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Title: An efficient algorithm for pairwise local alignment of protein interaction networks

Abstract

Recently, researchers seeking to understand, modify, and create beneficial traits in organisms have looked for evolutionarily conserved patterns of protein interactions. Their conservation likely means that the proteins of these conserved functional modules are important to the trait's expression. In this paper, we formulate the problem of identifying these conserved patterns as a graph optimization problem, and develop a fast heuristic algorithm for this problem. We compare the performance of our network alignment algorithm to that of the MaWISh algorithm [Koyuturk M, Kim Y, Topkara U, Subramaniam S, Szpankowski W, Grama A, Pairwise alignment of protein interaction networks, J Comput Biol 13(2): 182-199, 2006.], which bases its search algorithm on a related decision problem formulation. We find that our algorithm discovers conserved modules with a larger number of proteins in an order of magnitude less time. In conclusion, the protein sets found by our algorithm correspond to known conserved functional modules at comparable precision and recall rates as those produced by the MaWISh algorithm.

Authors:
 [1];  [2];  [3];  [2];  [4]
  1. Guangzhou Univ., Guangzhou (People's Republic of China); Fudan Univ., Shanghai (People's Republic of China); Nanjing Univ., Jiangsu (People's Republic of China)
  2. North Carolina State Univ., Raleigh, NC (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Univ. of Electronic and Technology of China, Sichuan (People's Republic of China)
  4. Guangzhou Univ., Guangzhou (People's Republic of China)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1331086
Grant/Contract Number:
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Bioinformatics and Computational Biology
Additional Journal Information:
Journal Volume: 13; Journal Issue: 02; Journal ID: ISSN 0219-7200
Publisher:
World Scientific
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; network alignment; conserved functional modules; graph optimization; graph theory

Citation Formats

Chen, Wenbin, Schmidt, Matthew, Tian, Wenhong, Samatova, Nagiza F., and Zhang, Shaohong. An efficient algorithm for pairwise local alignment of protein interaction networks. United States: N. p., 2015. Web. doi:10.1142/S0219720015500031.
Chen, Wenbin, Schmidt, Matthew, Tian, Wenhong, Samatova, Nagiza F., & Zhang, Shaohong. An efficient algorithm for pairwise local alignment of protein interaction networks. United States. doi:10.1142/S0219720015500031.
Chen, Wenbin, Schmidt, Matthew, Tian, Wenhong, Samatova, Nagiza F., and Zhang, Shaohong. Wed . "An efficient algorithm for pairwise local alignment of protein interaction networks". United States. doi:10.1142/S0219720015500031. https://www.osti.gov/servlets/purl/1331086.
@article{osti_1331086,
title = {An efficient algorithm for pairwise local alignment of protein interaction networks},
author = {Chen, Wenbin and Schmidt, Matthew and Tian, Wenhong and Samatova, Nagiza F. and Zhang, Shaohong},
abstractNote = {Recently, researchers seeking to understand, modify, and create beneficial traits in organisms have looked for evolutionarily conserved patterns of protein interactions. Their conservation likely means that the proteins of these conserved functional modules are important to the trait's expression. In this paper, we formulate the problem of identifying these conserved patterns as a graph optimization problem, and develop a fast heuristic algorithm for this problem. We compare the performance of our network alignment algorithm to that of the MaWISh algorithm [Koyuturk M, Kim Y, Topkara U, Subramaniam S, Szpankowski W, Grama A, Pairwise alignment of protein interaction networks, J Comput Biol 13(2): 182-199, 2006.], which bases its search algorithm on a related decision problem formulation. We find that our algorithm discovers conserved modules with a larger number of proteins in an order of magnitude less time. In conclusion, the protein sets found by our algorithm correspond to known conserved functional modules at comparable precision and recall rates as those produced by the MaWISh algorithm.},
doi = {10.1142/S0219720015500031},
journal = {Journal of Bioinformatics and Computational Biology},
number = 02,
volume = 13,
place = {United States},
year = {Wed Apr 01 00:00:00 EDT 2015},
month = {Wed Apr 01 00:00:00 EDT 2015}
}

Journal Article:
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