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OJBTM

Online Journal of Bioinformatics

Volume 14 (1): 51-55, 2013


Amino acid composition model for prediction and identification of Alpha and Epsilon-proteobacteria.

 

Anuja Shanker, Kamal Raj Pardasani.

 

Department of Mathematics, Bioinformatics & Computer Α-plications. MANIT, Bhopal, India.

 

ABSTRACT

 

Shanker A, Pardasani KR., Amino acid composition model for prediction and identification of α and --proteobacteria, Online J Bioinform., 14 (1): 51-55, 2013. Alpha (α)-proteobacteria are thought to be the precursors of mitochondria. Epsilon (-)-proteobacteria are either symbionts or pathogens in animals. Therefore assigning correct taxonomic identifiers to these organisms is important. A model to predict, classify and distinguish proteobacteria subclasses from other microbial species is described. Simulations using Amino Acid Composition (AAC) in a support vector machine using LibSVM and SVM light programs were used to obtain an accuracy of 90%. The finding suggests that AAC could be used as a parameter for prediction and annotation of genomic and proteomic data.

 

Keywords: Signature proteins; Support vector machine; Amino Acid composition; Kernel functions.


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