4.7 Article

Untangling Tanglegrams: Comparing Trees by Their Drawings

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2010.57

关键词

Optimization; combinatorial algorithms; graph algorithms; trees; analysis of algorithms; phylogeny; tree comparison; graph drawing; combinatorics

资金

  1. US National Science Foundation (NSF) [SEI-BIO 0513910, SEI-SBE 0513660, CCF-0515378, IIS-0803564]
  2. Direct For Computer & Info Scie & Enginr [GRANTS:13903144] Funding Source: National Science Foundation
  3. Division Of Mathematical Sciences
  4. Direct For Mathematical & Physical Scien [0920920] Funding Source: National Science Foundation
  5. Div Of Information & Intelligent Systems [GRANTS:13903144] Funding Source: National Science Foundation
  6. Div Of Information & Intelligent Systems
  7. Direct For Computer & Info Scie & Enginr [0803564] Funding Source: National Science Foundation

向作者/读者索取更多资源

A tanglegram is a pair of trees on the same set of leaves with matching leaves in the two trees joined by an edge. Tanglegrams are widely used in biology-to compare evolutionary histories of host and parasite species and to analyze genes of species in the same geographical area. We consider optimization problems in tanglegram drawings. We show a linear time algorithm to decide if a tanglegram admits a planar embedding by a reduction to the planar graph drawing problem. This problem was also studied by Fernau et al. [15]. A similar reduction to a graph crossing problem also helps to solve an open problem they posed, showing a fixed-parameter tractable algorithm for minimizing the number of crossings over all d-ary trees. For the case where one tree is fixed, we show an O(n log n) algorithm to determine the drawing of the second tree that minimizes the number of crossings. This improves the bound from earlier methods. We introduce a new optimization criterion using Spearman's footrule distance and give an O(n(2)) algorithm. We also show integer programming formulations to quickly obtain tanglegram drawings that minimize the two optimization measures discussed. We prove lower bounds on the maximum gap between the optimal solution and the heuristic of Dwyer and Schreiber [13] to minimize crossings.

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