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Advances in Bioinformatics
Volume 2015 (2015), Article ID 303605, 14 pages
Research Article

FN-Identify: Novel Restriction Enzymes-Based Method for Bacterial Identification in Absence of Genome Sequencing

1Department of Biotechnology, Faculty of Agriculture, Al-Azhar University, Cairo 11651, Egypt
2Department of Information Technology, Faculty of Computer and Information Sciences, Mansoura University, Mansoura 35516, Egypt
3Department of Applied Biology, College of Sciences, University of Sharjah, P.O. Box 27272, Sharjah, UAE
4Donnelly Centre for Cellular and Biomedical Research, University of Toronto, Toronto, ON, Canada M5S 3E1

Received 31 July 2015; Revised 25 November 2015; Accepted 29 November 2015

Academic Editor: Paul Harrison

Copyright © 2015 Mohamed Awad et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Sequencing and restriction analysis of genes like 16S rRNA and HSP60 are intensively used for molecular identification in the microbial communities. With aid of the rapid progress in bioinformatics, genome sequencing became the method of choice for bacterial identification. However, the genome sequencing technology is still out of reach in the developing countries. In this paper, we propose FN-Identify, a sequencing-free method for bacterial identification. FN-Identify exploits the gene sequences data available in GenBank and other databases and the two algorithms that we developed, CreateScheme and GeneIdentify, to create a restriction enzyme-based identification scheme. FN-Identify was tested using three different and diverse bacterial populations (members of Lactobacillus, Pseudomonas, and Mycobacterium groups) in an in silico analysis using restriction enzymes and sequences of 16S rRNA gene. The analysis of the restriction maps of the members of three groups using the fragment numbers information only or along with fragments sizes successfully identified all of the members of the three groups using a minimum of four and maximum of eight restriction enzymes. Our results demonstrate the utility and accuracy of FN-Identify method and its two algorithms as an alternative method that uses the standard microbiology laboratories techniques when the genome sequencing is not available.