Mostafa M Abbas1, Hazem M Bahig2. 1. Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar. mohamza@hbku.edu.qa. 2. Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Cairo, 11566, Egypt. hazem.m.bahig@gmail.com.
Abstract
BACKGROUND: Restriction site analysis involves determining the locations of restriction sites after the process of digestion by reconstructing their positions based on the lengths of the cut DNA. Using different reaction times with a single enzyme to cut DNA is a technique known as a partial digestion. Determining the exact locations of restriction sites following a partial digestion is challenging due to the computational time required even with the best known practical algorithm. RESULTS: In this paper, we introduce an efficient algorithm to find the exact solution for the partial digest problem. The algorithm is able to find all possible solutions for the input and works by traversing the solution tree with a breadth-first search in two stages and deleting all repeated subproblems. Two types of simulated data, random and Zhang, are used to measure the efficiency of the algorithm. We also apply the algorithm to real data for the Luciferase gene and the E. coli K12 genome. CONCLUSION: Our algorithm is a fast tool to find the exact solution for the partial digest problem. The percentage of improvement is more than 75% over the best known practical algorithm for the worst case. For large numbers of inputs, our algorithm is able to solve the problem in a suitable time, while the best known practical algorithm is unable.
BACKGROUND: Restriction site analysis involves determining the locations of restriction sites after the process of digestion by reconstructing their positions based on the lengths of the cut DNA. Using different reaction times with a single enzyme to cut DNA is a technique known as a partial digestion. Determining the exact locations of restriction sites following a partial digestion is challenging due to the computational time required even with the best known practical algorithm. RESULTS: In this paper, we introduce an efficient algorithm to find the exact solution for the partial digest problem. The algorithm is able to find all possible solutions for the input and works by traversing the solution tree with a breadth-first search in two stages and deleting all repeated subproblems. Two types of simulated data, random and Zhang, are used to measure the efficiency of the algorithm. We also apply the algorithm to real data for the Luciferase gene and the E. coli K12 genome. CONCLUSION: Our algorithm is a fast tool to find the exact solution for the partial digest problem. The percentage of improvement is more than 75% over the best known practical algorithm for the worst case. For large numbers of inputs, our algorithm is able to solve the problem in a suitable time, while the best known practical algorithm is unable.
Entities:
Keywords:
Bioinformatics algorithm; Breadth first search; DNA; Digestion process; Partial digest problem; Restriction site analysis
Authors: Jacek Blazewicz; Edmund Burke; Marta Kasprzak; Alexandr Kovalev; Mikhail Kovalyov Journal: IEEE/ACM Trans Comput Biol Bioinform Date: 2007 Oct-Dec Impact factor: 3.710