Seeded Tree Alignment

Abstract

The optimal transformation of one tree into another by means of elementary edit operations is an important algorithmic problem that has several interesting applications to computational biology. Here we introduce a constrained form of this problem in which a partial mapping of a set of nodes (the "seeds") in one tree to a corresponding set of nodes in the other tree is given, and present efficient algorithms for both ordered and unordered trees. Whereas ordered tree matching based on seeded nodes has applications in pattern matching of RNA structures, unordered tree matching based on seeded nodes has applications in co-speciation and phylogeny reconciliation. The latter involves the solution of the planar tanglegram layout problem, for which a polynomial-time algorithm is given here.

Comparing trees via crossing minimization

“…Further relevant recent algorithmic work on tanglegram problems include [28] who provide an algorithm for the planar layout problem, called drawability problem later on in this paper, worse than ours in terms of running time (quadratic versus linear).…”

Forest alignment with affine gaps and anchors, applied in RNA structure comparison

“…This constrains the alignment algorithm, and only substructures between the anchor points have to be aligned in the usual way. In contrast to the approach of [12] mentioned earlier, our anchors do not imply that lowest common ancestors of anchors are matched -they are still candidates for deletion or insertion.…”

“…An interesting cross-breed of mapping and alignment is the method of seeded tree alignment [12], which, in spite of its name, computes mappings rather than alignments. These mappings can be constrained by seed mappings (a set of node pairs required to map onto each other) which preserve the lowest common ancestor relationship.…”

An efficient seeded tree alignment algorithm for finding the similarity score of two RNA secondary structures

“…al. [3] designed an algorithm to find the similarity between two large RNA secondary structures. Before comparison, their designed algorithm converts a RNA secondary structure into a tree graph named RNA tree using Le et.…”