Better Approximation Algorithms for Scaffolding Problems View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016-05-27

AUTHORS

Zhi-Zhong Chen , Youta Harada , Eita Machida , Fei Guo , Lusheng Wang

ABSTRACT

Scaffolding is one of the main stages in genome assembly. During this stage, we want to merge contigs assembled from the paired-end reads into bigger chains called scaffolds. For this purpose, the following graph-theoretical problem has been proposed: Given an edge-weighted complete graph G and a perfect matching D of G, we wish to find a Hamiltonian path P in G such that all edges of D appear in P and the total weight of edges in P but not in D is maximized. This problem is NP-hard and the previously best polynomial-time approximation algorithm for it achieves a ratio of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\frac{1}{2}}$$\end{document}. In this paper, we design a new polynomial-time approximation algorithm achieving a ratio of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\frac{5-5{\epsilon }}{9-8{\epsilon }}}$$\end{document} for any constant \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0< {\epsilon } < 1$$\end{document}. Several generalizations of the problem have also been introduced in the literature and we present polynomial-time approximation algorithms for them that achieve better approximation ratios than the previous bests. In particular, one of the algorithms answers an open question. More... »

PAGES

17-28

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-39817-4_3

DOI

http://dx.doi.org/10.1007/978-3-319-39817-4_3

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https://app.dimensions.ai/details/publication/pub.1022958113


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