Hazem M. Bahig

A hybrid method for the exact planted (l, d) motif finding problem and its parallelization

BackgroundGiven a set of DNA sequences s1, ..., st, the (l, d) motif problem is to find an l-length motif sequence M , not necessary existing in any of the input sequences, such that for each sequence si, 1 ≤ i ≤ t, there is at least one subsequence differing with at most d mismatches from M. Many exact algorithms have been developed to solve the motif finding problem in the last three decades. However, the problem is still challenging and its solution is limited to small values of l and d.ResultsIn this paper we present a new efficient method to improve the performance of the exact algorithms for the motif finding problem. Our method is composed of two main steps: First, we process q ≤ t sequences to find candidate motifs. Second, the candidate motifs are searched in the remaining sequences. For both steps, we use the best available algorithms. Our method is a hybrid one, because it integrates currently existing algorithms to achieve the best running time. In this paper, we show how the optimal value of q is determined to achieve the best running time. Our experimental results show that there is about 24% speed-up achieved by our method compared to the best existing algorithm. Furthermore, we also present a parallel version of our method running on shared memory architecture. Our experiments show that the performance of our algorithm scales linearly with the number of processors. Using the parallel version, we were able to solve the (21, 8) challenging instance using 8 processors in 20.42 hours instead of 6.68 days of the serial version.ConclusionsOur method speeds up the solution of the exact motif problem. Our method is generic, because it can accommodate any new faster algorithm based on traditional methods. We expect that our method will help to discover longer motifs. The software we developed is available for free for academic research at http://www.nubios.nileu.edu.eg/tools/hymotif.

MonitTDPA: A tool for monitoring the tracing of dynamic programming algorithms

Algorithms are important procedures that are found in every aspect in our life. Large numbers of these algorithms cannot be accurately understood unless they are presented differently, especially if they are taught to students. A complete visualization system is created to teach dynamic programming algorithms. Through visualization, a student is able to trace the algorithm step‐by‐step, similar to the debugger, but in a simplified way. In addition, the system can handle errors during the execution of the algorithm. Moreover, the system guides and helps the students during the tracing and stores all of the information about the errors during the tracing. The students obtain the feedback of the tracing from a report generated by the system. The teacher benefits from the generated report through the enhancement of the teaching methods that focus on the misunderstood steps. Additionally, the system allows the user to see how a certain algorithm can be monitored by displaying the source code. The developed system is evaluated using different methods to prove the effectiveness of the system in teaching and learning.© 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:179–187, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae21781.

A fast exact sequential algorithm for the partial digest problem

BackgroundRestriction 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.ResultsIn 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.ConclusionOur 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.

Performance and analysis of modified voting algorithm for planted motif search

We consider the planted (l, d) motif search problem, which consists of finding a substring of length l that occurs in a set of input sequences {s1, s2, …, sn} with maximum Hamming distance, d, around the similar substring. In this paper, we present an experimental comparison between voting algorithm and its modification for planted motif on simulated data from (9, d) to (15, d) in case of challenging instances. The experimental results show that the modified voting algorithm is not better than voting algorithm as theoretically suggested. The results show that the running of voting algorithm is faster than modified voting algorithm in all the cases studied. We also determine, experimentally, the number of sequences that are required to make the modified voting algorithm faster than voting algorithm.

Parallel merging with restriction

Abstract In this paper, we study the merging of two sorted arrays

A fast optimal parallel algorithm for a short addition chain

A=(a_{1},a_{2},\ldots, a_{n_{1}})

Parallelizing exact motif finding algorithms on multi-core

and

Experimental study of modified voting algorithm for planted (l,d)-motif problem.

B=(b_{1},b_{2},\ldots,b_{n_{2}})

Merging on PRAM

on EREW PRAM with two restrictions: (1) The elements of two arrays are taken from the integer range [1,n], where n=Max(n1,n2). (2) The elements are taken from either uniform distribution or non-uniform distribution such that

Parallel Self-Index Integer Sorting

\#\{a\in A\,\mbox{and}\,b\in B\,\mbox{s.t.}\,a,b\in [(i-1)\frac{n}{p}+1,i\,\frac{n}{p}]\}=O(\frac{n}{p})