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Multiple Sequence Alignment Algorithms in Bioinformatics

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Smart Trends in Computing and Communications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 286))

Abstract

Bioinformatics is a fast-evolving topic today. It has useful from establishing phylogenetic trees, protein structure prediction to discovery of drugs, and hence the importance of bioinformatics cannot be underestimated. Multiple sequence alignment (MSA) is the main step in performing the above tasks mentioned. Multiple sequence alignment is the science or a method where more than two sequences are arranged one above the other to find the regions of similarity between them. These regions of similarity are called ‘conserved-regions.’ Over time, there are many algorithms which are developed to give a ‘good’ alignment. These developments were essential to construct phylogenetic reconstruction, protein structure and protein prediction accurately. In this paper, we will talk about the most popular multiple sequence alignment algorithms. We first begin with the definition of multiple sequence alignment. Thereafter, we shall talk about the different techniques in multiple sequence alignment along with the most popular MSA algorithms.

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References

  1. Kemena C, Notredame C (2009) Upcoming challenges for multiple sequence alignment methods in the high-throughput era. Bioinformatics 25(19):2455–2465

    Article  Google Scholar 

  2. Edgar RC, Batzoglou S (2006) Multiple sequence alignment. Curr Opin Struct Biol 16(3):368–373

    Article  Google Scholar 

  3. Haque W, Aravind AA, Reddy B (2008) An efficient algorithm for local sequence alignment. In: 2008 30th annual international conference of the IEEE engineering in medicine and biology society, pp 1367–1372

    Google Scholar 

  4. Reddy B, Fields R (2020) Multiple anchor staged alignment algorithm—sensitive. In: Proceedings with the international conference on information and computer technologies (ICICT), San Jose, USA

    Google Scholar 

  5. Fengand D-F, Doolittle RF (1987) Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J Mol Evol 25(4):351–360

    Google Scholar 

  6. Wallace IM, Blackshields G, Higgins DG (2005) Multiple sequence alignments. Curr Opin Struct Biol 15(3):261–266

    Article  MathSciNet  Google Scholar 

  7. Sievers F, Wilm A, Dineenetal D (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7(539)

    Google Scholar 

  8. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4(4):406–425

    Google Scholar 

  9. Gronauand I, Moran S (2007) Optimal implementations of UPGMA and other common clustering algorithms. Inf Process Lett 104(6):205–210

    Google Scholar 

  10. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22(22):4673–4680

    Article  Google Scholar 

  11. Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30(4):772–780

    Article  Google Scholar 

  12. Lassmann T, Sonnhammer ELL (2005) Kalign—an accurate and fast multiple sequence alignment algorithm. BMC Bioinform 6(298)

    Google Scholar 

  13. Roshan U, Livesay DR (2006) Probalign: multiple sequence alignment using partition function posterior probabilities. Bioinformatics 22(22):2715–2721

    Article  Google Scholar 

  14. Edgar RC (2004) MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform 5(113)

    Google Scholar 

  15. Morgenstern B (2004) DIALIGN: multiple DNA and protein sequence alignment at BiBiServ. Nucleic Acids Res 32(suppl 2):W33–W36

    Article  Google Scholar 

  16. Löytynoja A, Goldman N (2008) Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis. Science 320(5883):1632–1635

    Google Scholar 

  17. Bradley RK, Roberts A, Smoot M et al (2009) Fast statistical alignment. PLoS Comput Biol 5(5):e1000392

    Google Scholar 

  18. Di Tommaso P, Moretti S, Xenarios I et al (2011) T-Coffee: a webserver for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension. Nucleic Acids Res 39(suppl 2):W13–W17

    Article  Google Scholar 

  19. Notredame C, Higgins DG, Heringa J (2000) T-coffee:a novel method for fast and accurate multiple sequence alignment. J Mol Biol 302(1):205–217

    Article  Google Scholar 

  20. Do CB, Mahabhashyam MSP, Brudno M, Batzoglou S (2005) ProbCons: probabilistic consistency-based multiple sequence alignment. Genome Res 15(2):330–340

    Article  Google Scholar 

  21. Notredame C, Higgins DG (1996) SAGA: sequence alignment by genetic algorithm. Nucleic Acids Res 24(8):1515–1524

    Article  Google Scholar 

  22. O’Sullivan O, Suhre K, Abergel C, Higgins DG, Notredame C (2004) 3D Coffee: combining protein sequences and structures within multiple sequence alignments. J Mol Biol 340(2):385–395

    Article  Google Scholar 

  23. Armougom F, Moretti S, Poirotetal O (2006) Expresso: automatic incorporation of structural information in multiple sequence alignments using 3D-Coffee. Nucleic Acids Res 34, suppl 2, pp W604–W608

    Google Scholar 

  24. Xia X, Zhang S, Su Y, Sun Z (2009) MIC align: a sequence to-structure alignment tool integrating multiple sources of information in conditional random fields. Bioinformatics 25(11):1433–1434

    Article  Google Scholar 

  25. Wilbur WJ, Lipman DJ (1983) Rapid similarity searches of nucleic acid and protein data banks. Proc Natl Acad Sci USA 80(3):726–730

    Article  Google Scholar 

  26. Söding J (2005) Protein homology detection by HMM-HMM comparison. Bioinformatics 21(7)951–960

    Google Scholar 

  27. Gronau I, Moran S (2007) Optimal implementations of UPGMA and other common clustering algorithms. Inf Process Lett 104(6):205–210

    Article  MathSciNet  Google Scholar 

  28. Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. In: Proceedings of the 18th annual ACM-SIAM symposium on discrete algorithms, society for industrial and applied mathematics

    Google Scholar 

  29. Chowdhury B, Garai G (2017) A review on multiple sequence alignment from the perspective of genetic algorithm. Genomics 109(5–6):419–431. https://doi.org/10.1016/j.ygeno.2017.06.007

    Article  Google Scholar 

  30. Naznin F, Sarker R, Essam D (2012) Progressive alignment method using genetic algorithm for multiple sequence alignment. IEEE Trans Evol Comput 16:615–631

    Article  Google Scholar 

  31. Naznin F, Sarker R, Essam D (2011) Vertical decomposition with genetic algorithm for multiple sequence alignment. BMC Bioinf 12:353

    Article  Google Scholar 

  32. Thompson JD, Plewniak F, Poch O (1999) BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs. Bioinformatics 15:87–88

    Article  Google Scholar 

  33. Mizuguchi K, Deane CM, Blundell TL, Overington JP (1998) HOMSTRAD: a database of protein structure alignments for homologous families. Protein Sci 7:2469–2471

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Process Automation R&D, Schneider-Electric, Lake Forest, USA

    Bharath Reddy & Richard Fields

Authors
  1. Bharath Reddy
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  2. Richard Fields
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Corresponding author

Correspondence to Bharath Reddy .

Editor information

Editors and Affiliations

  1. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  2. Department of Electrical and Electronics Engineering, University of the Ryukyus, Nishihara, Okinawa, Japan

    Tomonobu Senjyu

  3. Department of Computer Science, Khon Kaen University, Khon Kaen, Thailand

    Chakchai So-In

  4. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Reddy, B., Fields, R. (2022). Multiple Sequence Alignment Algorithms in Bioinformatics. In: Zhang, YD., Senjyu, T., So-In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. Lecture Notes in Networks and Systems, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-4016-2_9

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