M. I. Khalil

Work place: Reactor Physics Dept., Nuclear Research Center, Atomic Energy Authority, Cairo, Egypt

E-mail: magdi_nrc@hotmail.com


Research Interests: Neural Networks, Image Compression, Image Manipulation, Image Processing


M.I. Khalil: Egyptian, male, has obtained his B.Sc degree in Computer and Automatic Control Engineering from Faculty of Engineering, Ain Shams University, Cairo, Egypt, in 1983, M.Sc degree in Computer Engineering from Faculty of Engineering, Tanta University,Tanta, Egypt, in 2003 and Ph.D degree in Computer Systems Engineering from Faculty of Engineering, Benha University, Cairo, Egypt, in 2005. He is currently working as Associate Professor in Department of Networking and Communication systems at the Faculty of Computer and Information Sciences, Princess Noura Bent Abdulrahman University, Riyadh, KSA. He has 15 years of previous experience at the Reactor Physics Department, Nuclear Research Center (NRC), Egyptian Atomic Energy Authority Cairo (EAEA), Egypt in the field of Data Acquisition and Interface Design. His main research interests focus on: Digital Signal Processing, Wireless Sensor Networks, Personal and Mobile Communications. So far, he has twelve years of teaching experience and has published more than twenty-five papers in repute journals and proceedings of conferences in fields of the data acquisition, digital signal processing, image processing and neural networks.   

Author Articles
Locating All Common Subsequences in Two DNA Sequences

By M. I. Khalil

DOI: https://doi.org/10.5815/ijitcs.2016.05.09, Pub. Date: 8 May 2016

Biological sequence comparison is one of the most important and basic problems in computational biology. Due to its high demands for computational power and memory, it is a very challenging task. The well-known algorithm proposed by Smith-Waterman obtains the best local alignments at the expense of very high computing power and huge memory requirements. This paper introduces a new efficient algorithm to locate the longest common subsequences (LCS) in two different DNA sequences. It is based on the convolution between the two DNA sequences: The major sequence is represented in the linked-list X while the minor one is represented in circular linked-list Y. An array of linked lists is established where each linked list is corresponding to an element of the linked-list X and a new node is added to it for each match between the two sequences. If two or more matches in different locations in string Y share the same location in string X, the corresponding nodes will construct a unique linked-list. Accordingly, by the end of processing, we obtain a group of linked-lists containing nodes that reflect all possible matches between the two sequences X and Y. The proposed algorithm has been implemented and tested using C# language. The benchmark test shows very good speedups and indicated that impressive improvements has been achieved.

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