On the Inversion-Indel Distance

Willing E, Zaccaria S, Dias Vieira Braga M, Stoye J. On the Inversion-Indel Distance. BMC Bioinformatics. 2013;14(Suppl 15: Proc. of RECOMB-CG 2013): S3.Background The inversion distance, that is the distance between two unichromosomal genomes with the same content allowing only inversions of DNA segments, can be computed thanks to a pioneering approach of Hannenhalli and Pevzner in 1995. In 2000, El-Mabrouk extended the inversion model to allow the comparison of unichromosomal genomes with unequal contents, thus insertions and deletions of DNA segments besides inversions. However, an exact algorithm was presented only for the case in which we have insertions alone and no deletion (or vice versa), while a heuristic was provided for the symmetric case, that allows both insertions and deletions and is called the inversion-indel distance. In 2005, Yancopoulos, Attie and Friedberg started a new branch of research by introducing the generic double cut and join (DCJ) operation, that can represent several genome rearrangements (including inversions). Among others, the DCJ model gave rise to two important results. First, it has been shown that the inversion distance can be computed in a simpler way with the help of the DCJ operation. Second, the DCJ operation originated the DCJ-indel distance, that allows the comparison of genomes with unequal contents, considering DCJ, insertions and deletions, and can be computed in linear time. Results In the present work we put these two results together to solve an open problem, showing that, when the graph that represents the relation between the two compared genomes has no bad components, the inversion-indel distance is equal to the DCJ-indel distance. We also give a lower and an upper bound for the inversion-indel distance in the presence of bad components

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

Generalizations of the genomic rank distance to indels

MOTIVATION: The rank distance model represents genome rearrangements in multi-chromosomal genomes as matrix operations, which allows the reconstruction of parsimonious histories of evolution by rearrangements. We seek to generalize this model by allowing for genomes with different gene content, to accommodate a broader range of biological contexts. We approach this generalization by using a matrix representation of genomes. This leads to simple distance formulas and sorting algorithms for genomes with different gene contents, but without duplications. RESULTS: We generalize the rank distance to genomes with different gene content in two different ways. The first approach adds insertions, deletions and the substitution of a single extremity to the basic operations. We show how to efficiently compute this distance. To avoid genomes with incomplete markers, our alternative distance, the rank-indel distance, only uses insertions and deletions of entire chromosomes. We construct phylogenetic trees with our distances and the DCJ-Indel distance for simulated data and real prokaryotic genomes, and compare them against reference trees. For simulated data, our distances outperform the DCJ-Indel distance using the Quartet metric as baseline. This suggests that rank distances are more robust for comparing distantly related species. For real prokaryotic genomes, all rearrangement-based distances yield phylogenetic trees that are topologically distant from the reference (65% similarity with Quartet metric), but are able to cluster related species within their respective clades and distinguish the Shigella strains as the farthest relative of the Escherichia coli strains, a feature not seen in the reference tree. AVAILABILITY AND IMPLEMENTATION: Code and instructions are available at https://github.com/meidanis-lab/rank-indel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

On Distance and Sorting of the Double Cut-and-Join and the Inversion-*indel* Model

Willing E. On Distance and Sorting of the Double Cut-and-Join and the Inversion-*indel* Model. Bielefeld: Universität Bielefeld; 2018.In der vergleichenden Genomik werden zwei oder mehrere Genome hinsichtlich ihres Verwandtschaftsgrades verglichen. Das Ziel dieser Arbeit ist die Erforschung von mathematischen Modellen, die zum einen die evolutionäre *Distanz*, zum anderen die evolutionären Vorgänge zwischen zwei Genomen bestimmen können. Neben Methoden, welche auf einer niedrigen Ebene, z. B. den Basen(paarungen), ansetzen, sind auch abstraktere Modelle, die auf einzelnen Genen oder noch größeren Abschnitten Genome vergleichen, etabliert. Handelt es sich auf niedrigerer Ebene um einzelne Basen, die eingefügt, gelöscht oder ersetzt werden, sind es auf höherer Ebene beispielsweise ganze Gene. Auf höherer Ebene können Ergebnisse sogenannter Umordnungsprozesse (*genome rearrangements*) beobachtet werden, welche in einem *Sortierszenario* beschrieben werden. Im Vergleich eines Genoms mit einem anderen können dies unter anderem Inversionen, Translokationen, aber auch Einfügungen oder Löschungen von großen Bereichen sein. Ein bekanntes Modell ist das *Inversionsmodell*, welches den Verwandtschaftsgrad zweier Genome ausschließlich durch Inversionen bestimmt. Ein weiteres ist das *double cut-and-join (DCJ)* Modell, welches neben Inversionen auch Translokationen, Chromosomenfusionen, bzw. -fissionen, sowie Integration und Extraktion von kleinen zirkulären Trägern erlaubt. Die Distanz ist hierbei die Anzahl Zwischenschritte eines Sortierszenarios von geringster Länge. Diese Dissertation ist in zwei Teile gegliedert. Der erste Teil beschäftigt sich mit dem zufälligen Ziehen eines Sortierszenarios innerhalb des DCJ-Modells. Neben einigen naiven Ansätzen interessieren wir uns im Wesentlichen dafür, jedes Szenario mit gleicher Wahrscheinlichkeit, also uniform verteilt, zu ziehen. Hierfür wird nicht nur der gesamte Sortierraum betrachtet, sondern auch Maßnahmen zur effizienten Berechnung aufgezeigt. Der vorgestellte Algorithmus ist in einer Software-suite implementiert und wird hinsichtlich seiner Erzeugung von zufälligen Szenarien evaluiert. Der zweite Teil der Arbeit beschäftigt sich mit dem Inversions-*indel* Modell. Dieses wenig erforschte Modell erlaubt Inversionen, sowie Einfügungen und Löschungen (kurz *indels*). Dessen Distanz soll in Abhängigkeit von der DCJ- bzw. der DCJ-*indel*-Distanz wiedergegeben werden. Wir erweitern altbekannte Datenstrukturen des Inversionsmodells um Einfügungen und Löschungen repräsentieren zu können. Hierfür benutzen wir unter anderem Ansätze aus zwei anderen Modellen: Die Erweiterung des DCJ-Modells um indels, sowie die Ermittlung der Abhängigkeit von DCJ- und Inversionsmodell. Um die minimale Anzahl an Inversionen, Einfügungen und Löschungen zu ermitteln muss beachtet werden, dass durch Inversionen zwei oder mehr getrennte Bereiche, die zur Löschung vorgesehen sind, verschmolzen werden. Diese können sodann in einem einzigen Schritt gelöscht werden. Ähnlich verhält es sich mit Einfügungen. Zunächst betrachten wir Instanzen in denen die DCJ-indel-Distanz und die Inversions-indel-Distanz identisch sind. Im Weiteren gehen wir dazu über, schwierige Instanzen, d.h. jene die mehr Schritte benötigen als die DCJ(-indel)-Distanz, zu berechnen. Zu diesen Zweck müssen die unterschiedlichen Eigenschaften der Instanzen und deren Auswirkungen ausgemacht werden. Durch geschickte Reduzierung des Lösungsraums gelangen wir zu einer Menge von Basisfällen, welche wir durch erschöpfende Aufzählung lösen können. Insgesamt bieten die unternommenen Schritte nicht nur die Lösung der Inversions-indel Distanz in Abhängigkeit zur DCJ-indel Distanz, sondern auch eine Möglichkeit des Sortierens. Die Suche nach einer exakten Lösung für das Distanz- und das Sortierproblem im Inversions-indel Modell blieb lange unbeantwortet. Der Hauptbeitrag dieser Arbeit liegt darin diese zwei Fragen zu klären

Fast ancestral gene order reconstruction of genomes with unequal gene content

Feijão P, Soares de Araujo FE. Fast ancestral gene order reconstruction of genomes with unequal gene content. BMC Bioinformatics. 2016;17(S14): 413.Background During evolution, genomes are modified by large scale structural events, such as rearrangements, deletions or insertions of large blocks of DNA. Of particular interest, in order to better understand how this type of genomic evolution happens, is the reconstruction of ancestral genomes, given a phylogenetic tree with extant genomes at its leaves. One way of solving this problem is to assume a rearrangement model, such as Double Cut and Join (DCJ), and find a set of ancestral genomes that minimizes the number of events on the input tree. Since this problem is NP-hard for most rearrangement models, exact solutions are practical only for small instances, and heuristics have to be used for larger datasets. This type of approach can be called event-based. Another common approach is based on finding conserved structures between the input genomes, such as adjacencies between genes, possibly also assigning weights that indicate a measure of confidence or probability that this particular structure is present on each ancestral genome, and then finding a set of non conflicting adjacencies that optimize some given function, usually trying to maximize total weight and minimizing character changes in the tree. We call this type of methods homology-based. Results In previous work, we proposed an ancestral reconstruction method that combines homology- and event-based ideas, using the concept of intermediate genomes, that arise in DCJ rearrangement scenarios. This method showed better rate of correctly reconstructed adjacencies than other methods, while also being faster, since the use of intermediate genomes greatly reduces the search space. Here, we generalize the intermediate genome concept to genomes with unequal gene content, extending our method to account for gene insertions and deletions of any length. In many of the simulated datasets, our proposed method had better results than MLGO and MGRA, two state-of-the-art algorithms for ancestral reconstruction with unequal gene content, while running much faster, making it more scalable to larger datasets. Conclusion Studing ancestral reconstruction problems under a new light, using the concept of intermediate genomes, allows the design of very fast algorithms by greatly reducing the solution search space, while also giving very good results. The algorithms introduced in this paper were implemented in an open-source software called RINGO (ancestral Reconstruction with INtermediate GenOmes), available at https://github.com/pedrofeijao/RINGO