A parallel algorithm for DNA sequences alignment based on MPI

Xue, Qianfei; Xie, Jiang; Shu, Junhui; Zhang, Huiran; Dai, Donghai; Wu, Xing Zheng; Wu, Zhang

doi:10.1109/infoseee.2014.6947774

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Pattern matching over DNA sequences can be considered an embarrassingly parallel application, because the average use case consists in matching millions of patterns against multiple text sequences, independently [27].…”

Section: Implementation Of Dna Sequence Matching With Mpimentioning

confidence: 99%

Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm

et al. 2019

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SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implementation of a parallel DNA sequence matching algorithm implemented by using the MPI programming paradigm ported to the SpiNNaker platform. In our implementation, all cores available in the board are configured for executing in parallel an optimised version of the Boyer-Moore (BM) algorithm. Exploiting this application, we benchmarked the SpiNNaker platform in terms of scalability and synchronisation latency. Experimental results indicate that the SpiNNaker parallel architecture allows a linear performance increase with the number of used cores and shows better scalability compared to a general-purpose multi-core computing platform.

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Section: Implementation Of Dna Sequence Matching With Mpimentioning

confidence: 99%

Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm

et al. 2019

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“…using Message Passing Interface (MPI). The results reported the matching in the given sequence and improved speedup using MPI [13]. The performance evaluation of DNA sequencing using OpenMP and CUDA is presented in [14].…”

Section: Examplementioning

confidence: 99%

A Work Distribution Strategy for Global Sequence Alignment

Kalare¹,

Tembhurne²

2019

IJC

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The sequence alignment comprises to identify similarities and dissimilarities between two given sequences. In this paper, we propose a work distribution strategy for the implementation of DNA global sequence alignment. The main objective of this work is to minimize the execution time required for DNA global alignment of large biological sequences. The proposed approach dealt the issues with the memory optimizations and minimization of execution time. We considered the biological sequences of different size to fit into the global memory of the system. The proposed strategy is implemented in shared memory architecture using OpenMP programming for large biological sequences. Parallelization using OpenMP directive is relatively easy and execute the code fast. We experimented on the Dell Precision Tower 7910 with Intel Xeon processor with 32GB RAM and 28 CPU cores. The efficient use of global memory and cache memory optimization dominate the results of execution time. The results demonstrate the significantly high speed up using OpenMP as compared with other implementations.

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“…These words include "MPI", "hadoop", "mapreduce", "cuda", and "membrane", "cloud computing". Interestingly, all these words pertain to big-data approaches that have recently come into play to analyze high throughput data from next generation sequencing approaches [19,21]. As sequencing data becomes more and more complex and voluminous, we can expect these words to become more central to this cluster over time.…”

Section: Keyword-based Analysismentioning

confidence: 99%

What’s Hot and What’s Not? - Exploring Trends in Bioinformatics Literature Using Topic Modeling and Keyword Analysis

Hahn¹,

Mohanty²,

Manda³

2017

Lecture Notes in Computer Science

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Abstract. Scientists exploring a new area of research are interested to know the "hot" topics in that area in order to make informed choices. With exponential growth in scientific literature, identifying such trends manually is not easy. Topic modeling has emerged as an effective approach to analyze large volumes of text. While this approach has been applied on literature in other scientific areas, there has been no formal analysis of bioinformatics literature.Here, we conduct keyword and topic model-based analysis on bioinformatics literature starting from 1998 to 2016. We identify top keywords and topics per year and explore temporal popularity trends of those keywords/areas. Network analysis was conducted to identify clusters of sub-areas/topics in bioinformatics. We found that "big-data", "next generation sequencing", and "cancer" all experienced exponential increase in popularity over the years. On the other hand, interest in drug discovery has plateaued after the early 2000s.

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A parallel algorithm for DNA sequences alignment based on MPI

Cited by 3 publications

References 13 publications

Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm

Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm

A Work Distribution Strategy for Global Sequence Alignment

What’s Hot and What’s Not? - Exploring Trends in Bioinformatics Literature Using Topic Modeling and Keyword Analysis

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