Models and algorithms for genome rearrangement with positional constraints
 Krister M. Swenson†^{1, 2}Email author,
 Pijus Simonaitis†^{3} and
 Mathieu Blanchette†^{4}
https://doi.org/10.1186/s1301501600659
© Swenson et al. 2016
Received: 1 February 2016
Accepted: 30 March 2016
Published: 17 May 2016
Abstract
Background
Traditionally, the merit of a rearrangement scenario between two gene orders has been measured based on a parsimony criteria alone; two scenarios with the same number of rearrangements are considered equally good. In this paper, we acknowledge that each rearrangement has a certain likelihood of occurring based on biological constraints, e.g. physical proximity of the DNA segments implicated or repetitive sequences.
Results
We propose optimization problems with the objective of maximizing overall likelihood, by weighting the rearrangements. We study a binary weight function suitable to the representation of sets of genome positions that are most likely to have swapped adjacencies. We give a polynomialtime algorithm for the problem of finding a minimum weight double cut and join scenario among all minimum length scenarios. In the process we solve an optimization problem on colored noncrossing partitions, which is a generalization of the Maximum Independent Set problem on circle graphs.
Conclusions
We introduce a model for weighting genome rearrangements and show that under simple yet reasonable conditions, a fundamental distance can be computed in polynomial time. This is achieved by solving a generalization of the Maximum Independent Set problem on circle graphs. Several variants of the problem are also mentioned.
Keywords
Background
A huge body of work exists on modeling the evolution of whole chromosomes [1]. The main difference between such models is the set of rearrangements that they allow. The moves of interest are usually inversion, transposition, translocation, chromosome fission and fusion, deletion, insertion, and duplication.
Almost all versions of the problem are NPHard if content modifying operations such at duplication, loss, and insertion are allowed [2, 3]. Fortunately, a model that considers genomes with equal content (i.e., no duplications or insertions/deletions) is quite pertinent, particularly in eukaryotes, since syntenic blocks of genes can be assigned between genomes so that each block occurs exactly once in each genome. For two genomes with equal content, double cut and join (DCJ) has been the model of choice since it elegantly includes inversion, translocation, chromosome circularization and linearization, as well as chromosome fission and fusion [4, 5].
One of the most important problems in comparative genomics is the inference of ancestral gene orders, i.e., paleogenetics. Given a realistic model of evolution, one can infer ancestral adjacencies of high confidence from presentday genomes [6–8]. However, methods that attempt to infer deeper structure for ancestral species suffer due to the huge number of parsimonious scenarios between genomes [9–11].
The apparent difficulty of the ancestral inference problem—because of the potentially astronomical number of parsimonious sorting scenarios—highlights the importance of methods that infer scenarios that conform to some extra biological constraints. Yet, aside from methods that weight inversions based on their length [12–16], to our knowledge no algorithmic work exists in this direction.
In this paper we use a weight function on rearrangements suitable for modeling positional constraints, i.e., sets of positions in the genome that are likely to swap adjacencies. Two examples of constraints that fit this paradigm are: (1) the physical 3D location of DNA segments in a nucleus and, (2) repetitive sequences that are the cause or consequence of rearrangement mechanisms. We illustrate the utility of our model with 3D constraints in the “Positional constraints as colored adjacencies” section.
We propose a general optimization problem that minimizes the sum of weights over the moves in a scenario. A more constrained version of the problem asks for such a scenario out of all possible unweighted parsimonious scenarios. Our algorithm solves this version of the problem in polynomial time given a binary weight function, despite an exponential growth of the number of parsimonious DCJ scenarios with respect to the distance [17, 18]. The commutation properties of DCJ moves as studied in [17] link certain DCJ scenarios to noncrossing partitions. Our algorithm relies on solving a new optimization problem on colored noncrossing partitions, called Minimum Noncrossing Colored Partition. It is a generalization of the Maximum Independent Set problem on circle graphs [19–21].
Genomes as sets of signed integers
DCJ and sorting DCJs
 1.
If a single adjacency is cut, then add new telomeres to the resulting ends (resulting in two new telomeric adjacencies).
 2.
If two adjacencies are cut, then glue the adjacencies back in one of two new ways.
The minimum weighted rearrangements problem
Consider a genome \(A_i\) made of a set of linear or circular chromosomes. Each rearrangement on this genome may have a certain likelihood of occurring. In the “Locality and the adjacency graph” section we will describe a DCJ move on \(G(A_i,B)\) as a reconnection of two adjacency edges of \(G(A_i,B)\); the resulting graph \(G(A_{i+1},B)\) is identical to \(G(A_i,B)\) aside from the connectivity of two adjacency edges. Therefore there is a bijection between edges of \(G(A_i,B)\) and edges of \(G(A_{i+1},B)\), so we can weight all pairs of genome adjacencies occurring in a sorting scenario by weighting all pairs of adjacency edges in G(A, B). For the set P of all pairs of adjacency edges in genome A, the weight function for a pair is \(w:P \mapsto \mathbb {R} _+\), where \(\mathbb {R} _+\) denotes the nonnegative real numbers. The higher the value of w the less likely the rearrangement is to occur, e.g., a value of 0 represents a most likely rearrangement.
A sequence of rearrangements \(\rho _1,\rho _2,\ldots ,\rho _d\) such that \((\cdots ((A\rho _1)\rho _2)\cdots \rho _d) = B\) is called a sorting scenario. The weight of a scenario is the sum of the weights of all the rearrangements in the scenario, i.e., \(\sum _{i=1}^d{w(\rho _i)}\). The Minimum Weighted Rearrangements problem is the following.
Problem 1

INPUT: Genomes A and B and a weight function w.

OUTPUT: A scenario of rearrangements turning A into B.

MEASURE: The weight of the scenario.
Positional constraints as colored adjacencies
We incorporate the constraint by grouping adjacencies of the genome into classes that are more likely to swap endpoints. This idea is illustrated in Fig. 3, where the physical (3D) structure of genome A is drawn and the adjacencies are grouped into colored localities. According to Véron et al. [27] and our recent results [28], rearrangements are more likely to occur between adjacencies at the same position.
Locality and the adjacency graph
Each adjacency edge in G corresponds to an adjacency in genome A or B. The color of an adjacency is given to the adjacency edge it corresponds to. Figure 1 shows a coloring for the adjacencies of genome A that matches the localities in Fig. 3. The application of a DCJ operation to a genome has the effect of swapping the endpoints of two adjacency edges, or splitting an adjacency edge as in the case of Fig. 4e.
Throughout a DCJ sorting scenario, adjacency edges always keep the same color. Thus, each DCJ operation corresponds to one of two possible updates of the same pair of adjacency edges, as depicted in Fig. 4a.
A positional weight function
Categorize rearrangements into two sets: those that are likely, and those that are not. Such a categorization of rearrangements is powerful enough to encapsulate the positional property discussed earlier.
Two restricted versions of the general problem are now described. The problem Minimum Local Scenario is exactly Minimum Weighted Rearrangements with the positional weight function w.
Problem 2

INPUT: Genomes A and B and positional weight function w.

OUTPUT: A scenario of rearrangements turning A into B.

MEASURE: The weight of the scenario.
The problem Minimum Local Parsimonious Scenario introduces the constraint that the scenario output is also a parsimonious scenario, i.e., a scenario of minimum length.
Problem 3

INPUT: Genomes A and B and positional weight function w.

OUTPUT: A parsimonious scenario of rearrangements turning A into B.

MEASURE: The weight of the scenario.
Minimum local parsimonious scenario
Since a solution to Minimum Local Parsimonious Scenario is limited to sorting moves, most connected components of G(A, B, col) must be sorted independently of each other, the exception being for evenlength paths; all but one DCJ in Fig. 2 act on a single connected component. We first give a method for computing the number of rare operations per connected component when no pair of evenlength paths exist, as in Fig. 2d. We then show in the “Evenlength paths” section how to solve the problem when such pairs exist.
Colored partitions
Recall that \(AA\)paths and \(BB\)paths are paths that start and end in the same genome. In this subsection, we assume that there does not exist both an \(AA\)path and a \(BB\)path in the graph (Fig. 2d). Ouangraoua and Bergeron established that the DCJs in a sorting scenario can be done in any order for such a graph and that every component will be sorted independently, thereby defining a noncrossing partition on each component (see sections 3 and 4 of [17]). Later in this section we show that Minimum Local Parsimonious Scenario on a single component is equivalent to the following problem concerning a generalization of noncrossing partitions. A partition of a set is a collection of pairwise disjoint subsets whose union is the entire set. The subsets are called classes. [1, n] is the set of integers from 1 to n.
Definition 1
A noncrossing partition is a partition \(\mathcal {P}\) of [1, n] such that for any classes \(S_i,S_j \in \mathcal {P}\) if we have \(p < q < p' < q'\) for \(p,p' \in S_i\) and \(q,q' \in S_j\) , then \(S_i = S_j\). A noncrossing colored partition is a noncrossing partition where for any \(p,p' \in S_i\), \(col(p) = col(p')\).
Another way to define a noncrossing partition is on a convex polygon. A noncrossing partition is a partition of the vertices of an ngon with the property that if you draw a line between all pairs of vertices in the same class, for all classes, then no two lines from different classes intersect. A colored partition has colored vertices, and respects the property that any pair of vertices in the same class of the partition have the same color (see Fig. 5A, B).
Problem 4

INPUT: Set size n, color set \(\Sigma\), and color function \(col:[1,n] \rightarrow \Sigma\).

OUTPUT: A noncrossing colored partition.

MEASURE: The cardinality of the partition.
We present a polynomialtime algorithm for the Minimum Noncrossing Colored Partition problem, which according to Lemma 2 (later in this section) gives a solution to Minimum Local Parsimonious Scenario on a single component. We describe the algorithm on an instance that has been embedded on a line where the leftmost vertex ① represents the smallest element of the set, as shown in Fig. 5C. For an interval [i, j], let NCP(i, j) be the number of classes in the MNCP on that subproblem. Thus, NCP(1, n) corresponds to the Minimum Noncrossing Colored Partition of [1, n].
We now show the link between MLPS and MNCP. Consider component C to be sorted. Pick an arbitrary vertex of C if it is a cycle, or either endpoint of C if it is a path, and consider an ordering of the vertices of genome A based on a traversal of the edges of C from that vertex. Embed the vertices of the component on a circle with respect to that ordering, and the edges so that they remain inside the circle. Call this a circular embedding of the component. Consider a sorting scenario for C that corresponds to a sequence of adjacency graphs \(C_0,C_1,\ldots ,C_d\) (\(C=C_0\)). Call \(C_i^{\circ }\) the graph \(C_i\) with vertices embedded according to the circular embedding of \(C_0\).
Lemma 1
[17] \(C_i^{\circ }\) has no pair of crossing adjacency edges for any i.
Proof
By construction, all adjacency edges in \(C_0^{\circ }\) connect adjacent vertices on the circle, so none of them cross. Assume that \(C_j^{\circ }\) has crossing adjacency edges and \(C_{j1}^{\circ }\) does not. This implies that the jth DCJ did not split a component. This is a contradiction since every sorting move on C splits a component, never creating both an \(AA\)path and \(BB\)path. \(\square\)
Lemma 2
Given a connected component C, Minimum Local Parsimonious Scenario on C can be solved by Minimum Noncrossing Colored Partition.
Proof
First, transform an instance of MLPS on a single component to an instance of MNCP. Given a cycle C representing genomes A and B, map the set of elements [1, n] from the set of adjacency edges of A ordered according to a circular embedding of C. The color function col maps each element to its corresponding adjacency edge’s color.
Now transform an optimal solution of MNCP into an optimal solution for MLPS. Clearly, any partition of [1, n] corresponds to a partition of adjacency edges of genome A. We show that there always exists a scenario of DCJs whose prefix separates C into connected components according to the partition. Any two edges of the same component can be chosen for a DCJ [17] and the DCJs on a cycle can be done in any order (Lemma 1). Since the ordering of the edges on the cycle corresponds to the ordering on [1, n], an edge partition of size k can be achieved with \(k1\) DCJs. Since k is minimum over all feasible partitions and the remaining DCJs of the scenario are likely, the constructed scenario has a minimum number of rare DCJs. \(\square\)
In fact, the two problems are equivalent. We omit the reduction in the other direction since it is out of the scope of this paper.
Evenlength paths
A Minimum Noncrossing Colored Partition can be computed in polynomial time for a single component independent of all others. Yet it is possible to mix components in a parsimonious DCJ scenario. As described in Fig. 2, the only parsimonious DCJs that mix components are those that act on one edge from an \(AA\)path and one edge from a \(BB\)path. Call AA (BB respectively) the set of \(AA\)paths (\(BB\)paths respectively) in the adjacency graph. The key observation is that once a path has been mixed with another, the result is always two oddlength paths which subsequently cannot be mixed with any other. Thus we devote this section to the computation of which pairs of paths \((p,q) \in AA \times BB\) will be mixed in an optimal solution, and which paths will remain unmixed.
Any pair (p, q) can be mixed in several ways. For all possible DCJs that mix them, we compute the MNCP on the resulting components. The minimum MNCP over all mixings is the cost in rare moves for mixing the two paths. To compute the pairs of paths to be mixed in an optimal solution, we use the inverse of these costs—the number of likely moves—as weights in a bipartite graph.
The function MNCPonComp(c, col) computes the Minimum Noncrossing Colored Partition on the given component c. In other words it builds the color function col according to the component c and then calls MNCP(1, n, col) where n is the number of adjacency edges on the A side of the component c. The function maxMix(p, q) computes the maximum number of likely DCJs over all possible DCJs that use one edge from p and one edge from q. The function d(AA) computes the sum of DCJ distances from each component in AA using Formula 1. The function \(maxMatching(V_A,V_B,w)\) builds the bipartite graph with vertices \(V_A\) on one side and vertices \(V_B\) on the other, and the edges described by the weight function w.
To summarize, any path can be mixed at most once in a parsimonious scenario. Potential mixings, as well as potential nonmixings, are encoded into a bipartite graph with edges weighted by the cost of a mix. A maximum weight matching in this graph corresponds to a scenario that minimizes the number of rare moves on the paths. All other connected components of the graph are sorted using the Minimum Noncrossing Colored Partition on the component.
The running time of our algorithm is dominated by the weighting of the edges on the bipartite graph. Consider all mixings done between elements of AA and elements of BB. A particular adjacency edge e from a given path \(p \in AA\) will take part in exactly one DCJ with every edge f from a path \(q \in BB\) throughout the weighting process. Therefore for each pair (e, f), e being an edge from a path in AA and f being an edge from a path in BB, we will compute the MNCP on the resulting mix. If the number of edges in the paths AA (respectively BB) is n(AA) (respectively n(BB)), then the running time of our algorithm is \(O(n(AA)n(BB)n^3)\). In the worst case, half of the edges are used in \(AA\)paths and half in \(BB\)paths, yielding a running time of \(O(n^5)\).
Faster mixing of evenlength paths
Define the Aedges of a component of the graph G(A, B) to be those edges connecting two nodes in genome A. Consider paths \(p \in AA\) and \(q \in BB\) where p is the path with Aedges \(e_1,e_2,\ldots ,e_k\) and telomeres t1 and t2, and q is the path with Aedges \(f_1,f_2,\ldots ,f_\ell\) and telomeres t3 and t4 (see Fig. 6). Construct two different cycles from p and q, cycle c1 results from joining t1 to t3 and t2 to t4 by cross edges, and cycle c2 results from joining t1 to t4 and t2 to t3. The Aedges of p can then be ordered circularly in c1 where edge \(e_1\) follows edge \(e_k\). Similarly, \(f_1\) follows \(f_\ell\) in c2. We show that there is a bijection between scenarios that start by mixing p and q, and scenarios that act on one of these two cycles by first performing a DCJ between an e edge and an f edge.
 1.
paths \(e_1,e_2,\ldots ,\,e_i,f_{j1},f_{j2},\ldots ,f_1\) and \(e_k,e_{k1},\ldots ,\,e_{i+1},f_j,f_{j+1},\ldots ,f_\ell\), or
 2.
paths \(e_1,e_2,\ldots ,\,e_{i1},f_j,f_{j1},\ldots ,f_1\) and \(e_k,e_{k1},\ldots ,\,e_i,f_{j+1},f_{j+2},\ldots ,f_\ell\), or
 3.
paths \(e_1,e_2,\ldots ,\,e_i,f_{j+1},f_{j+2},\ldots ,f_\ell\) and \(e_k,e_{k1},\ldots ,\,e_{i+1},f_j,f_{j1},\ldots ,f_1\), or
 4.
paths \(e_1,e_2,\ldots ,\,e_{i1},f_j,f_{j+1},\ldots ,f_\ell\) and \(e_k,e_{k1},\ldots ,\,e_i,f_{j1},f_{j2},\ldots ,f_1\).
The DCJ acting on \(e_i\) and \(f_j\) in c1 yields two cycles partitioning the edges as they are in either Case 1 or Case 2. The DCJ acting on \(e_i\) and \(f_j\) in c2 yields two cycles partitioning the edges as they are in either Case 3 or Case 4. Since odd length paths and cycles can only be sorted by cycleextraction moves (see Fig. 2), each scenario mixing \(e_i\) and \(f_j\) maps to a scenario on c1 or c2. The bijection follows from the fact that moves on a cycle can be ordered in any way (Lemma 1).
Due to the bijection between mixing scenarios on p and q, and scenarios on c1 or c2, the MNCP by mixing p and q must be either the MNCP on c1 or the MNCP on c2. Thus, our algorithm to compute maxMix(p, q) returns the maximum of MNCPonComp(c1, col) or MNCPonComp(c2, col) or \(MNCPonComp(p,col) + MNCPonComp(q,col)\).
Our new version of maxMix removes a linear factor from the overall computation time. Note \(a_{1}, \dots , a_{x}\) the sizes of the paths in AA and \(b_{1}, \dots , b_{y}\) the sizes of the paths in BB so that \(AA=\sum _{i=0}^{x}a_{i}\) and \(BB=\sum _{j=0}^{y}b_{j}\).
Conclusion
The number of parsimonious DCJ scenarios between two genomes is exponential in the distance between them. However, many of the scenarios are probably unrealistic in the biological sense. This paper takes a step towards modeling realistic scenarios by posing optimization problems that take into account positional constraints. An example of such a positional constraint is the 3D proximity of genome segments given by HiC experiments.
An \(O(n^4)\) algorithm is proposed for computing a parsimonious DCJ scenario that is most likely, given an edgecoloring function that classifies DCJ as “likely” or “unlikely”. In practice the algorithm will be \(O(n^3)\) since we expect long evenlength paths to be rare in nature. For example, the adjacency graph for the mouse/human syntenic map built by Véron et al. [27] from onetoone orthologs in Biomart has only 182 edges in evenlength paths out of a total of 13,302 edges. The largest connected component has 35 edges.
From a biological perspective, a solution to Minimum Local Parsimonious Scenario corresponds to finding a maximum likelihood scenario in a situation where likely and unlikely scenarios are both rare, and the difference between the likelihoods of likely and unlikely moves is not very large. In this situation, a most parsimonious scenario made of k unlikely moves is more likely than a nonparsimonious scenario made of \(k+1\) likely moves. Thus the maximum likelihood scenario is the most parsimonious scenario that involves the smallest number of unlikely moves.
We introduce the Minimum Noncrossing Colored Partition problem—a generalization of the Maximum Independent Set problem on circle graphs—for weighting the edges of a bipartite graph, on which we obtain a maximum matching. While this technique is essential to our algorithm for finding DCJ scenarios, we believe it will also come in handy for an algorithm that finds likely inversion scenarios (e.g., for handling the infamous “hurdles”). A multitude of biologically relevant variations on this problem exist, including variations on the model of genome rearrangement, a variant where edges have multiple colors, and a bidirectional sorting variant where edges are weighted on both genomes according to the chromatin conformation on each. Models that incorporate uncertainty or evolution in the HiC data would also be relevant. We hope that this work provokes further study from both the algorithmic and the biological perspectives.
Notes
Declarations
Authors' contributions
All authors contributed to this paper. All authors read and approved the final manuscript.
Acknowledgements
We would like to thank Anne Bergeron for her helpful comments during the preparation of this manuscript. This work was funded in part by a Grant from the Fonds de Recherche du Québec en Nature et Technologies.
A preliminary version of this paper appeared in the 15th Workshop on Algorithms in Bioinformatics (WABI 2015).
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
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