Abstract
Next-generation sequencing (NGS) technologies and different types of sequencing machines are introduced in an enormous volume of omics data. For analysis of NGS data, sequence alignment is always an essential step in finding relationships between sequences. Pairwise sequence alignment is a challenging task for reasonably large input sequences. Smith–Waterman (SW) is a popular centralized algorithm for sequence alignment. However, as data is spreading expeditiously, conventional centralized sequence alignment tools are inefficient in terms of computational time. In this paper, we propose a distributed pairwise sequence alignment technique using MapReduce implemented on Apache Spark framework, called MRaligner. We have compared the result of the proposed MRaligner with Jaligner, an open-source Java implementation of the Smith–Waterman algorithm for biological sequence alignment, and found significant improvement in terms of computational time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Buermans, H.P.J., Dunnen, J.T.: Next generation sequencing technology: advances and applications. Biochim. Biophys. Acta 1842, 1932–1941 (2014)
Benson, D.A.: GenBank. Nucleic Acids Res. 28, 15–18 (2000)
Ekre, A.R., Mante, R.V.: Genome sequence alignment tools: a review. In: AEEICB16. 978-1-4673-9745-2 IEEE (2016)
Li, H., Durbin, R.: Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26(5), 589–595, 2010 (2009)
Liu, Y., Schmidt, B. Long read alignment based on maximal exact match seeds. In: Bioinformatics. ECCB 2012, vol. 28, pp. i318–i324 (2012)
Smith, A.D., Xuan, Z., Zhang, M.Q.: Using quality scores and longer reads improves accuracy of Solexa read mapping. BMC Bioinform. (2008)
Li, H., et al.: Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. (2008)
Li, R., et al.: SOAP: short oligonucleotide alignment program. Bioinformatics (2008)
Schatz, M.C.: CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics 25(11), 1363–1369 (2009)
Nguyen, T.: CloudAligner: a fast and full-featured MapReduce based tool for sequence mapping. BMC Res. Notes 4, 171 (2011)
Li, W., Homer, N.: A survey of sequence alignment algorithms for next-generation sequencing. Brief. Bioinform. 11(5), 473–483 (2010)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981)
Gotoh, O.: An improved algorithm for matching biological sequences. J. Mol. Biol. 162, 705–708 (1982)
Moustafa, A.: JAligner: Open source Java implementation of Smith-Waterman. (2005)
Haque, W.: Pairwise sequence alignment algorithms: a survey. ISTA Information Science, Technology and Applications (2009)
Li, J.: Pairwise sequence alignment for very long sequences on GPUs. IEEE Int. Conf. Comput. Adv. Biol. Med. Sci. PMC (2013)
Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters Google, Inc. (2004)
National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mondal, S., Khatua, S. (2019). Accelerating Pairwise Sequence Alignment Algorithm by MapReduce Technique for Next-Generation Sequencing (NGS) Data Analysis. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_19
Download citation
DOI: https://doi.org/10.1007/978-981-13-1498-8_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1497-1
Online ISBN: 978-981-13-1498-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)