Parallel pairwise statistical significance estimation of local sequence alignment using Message Passing Interface library

Agrawal, Ankit; Misra, Sanchit; Honbo, Daniel; Choudhary, Alok

doi:10.1002/cpe.1798

Cited by 12 publications

(8 citation statements)

References 46 publications

(76 reference statements)

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“…Next, the proposed algorithm is compared with MPIPairwiseStatSig [23]. As stated in the related work section, MPIPairwiseStatSig is one of the recent parallel solutions for studying the problem under consideration using a cluster of 64 nodes.…”

Section: Resultsmentioning

confidence: 99%

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Cloud‐based parallel solution for estimating statistical significance of megabyte‐scale DNA sequences

Hosny

Shedeed

Hussein

et al. 2012

Concurrency and Computation

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Confidence in a pairwise local sequence alignment is a fundamental problem in bioinformatics. For huge DNA sequences, this problem is highly compute-intensive because it involves evaluating hundreds of local alignments to construct an empirical score distribution. Recent parallel solutions support only kilobyte-scale sequence sizes and/or are based on sophisticated infrastructures that are not available for most of the research labs. This paper presents an efficient parallel solution for evaluating the statistical significance for a pair of huge DNA sequences using cloud infrastructures. This solution can receive requests from various researchers via web-portal and allocate resources according to their demand. In this way, the benefits of cloud-based services can be achieved. The fundamental innovation of this research work is proposing an efficient solution that utilizes both shared and distributed memory architectures via cloud technology to enhance the performance of evaluating the statistical significance for pair of DNA sequences. Therefore, the restriction on the sequence sizes is released to be in megabyte-scale, which was not supported before for the statistical significance problem. The performance evaluation of the proposed solution was carried out on Microsoft's cloud and compared with the existing parallel solutions. The results show that the processing speed outperforms the recent cluster solutions that target the same problem. In addition, the performance metrics exhibit linear behavior for the addressed number of instances.

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Section: Resultsmentioning

confidence: 99%

“…The MPIPairwiseStatSig parallel solution was presented in [23] for pairwise statistical significance estimation. It is implemented using message passing interface for a computer cluster consisting of 64 nodes.…”

Section: Related Workmentioning

confidence: 99%

Cloud‐based parallel solution for estimating statistical significance of megabyte‐scale DNA sequences

Hosny

Shedeed

Hussein

et al. 2012

Concurrency and Computation

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“…Also, considering that the speed-up found is obtained on a single processor, it may be combined with hardware acceleration using multi-processors [7,13], which can possibly make it an extremely useful tool in bioinformatics, given the all-pervading application of sequence comparison in bioinformatics. Future work also includes the application of the proposed heuristics for estimating pairwise statistical significance using position-specific substitution matrices.…”

Section: Discussionmentioning

confidence: 99%

Derived distribution points heuristic for fast pairwise statistical significance estimation

Agrawal

Choudhary

Huang

2010

Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

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Estimation of statistical significance of a pairwise sequence alignment is crucial in homology detection. A recent development in the field is the use of pairwise statistical significance as an alternative to database statistical significance. Although pairwise statistical significance has been shown to be potentially better than database statistical significance in terms of homology detection retrieval accuracy, currently it is much time consuming since it involves generating an empirical score distribution by aligning one sequence of the sequence-pair with N random shuffles of the other sequence. A high value of N produces (statistically and potentially biologically) accurate estimates, but also consumes more time. A low value of N leads to inaccurate fitting of the score distribution, and hence poor estimates of statistical significance. In this paper, we propose a simple heuristic, called the Derived Distribution Points (DDP) heuristic, which is designed taking into account the features of the pairwise statistical significance estimation procedure, and has shown to significantly improve the quality of pairwise statistical significance estimates (evaluated in terms of retrieval accuracy) even when using low values of N . Alternatively, it can be thought of as speeding-up pairwise statistical significance estimation using high values of N , where comparable performance is achieved by actually using a much lower number of random shuffles. Experiments indicate that a speed-up of up to 40 as compared to current implementations can be achieved without loss in retrieval accuracy.

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“…Different high performance computing techniques have been HPC techniques have been successfully applied in many fields like aerospace [35], climate science [36], nuclear physics [37][38], power systems [39][40][41], linear programming solvers [42][43][44] and others. The PSS problem has also been accelerated on multiple hardware architectures such as OpenMP [45], MPI [48], GPGPU [46] and NUMA [47] machines. In this work, we present hardware acceleration on FPGAs that provide orders of magnitude speedup over software counterparts owing to customization of their architecture.…”

Section: High Performance Computingmentioning

confidence: 99%

FPGA architecture for pairwise statistical significance estimation

Honbo

Pande

Choudhary

2013

IJHPSA

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Sequence comparison is one of the most fundamental computational problems in bioinformatics. Pairwise sequence alignment methods align two sequences using a substitution matrix consisting of pairwise scores of aligning different residues with each other (like BLOSUM62), and give an alignment score for the given sequence-pair. This work 1 addresses the problem of accurately estimating statistical significance of pairwise alignment for the purpose of identifying related sequences, by making the sequence comparison process more sequencespecific. Specifically, we develop algorithms for sequence-specific strategies for hardware acceleration of pairwise sequence alignment in conjunction with statistical significance estimation. Using pairwise statistical significance has been shown to give better retrieval accuracy compared to database statistical significance reported by popular database search programs like BLAST and PSI-BLAST. We provide a 'flexible array' hardware architecture, which provides a scalable systolic array suitable for both long and short sequences. The results with Xtremedata XD1000 FPGA platform show a speed-up by up to a factor of more than 200.

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Parallel pairwise statistical significance estimation of local sequence alignment using Message Passing Interface library

Cited by 12 publications

References 46 publications

Cloud‐based parallel solution for estimating statistical significance of megabyte‐scale DNA sequences

Cloud‐based parallel solution for estimating statistical significance of megabyte‐scale DNA sequences

Derived distribution points heuristic for fast pairwise statistical significance estimation

FPGA architecture for pairwise statistical significance estimation

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