Evidence map›Paper›PMID 38679703›Full record

ArticleAlgorithms for molecular biology : AMB2024

Revisiting the complexity of and algorithms for the graph traversal edit distance and its variants.

Yutong Qiu, Yihang Shen, Carl Kingsford

Abstract read
In one paragraph

Article in Algorithms for molecular biology : AMB, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Yutong Qiu *Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, USA.
Yihang Shen *Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, USA.
Carl KingsfordRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, USA. carlk@cs.cmu.edu.

Funding

Improved genomic sketching for MUMmer and metagenomicsR01HG012470 · NHGRI · CARNEGIE-MELLON UNIVERSITY · PI KINGSFORD, CARLETON LEE, MARÇAIS, GUILLAUME · 2022 to 2025
$1.7M
NHGRI NIH HHS R01 HG012470NIH HHS R01HG012470
6 · The paper itself

Abstract

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/ .

Indexed as

Flow networkGenome graphsGraph comparisonInteger linear programming

Identifiers

PMID38679703
PMCPMC11056321

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.