Parallelizing and optimizing a bioinformatics pairwise sequence alignment algorithm for many-core architecture

Díaz, David; Esteban, Francisco J.; Hernández, Pilar; Caballero, J. A.; Dorado, Gabriel; Gálvez, Sergio

doi:10.1016/j.parco.2011.03.003

Cited by 24 publications

(29 citation statements)

References 35 publications

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“…They had to work in small groups of three to four people, on assigned tasks, and report on progress made. This improves student"s motivation and enhances problem solving and tool use skills in mutli-core and CUDA programming and sequencing technologies [14,15]. In addition, they develop abilities in both analytical and creative thinking.…”

Section: Fig 2: Traceback In Dynamic Programmingmentioning

confidence: 97%

A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program

Amrani¹

2014

IJCA

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The graphics processing unit (GPU) learning initiative is developed within a project awarded by the Moroccan Fulbright Alumni Association (MFAA), entitled "GPU Acceleration of Human Genome Sequencing". This project involves undergraduate students at Abdelmalek Essaadi University, and is conducted in collaboration with the High Performance Computing Lab (HPCL) at the George Washington University in U.S.The study brings together two of the most important topics and challenges for the medical field, Genomics, and information technology, parallel computing specially with Graphical Processing Units. The potential outcomes from the project will make very valuable contributions to medical and information technology research and will enrich the academic experience of the students.

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Section: Fig 2: Traceback In Dynamic Programmingmentioning

confidence: 97%

A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program

Amrani¹

2014

IJCA

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“…Most two approaches for aligning pairwise sequences are global and local. Global alignment is convenient if sequences compared as a whole, and compared sequences are homologous across their entire length [7]. Local alignments appropriate for detecting specific conserved regions, and obtain similarity between parts of sequences.…”

Section: The Sequences Alignment Problemmentioning

confidence: 99%

“…Each card includes multicore processor, RAM memory, and communication ports. The parallel version of the algorithms FstaLSA and MC64-NW/SW is implemented on Tile64 card in order to detect similarity regions in two compared sequences [7]. The algorithm is based on NW and SW algorithms to optimize the performance of pairwise sequence alignments.…”

Section: Network-on-chip (Noc)mentioning

confidence: 99%

Shared Memory and Hardware Utilizations for the Parallelization of Local Sequences Alignment using SW Algorithm: A Review

Eltayeeb¹,

Latiff²,

Isnin³

2015

IJCA

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It is becoming increasingly difficult to ignore the importance of aligning DNA and Protein sequences to infer properties of new sequences from well-known reference sequences established and sorted in genetics databanks. Many studies in recent years have focused on different implementations of Sequences Alignment Problems (SAP). However, researcher confused with the ambiguous classification of the SAP. This paper is set out mainly to review, investigate, and analysis current trends in shared memory and hardware implementation of local SAP using Smith-Waterman algorithm. The literatures are addressing and evaluating in order to highlight their advantages and disadvantages.

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“…(b)-dynamic algorithms such as (Needleman-Wunsch ,SmithWaterman and longest common subsequences ) which it's advantage is finding the optimal alignment solution between the sequences, and it's advantages is taking more time to make the alignment this decrease the performance [3], [8], [10] 2.1 Comparison between heuristic and dynamic algorithms:…”

Section: Related Workmentioning

confidence: 99%

“…The running time of FASTA is faster than dynamic programming because it doesn't evaluate the result statistically and uses partial information. The second type of programming, dynamic programming is the most sensitive result because the dynamic programming uses all information of two sequences, so the running time of the dynamic programming is slow because it computes the useless area for computing the optimal alignment [2], [8]. Comparison between the score of alignment (performance) for three dynamic algorithms:…”

Section: Related Workmentioning

confidence: 99%

Fast Longest Common Subsequences for Bioinformatics Dynamic Programming

Keshk¹,

Ossman²,

Hussein³

2012

IJCA

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Bioinformatics applications represent an increasingly important workload to improve the programs of sequence analysis. It can be used to assign function to genes and proteins by the study of the similarities between the compared sequences. This paper introduces a modified implementation of bioinformatics algorithm for sequence alignment .The implemented algorithm is called Fast Longest Common Subsequence (FLCS). It is filling the three main diagonals without filling the entire matrix by the unused data. It gets the optimal solution but the execution time is decreased and the performance is high. To illustrate the effectiveness of optimizing the performance of the proposed FLCS algorithm and demonstrate its superiority, it is compared with Needleman-Wunsch, Smith-Waterman and Longest Common Subsequence algorithms. General TermsBioinformatics.

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Parallelizing and optimizing a bioinformatics pairwise sequence alignment algorithm for many-core architecture

Cited by 24 publications

References 35 publications

A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program

A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program

Shared Memory and Hardware Utilizations for the Parallelization of Local Sequences Alignment using SW Algorithm: A Review

Fast Longest Common Subsequences for Bioinformatics Dynamic Programming

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