genomes typically perform well on telomere to telomere resolved genomes, they have been shown in recent years to quickly loose performance on genomes with a large number of contigs or linear chromosomes. This has been linked to a particular technique, namely capping. Simply put, capping circularizes linear chromosomes by concatenating them during solving time, increasing the solution space of the ILP superexponentially. Recently, we introduced a new conceptualization of the DCJ-indel model within the context of another rearrangement problem. In this manuscript, we will apply this new conceptualization to the distance problem. In doing this, we uncover the relation between the disparate conceptualizations of the DCJ-indel model. We are also able to derive an ILP solution to the distance problem that does not rely on capping. This solution significantly improves upon the performance of previous solutions on genomes with high numbers of contigs while still solving the problem exactly and being competitive in performance otherwise. We demonstrate the performance advantage on simulated genomes as well as showing its practical usefulness in an analysis of 11 Drosophila genomes

Bridging Disparate Views on the DCJ-Indel Model for a Capping-Free Solution to the Natural Distance Problem

Bohnenkämper L. Bridging Disparate Views on the DCJ-Indel Model for a Capping-Free Solution to the Natural Distance Problem.One of the most fundamental problems in genome rearrangement is the (genomic) distance problem. It is typically formulated as finding the minimum number of rearrangements under a model that are needed to transform one genome into the other. A powerful multi-chromosomal model is the Double Cut and Join (DCJ) model. While the DCJ model is not able to deal with some situations that occur in practice, like duplicated or lost regions, it was extended over time to handle these cases. First, it was extended to the DCJ-indel model, solving the issue of lost markers. Later ILP-solutions for so called natural genomes, in which each genomic region may occur an arbitrary number of times, were developed, enabling in theory to solve the distance problem for any pair of genomes. However, some theoretical and practical issues remained unsolved. On the theoretical side of things, there exist two disparate views of the DCJ-indel model, motivated in the same way, but with different conceptualizations that could not be reconciled so far. On the practical side, while the solutions for natural genomes typically perform well on telomere to telomere resolved genomes, they have been shown in recent years to quickly loose performance on genomes with a large number of contigs or linear chromosomes. This has been linked to a particular technique increasing the solution space superexponentially named capping. Recently, we introduced a new conceptualization of the DCJ-indel model within the context of another rearrangement problem. In this manuscript, we will apply this new conceptualization to the distance problem. In doing this, we uncover the relation between the disparate conceptualizations of the DCJ-indel model. We are also able to derive an ILP solution to the distance problem that does not rely on capping and therefore significantly improves upon the performance of previous solutions for genomes with high numbers of contigs while still solving the problem exactly. To the best of our knowledge, our approach is the first allowing for an exact computation of the DCJ-indel distance for natural genomes with large numbers of linear chromosomes. We demonstrate the performance advantage as well as limitations in comparison to an existing solution on simulated genomes as well as showing its practical usefulness in an analysis of 11 Drosophila genomes

The Floor Is Lava - Halving Genomes with Viaducts, Piers and Pontoons

Bohnenkämper L. The Floor Is Lava - Halving Genomes with Viaducts, Piers and Pontoons. In: Jahn K, Vinař T, eds. Comparative Genomics. 20th International Conference, RECOMB-CG 2023, Istanbul, Turkey, April 14–15, 2023, Proceedings. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland; 2023: 51-67.The Double Cut and Join (DCJ) model is a simple and powerful model for the analysis of large structural rearrangements. After being extended to the DCJ-indel model, capable of handling gains and losses of genetic material, research has shifted in recent years toward enabling it to handle natural genomes, for which no assumption about the distribution of markers has to be made. Whole Genome Duplications (WGD) are events that double the content and structure of a genome. In some organisms, multiple WGD events have been observed while loss of genetic material is a typical occurrence following a WGD event. Natural genomes are therefore the ideal framework, under which to study this event. The traditional theoretical framework for studying WGD events is the Genome Halving Problem (GHP). While the GHP is solved for the DCJ model for genomes without losses, there are currently no exact algorithms utilizing the DCJ-indel model. In this work, we make the first step towards halving natural genomes and present a simple and general view on the DCJ-indel model that we apply to derive an exact polynomial time and space solution for the GHP on genomes with at most two genes per family. Supplementary material including a generalization to natural genomes can be found at https://doi.org/10.6084/m9.figshare.22269697

Computing the rearrangement distance of natural genomes

Bohnenkämper L, Dias Vieira Braga M, Dörr D, Stoye J. Computing the rearrangement distance of natural genomes. In: Proceedings of RECOMB 2020. LNBI. Vol 12074. 2020: 3-18

Applying rearrangement distances to enable plasmid epidemiology with pling

Frolova D, Lima L, Roberts L, et al. Applying rearrangement distances to enable plasmid epidemiology with pling. bioRxiv. 2024.Plasmids are a key vector of antibiotic resistance, but the current bioinformatics toolkit is not well suited to tracking them. The rapid structural changes seen in plasmid genomes present considerable challenges to evolutionary and epidemiological analysis. Typical approaches are either low resolution (replicon typing) or use shared k-mer content to define a genetic distance. However this distance can both overestimate plasmid relatedness by ignoring rearrangements, and underestimate by over-penalising gene gain/loss. Therefore a model is needed which captures the key components of how plasmid genomes evolve structurally - through gene/block gain or loss, and rearrangement. A secondary requirement is to prevent promiscuous transposable elements (TEs) leading to over-clustering of unrelated plasmids. We choose the "Double Cut and Join Indel" model, in which plasmids are studied at a coarse level, as a sequence of signed integers (representing genes or aligned blocks), and the distance between two plasmids is the minimum number of rearrangement events or indels needed to transform one into the other. We show how this gives much more meaningful distances between plasmids. We introduce a software workflow pling (https://github.com/iqbal-lab-org/pling), which uses the DCJ-Indel model, to calculate distances between plasmids and then cluster them. In our approach, we combine containment distances and DCJ-Indel distances to build a TE-aware plasmid network. We demonstrate superior performance and interpretability to other plasmid clustering tools on the "Russian Doll" dataset and a hospital transmission dataset