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Mahmoud Fahsi
Djillali Liabes University
Computer Science & IT

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Parallel random projection using R high performance computing for planted motif search Lala Septem Riza; Tyas Farrah Dhiba; Wawan Setiawan; Topik Hidayat; Mahmoud Fahsi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.11750

Abstract

Motif discovery in DNA sequences is one of the most important issues in bioinformatics. Thus, algorithms for dealing with the problem accurately and quickly have always been the goal of research in bioinformatics. Therefore, this study is intended to modify the random projection algorithm to be implemented on R high performance computing (i.e., the R package pbdMPI). Some steps are needed to achieve this objective, ie preprocessing data, splitting data according to number of batches, modifying and implementing random projection in the pbdMPI package, and then aggregating the results. To validate the proposed approach, some experiments have been conducted. Several benchmarking data were used in this study by sensitivity analysis on number of cores and batches. Experimental results show that computational cost can be reduced, which is that the computation cost of 6 cores is faster around 34 times compared with the standalone mode. Thus, the proposed approach can be used for motif discovery effectively and efficiently.
Genomic repeats detection using Boyer-Moore algorithm on Apache Spark Streaming Lala Septem Riza; Farhan Dhiyaa Pratama; Erna Piantari; Mahmoud Fahsi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14883

Abstract

Genomic repeats, i.e., pattern searching in the string processing process to find repeated base pairs in the order of Deoxyribonucleic Acid (DNA), requires a long processing time. This research builds a big-data computational model to look for patterns in strings by modifying and implementing the Boyer-Moore algorithm on Apache Spark Streaming for human DNA sequences from the Ensemble site. Moreover, we perform some experiments on cloud computing by varying different specifications of computer clusters with involving datasets of human DNA sequences. The results obtained show that the proposed computational model on Apache Spark Streaming is faster than standalone computing and parallel computing with multicore. Therefore, it can be stated that the main contribution in this research, which is to develop a computational model for reducing the computational costs, has been achieved.
Co-Authors Erna Piantari Farhan Dhiyaa Pratama Lala Septem Riza Topik Hidayat Tyas Farrah Dhiba Wawan Setiawan