SEQOPTICS: a protein sequence clustering system.
BACKGROUND
Protein sequence clustering has been widely used as a part of the analysis of protein structure and function. In most cases single linkage or graph-based clustering algorithms have been applied. OPTICS (Ordering Points To Identify the Clustering Structure) is an attractive approach due to its emphasis on visualization of results and support for interactive work, e.g., in choosing parameters. However, OPTICS has not been used, as far as we know, for protein sequence clustering.


RESULTS
In this paper, a system of clustering proteins, SEQOPTICS (SEQuence clustering with OPTICS) is demonstrated. The system is implemented with Smith-Waterman as protein distance measurement and OPTICS at its core to perform protein sequence clustering. SEQOPTICS is tested with four data sets from different data sources. Visualization of the sequence clustering structure is demonstrated as well.


CONCLUSIONS
The system was evaluated by comparison with other existing methods. Analysis of the results demonstrates that SEQOPTICS performs better based on some evaluation criteria including Jaccard coefficient, Precision, and Recall. It is a promising protein sequence clustering method with future possible improvement on parallel computing and other protein distance measurements.
Chen
Yonghui
Y
Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, AL 35294-1170, USA. chenyh@cis.uab.edu
Reilly
Kevin D
KD
Sprague
Alan P
AP
Guan
Zhijie
Z
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D016000:Cluster Analysis; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D014584:User-Computer Interface
D011506:Proteins