Advertisement

Evolution of Methods for NGS Short Read Alignment and Analysis of the NGS Sequences for Medical Applications

  • J. A. M. RexieEmail author
  • Kumudha Raimond
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 31)

Abstract

In medical and genomic research, the Next Generation Sequencing (NGS) has a major role. Presently, NGS data are produced at the rate of 10 TB a day and challenge the storage and data processing capacities. These huge datasets are being used by a wide sort of applications such as customized cancer healing and precision medicine. NGS technologies offer prospects for understanding unidentified species and complex syndrome. To utilize genomic data for such applications, the genomic data in the form of short reads produced by NGS initially has to be assembled into whole genome sequence. And then, the sequences have to be compared for similarity and variation discovery which will be useful for analyzing and arriving at health-related solutions. In this paper, the fundamental methods for short read alignment such as assembly-based and alignment-based methods are discussed. Followed by which, the different ways to compare the sequences to check the alignment for similarity/dissimilarity discovery are discussed. This comparative analysis report can be utilized for health-related medical decisions.

Keywords

NGS Short read alignment Multiple sequence alignment Pairwise sequence alignment 

References

  1. 1.
    Ayday E, De Cristofaro E, Hubaux J-P, Tsudik G (2015) Whole genome sequencing: revolutionary medicine or privacy nightmare? Comput Publ IEEE Comput Soc 48(2)CrossRefGoogle Scholar
  2. 2.
    Hengyun L, Giordano F, Ning Z (2016) Oxford nanopore MinION sequencing and genome assembly. Genom Proteom Bioinf 14:265–279CrossRefGoogle Scholar
  3. 3.
    Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bio Inf 25(14):1754–1760Google Scholar
  4. 4.
    Chelsea J-T, Ju ZZ, Wang W (2017) Efficient approach to correct read alignment for pseudogene abundance estimates. IEEE/ACM Trans Comput Biol Bioinf 14(3)Google Scholar
  5. 5.
    Xu H, Luo X, Qian J, Pang X, Song J, Qian G et al (2012) FastUniq: a fast De Novo duplicates removal tool for paired short reads. PLoS ONE 7(12)CrossRefGoogle Scholar
  6. 6.
    Roy A, Diao Y, Mauceli E, Shen Y, Wu BL (2012) Massive genomic data processing and deep analysis. Proc VLDB Endow 5(10)CrossRefGoogle Scholar
  7. 7.
    Houtgast EJ, Sima V-M, Bertels K, Al-Ars Z (2016) GPU-accelerated BWA-MEM genomic mapping algorithm using adaptive load balancing. In: Hannig F et al (ed), ARCS 2016, LNCS 9637, pp 130–142CrossRefGoogle Scholar
  8. 8.
    Chena C-C, Ghaffarib N, Qiana X, Yoona B-J (2017) Article optimal hybrid sequencing and assembly: feasibility conditions for accurate genome reconstruction and cost minimization strategy. Comput Biol Chem 69:153–163MathSciNetCrossRefGoogle Scholar
  9. 9.
    Bresler G, Bresler M, Tse D (2013) Optimal assembly for high throughput shotgun sequencing. Bioinformatics 14(Suppl 5):S18Google Scholar
  10. 10.
    Haubold B, Reed FA, Pfaffelhuber P (2011) Alignment-free estimation of nucleotide diversity. Bioinformatics 27(4):449–455CrossRefGoogle Scholar
  11. 11.
    Baichooa S, Ouzounisb CA (2017) Computational complexity of algorithms for sequence comparison, short-read assembly and genome alignment. BioSystems 72–85, 156–157Google Scholar
  12. 12.
    Haque W, Aravind A, Reddy B Pairwise sequence alignment algorithms—a survey. In: ISTA ‘09 Proceedings of the 2009 conference on Information Science, Technology and Applications, pp 96–103Google Scholar
  13. 13.
    Kieran Boyce A, Sievers F, Higgins DG (2014) Simple chained guide trees give high-quality protein multiple sequence alignments. Proc Nat Acad Sci United States Amer 111(29):10556–10561CrossRefGoogle Scholar
  14. 14.
    Zielezinski A, Vinga S, Almeida J, Karlowski WM (2017) Alignment-free sequence comparison: benefits, applications, and tools. Genome Biol 18:186Google Scholar
  15. 15.
    Li Y, Heavican TB, Vellichirammal NN, Iqbal J, Guda C (2017) ChimeRScope: a novel alignment-free algorithm for fusion transcript prediction using paired-end RNA-Seq data. Nucleic Acids Res 45(13)CrossRefGoogle Scholar
  16. 16.
    Leimeister C-A, Morgenstern B (2014) kmacs: the k-mismatch average common substring approach to alignment-free sequence comparison. Bioinformatics 30(14):2000–2008CrossRefGoogle Scholar
  17. 17.
    Haubold B, Pierstorff N, Möller F, Wiehe T (2005) Genome comparison without alignment using shortest unique substrings. BMC Bioinf 6:123Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Sciences TechnologyKarunya Institute of Technology and SciencesCoimbatoreIndia

Personalised recommendations