1996-2019. All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking:To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.


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.




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.