High Performance computing improvements on bioinformatics consistency-based multiple sequence alignment tools

Orobitg, Miquel; Guirado, Fernando; Cores, Fernando; Lladós, Jordi; Notredame, Cédric

doi:10.1016/j.parco.2014.09.010

Cited by 16 publications

(10 citation statements)

References 25 publications

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“…Jurate Daugelaite, Aisling O"Driscoll, and Roy D. Sleator [19] have summarized various MSA algorithms in distributed and cloud environment. High performance computing techniques have been used for MSA tools in [20]. Authors have developed MTA-TCofee tool.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Resource Utilization on GPU

Pimple¹,

Sathe²

2019

ijacsa

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The problems arising due to massive data storage and data analysis can be handled by recent technologies, like cloud computing and parallel computing. MapReduce, MPI, CUDA, OpenMP, OpenCL are some of the widely available tools and techniques that use multithreading approach. However, it is a challenging task to use these technologies effectively to handle the compute intensive problems in the fields like life science, environment, fluid dynamics, image processing, etc. In this paper, we have used many core platforms with graphics processing units (GPU) to implement one of very important and fundamental problem of sequence alignment in the field of bioinformatics. Dynamic and concurrent kernel features offered by graphics card are used to speed up the performance. With these features, we achieved a speed up of around 120X and 55X. We have coupled well-known tiling technique with these features and observed a performance improvement up to 4X and 2X, as compared to non-tiling execution. The paper also analyses resource parameters, GPU occupancy and proposes their relationship with the design parameters for the chosen algorithm. These observations have been quantified and the relationship between the parameters is presented. The results of study can be extended further to study similar algorithms in this area. 285 | P a g e www.ijacsa.thesai.org  Algorithms that are based on dynamic programming methodology giving accurate results but taking exponential time to produce the output. For example, Needleman and Wunsch [NW] [1], Smith and Waterman [SW], [2] proposed the algorithm for global and sequence alignment, respectively.

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Section: Related Workmentioning

confidence: 99%

Analysis of Resource Utilization on GPU

Pimple¹,

Sathe²

2019

ijacsa

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“…However, multiprocessing and multithreading capabilities of the current personal computer would significantly reduce the overall processing time depending on its central processing unit and shared memory. Currently, HPC is used in several bioinformatics analyses (D'Angelo & Rampone 2014; Orobitg et al 2015;Zhang et al 2014). HPC can manage large datasets and handle intensive computation while significantly reduce the processing time.…”

Section: Sequence Alignment Without Grid Implementationmentioning

confidence: 99%

“…To overcome the bottleneck of data analysis, high performance computing (HPC) is now commonly used in large-scale bioinformatics tasks including sequence alignment (Orobitg et al 2015), simulation (Zhang et al 2014) Manuscript to be reviewed HPC is costly and requires extensive maintenance. Cloud computing services, such as Amazon EC2, is now an alternative to purchase a physical HPC for scientific computing (Juve et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #2 of "A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model (v0.1)"

Gesing¹

2016

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Development of high-throughput technologies, such as Next-generation sequencing, allows thousands of experiments to be performed simultaneously while reducing resource requirement. Consequently, a massive amount of experiment data is now rapidly generated. Nevertheless, the data are not readily usable or meaningful until they are further analysed and interpreted. Due to the size of the data, a high performance computer (HPC) is required for the analysis and interpretation. However, the HPC is expensive and difficult to access. Other means were developed to allow researchers to acquire the power of HPC without a need to purchase and maintain one such as cloud computing services and grid computing system. In this study, we implemented grid computing in a computer training center environment using Berkeley Open Infrastructure for Network Computing (BOINC) as a job distributor and data manager combining all desktop computers to virtualize the HPC. Fifty desktop computers were used for setting up a grid system during the off-hours. In order to test the performance of the grid system, we adapted the Basic Local Alignment Search Tools (BLAST) to the BOINC system. Sequencing results from Illumina platform were aligned to the human genome database by BLAST on the grid system. The result and processing time were compared to those from a single desktop computer and HPC. The estimated durations of BLAST analysis for 4 million sequence reads on a desktop PC, HPC and the grid system were 568, 24 and 5 days, respectively. Thus, the grid implementation of BLAST by BOINC is an efficient alternative to the HPC for sequence alignment. The grid implementation by BOINC also helped tap unused computing resources during the off-hours and could be easily modified for other available bioinformatics software.

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“…Each tree corresponds to a variation of the original obtained by NJ but adding some random noise into the distances in order to introduce some variability. The variation introduced in the guide tree is low enough to keep the distance criteria but significant enough to provide the necessary flexibility to generate multiple alternative trees [22]. Figure 5 shows two guide trees (b and c) produced by adding variation to distances in the NJ clustering algorithm used to obtain the initial guide tree a.…”

Section: Neighborhood Generationmentioning

confidence: 99%

Multiple Guide Trees in a Tabu Search Algorithm for the Multiple Sequence Alignment Problem

Mehenni

2015

IFIP Advances in Information and Communication Technology

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Abstract. Nowadays, Multiple Sequence Alignment (MSA) approaches do not always provide consistent solutions. In fact, alignments become increasingly difficult when treating low similarity sequences. Tabu Search is a very useful meta-heuristic approach in solving optimization problems. For the alignment of multiple sequences, which is a NP-hard problem, we apply a tabu search algorithm improved by several neighborhood generation techniques using guide trees. The algorithm is tested with the BAliBASE benchmarking database, and experiments showed encouraging results compared to the algorithms studied in this paper.

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High Performance computing improvements on bioinformatics consistency-based multiple sequence alignment tools

Cited by 16 publications

References 25 publications

Analysis of Resource Utilization on GPU

Analysis of Resource Utilization on GPU

Peer Review #2 of "A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model (v0.1)"

Multiple Guide Trees in a Tabu Search Algorithm for the Multiple Sequence Alignment Problem

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