重新审视图遍历编辑距离及其变体的复杂性和算法

IF 1.5 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS Algorithms for Molecular Biology Pub Date : 2024-04-29 DOI:10.1186/s13015-024-00262-6
Yutong Qiu, Yihang Shen, Carl Kingsford
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引用次数: 0

摘要

Ebrahimpour Boroojeny 等人(2018 年)提出的图遍历编辑距离(GTED)是一种优雅的距离度量,定义为两个边缘标记图中由欧拉轨迹重建的字符串之间的最小编辑距离。GTED 可用于通过直接比较 de Bruijn 图来推断物种之间的进化关系,而无需计算成本高且容易出错的基因组组装过程。Ebrahimpour Boroojeny 等人(2018)为 GTED 提出了两个 ILP 公式,并声称 GTED 是多项式可解的,因为其中一个 ILP 的线性规划松弛总是能得到最优整数解。GTED 多项式可解的说法与现有字符串图匹配问题的复杂性结果相矛盾。我们通过证明 GTED 是 NP-完备的,并证明 Ebrahimpour Boroojeny 等人提出的 ILPs 并没有求解 GTED,而是求解了 GTED 的下限,且无法在多项式时间内求解,从而解决了复杂性结果中的这一矛盾。此外,我们还提供了 GTED 的前两个正确的 ILP 公式,并评估了它们的经验效率。这些结果为比较基因组图提供了坚实的算法基础,并指明了启发式算法的方向。重现实验结果的源代码可在 https://github.com/Kingsford-Group/gtednewilp/ 上获取。
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Revisiting the complexity of and algorithms for the graph traversal edit distance and its variants
The graph traversal edit distance (GTED), introduced by Ebrahimpour Boroojeny et al. (2018), is an elegant distance measure defined as the minimum edit distance between strings reconstructed from Eulerian trails in two edge-labeled graphs. GTED can be used to infer evolutionary relationships between species by comparing de Bruijn graphs directly without the computationally costly and error-prone process of genome assembly. Ebrahimpour Boroojeny et al. (2018) propose two ILP formulations for GTED and claim that GTED is polynomially solvable because the linear programming relaxation of one of the ILPs always yields optimal integer solutions. The claim that GTED is polynomially solvable is contradictory to the complexity results of existing string-to-graph matching problems. We resolve this conflict in complexity results by proving that GTED is NP-complete and showing that the ILPs proposed by Ebrahimpour Boroojeny et al. do not solve GTED but instead solve for a lower bound of GTED and are not solvable in polynomial time. In addition, we provide the first two, correct ILP formulations of GTED and evaluate their empirical efficiency. These results provide solid algorithmic foundations for comparing genome graphs and point to the direction of heuristics. The source code to reproduce experimental results is available at https://github.com/Kingsford-Group/gtednewilp/ .
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来源期刊
Algorithms for Molecular Biology
Algorithms for Molecular Biology 生物-生化研究方法
CiteScore
2.40
自引率
10.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Algorithms for Molecular Biology publishes articles on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning. Areas of interest include but are not limited to: algorithms for RNA and protein structure analysis, gene prediction and genome analysis, comparative sequence analysis and alignment, phylogeny, gene expression, machine learning, and combinatorial algorithms. Where appropriate, manuscripts should describe applications to real-world data. However, pure algorithm papers are also welcome if future applications to biological data are to be expected, or if they address complexity or approximation issues of novel computational problems in molecular biology. Articles about novel software tools will be considered for publication if they contain some algorithmically interesting aspects.
期刊最新文献
ESKEMAP: exact sketch-based read mapping Revisiting the complexity of and algorithms for the graph traversal edit distance and its variants NestedBD: Bayesian inference of phylogenetic trees from single-cell copy number profiles under a birth-death model Fast, parallel, and cache-friendly suffix array construction Pfp-fm: an accelerated FM-index
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