Improving the quality of protein similarity network clustering algorithms using the network edge weight distribution.
BACKGROUND
Clustering protein sequence data into functionally specific families is a difficult but important problem in biological research. One useful approach for tackling this problem involves representing the sequence dataset as a protein similarity network, and afterwards clustering the network using advanced graph analysis techniques. Although a multitude of such network clustering algorithms have been developed over the past few years, comparing algorithms is often difficult because performance is affected by the specifics of network construction. We investigate an important aspect of network construction used in analyzing protein superfamilies and present a heuristic approach for improving the performance of several algorithms.


RESULTS
We analyzed how the performance of network clustering algorithms relates to thresholding the network prior to clustering. Our results, over four different datasets, show how for each input dataset there exists an optimal threshold range over which an algorithm generates its most accurate clustering output. Our results further show how the optimal threshold range correlates with the shape of the edge weight distribution for the input similarity network. We used this correlation to develop an automated threshold selection heuristic in order to most optimally filter a similarity network prior to clustering. This heuristic allows researchers to process their protein datasets with runtime efficient network clustering algorithms without sacrificing the clustering accuracy of the final results.


BACKGROUND
Python code for implementing the automated threshold selection heuristic, together with the datasets used in our analysis, are available at http://www.rbvi.ucsf.edu/Research/cytoscape/threshold_scripts.zip.
Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA.
Leonard
L
Apeltsin
John H
JH
Morris
Patricia C
PC
Babbitt
Thomas E
TE
Ferrin
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D016000:Cluster Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
D011506:Proteins