Skip to main content
SpringerLink
Log in

An Introduction to the Computational Challenges in Next Generation Sequencing

  • Conference paper
  • First Online:
Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 822))

  • 552 Accesses

Abstract

During the last decade next generation sequencing has become one of the research areas that poses the most significant challenges both in terms of big data handling and algorithmic problems.

In this review we will discuss those challenges with a particular emphasis on those issues where scientific innovation will be essential to make progress.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Muir, P., Li, S., Lou, S., et al.: The real cost of sequencing: scaling computation to keep pace with data generation. Genome Biol. 17(1), 53 (2016)

    Article  Google Scholar 

  2. Szallasi, Z.: Development of genomic based diagnostics in various application domains. In: XIX International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2017), CEUR WS, vol. 2022, pp. 3–4, (2017). http://ceur-ws.org/Vol-2022/. Extended Abstract

  3. Boone, M., De Koker, A., Callewaert, N.: Capturing the “ome”: the expanding molecular toolbox for RNA and DNA library construction. Nucleic Acids Res. 107, 1 (2018)

    Google Scholar 

  4. Stephens, Z.D., Lee, S.Y., Faghri, F., et al.: Big data: astronomical or genomical? PLoS Biol. 13(7), e1002195 (2015)

    Article  Google Scholar 

  5. Nik-Zainal, S., Davies, H., Staaf, J., et al.: Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534(7605), 47–54 (2016)

    Article  Google Scholar 

  6. Reynolds, S.M., Miller, M., Lee, P., et al.: The ISB cancer genomics cloud: a flexible cloud-based platform for cancer genomics research. Cancer Res. 77(21), e7–e10 (2017)

    Article  Google Scholar 

  7. Rhoads, A., Au, K.F.: PacBio sequencing and its applications. Genomics Proteomics Bioinf. 13(5), 278–289 (2015)

    Article  Google Scholar 

  8. Liao, P., Satten, G.A., Hu, Y.-J.: PhredEM: a phred-score-informed genotype-calling approach for next-generation sequencing studies. Genet. Epidemiol. 41(5), 375–387 (2017)

    Article  Google Scholar 

  9. Pevzner, P.A., Tang, H., Waterman, M.S.: An Eulerian path approach to DNA fragment assembly. Proc. Natl. Acad. Sci. U.S.A. 98(17), 9748–9753 (2001)

    Article  MathSciNet  Google Scholar 

  10. Compeau, P.E.C., Pevzner, P.A., Tesler, G.: How to apply de Bruijn graphs to genome assembly. Nat. Biotechnol. 29(11), 987–991 (2011)

    Article  Google Scholar 

  11. Yang, J., Moeinzadeh, M.-H., Kuhl, H., et al.: Haplotype-resolved sweet potato genome traces back its hexaploidization history. Nat. Plants 3(9), 696–703 (2017)

    Article  Google Scholar 

  12. Olson, N.D., Treangen, T.J., Hill, C.M., et al.: Metagenomic assembly through the lens of validation: recent advances in assessing and improving the quality of genomes assembled from metagenomes. Brief. Bioinf. 8, e61692 (2017)

    Google Scholar 

  13. Buhler, S., Sanchez-Mazas, A.: HLA DNA sequence variation among human populations: molecular signatures of demographic and selective events. PLoS One 6(2), e14643 (2011)

    Article  Google Scholar 

  14. Szilveszter Juhos, K.R., Horváth, G.: On Genotyping Polymorphic HLA Genes — Ambiguities and quality measures using ngs. next generation sequencing - advances, applications and challenges. InTech (2016). https://doi.org/10.5772/61592

    Article  Google Scholar 

  15. Szolek, A., Schubert, B., Mohr, C., et al.: OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30(23), 3310–3316 (2014)

    Article  Google Scholar 

  16. Shukla, S.A., Rooney, M.S., Rajasagi, M., et al.: Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33(11), 1152–1158 (2015)

    Article  Google Scholar 

  17. Goodhead, I., Darby, A.C.: Taking the pseudo out of pseudogenes. Curr. Opin. Microbiol. 23, 102–109 (2015)

    Article  Google Scholar 

  18. Krøigård, A.B., Thomassen, M., Lænkholm, A.-V., et al.: Evaluation of nine somatic variant callers for detection of somatic mutations in exome and targeted deep sequencing data. PLoS One 11(3), e0151664 (2016)

    Article  Google Scholar 

  19. Alexandrov, L.B., Nik-Zainal, S., Wedge, D.C., et al.: Signatures of mutational processes in human cancer. Nature 500(7463), 415–421 (2013)

    Article  Google Scholar 

  20. Dill, K.A., MacCallum, J.L.: The protein-folding problem, 50 years on. Science 338(6110), 1042–1046 (2012)

    Article  Google Scholar 

  21. Berger, B., Leighton, T.: Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete. J. Comput. Biol. 5(1), 27–40 (1998)

    Article  Google Scholar 

  22. Eccles, D.M., Mitchell, G., Monteiro, A.N.A., et al.: BRCA1 and BRCA2 genetic testing-pitfalls and recommendations for managing variants of uncertain clinical significance. Ann. Oncol. 26(10), 2057–2065 (2015)

    Article  Google Scholar 

  23. Li, Q., Wang, K.: InterVar: Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines. Am. J. Hum. Genet. 100(2), 267–280 (2017)

    Article  Google Scholar 

  24. Jurtz, V., Paul, S., Andreatta, M., et al.: NetMHCpan-4.0: improved peptide-MHC Class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199(9), 3360–3368 (2017)

    Article  Google Scholar 

  25. Bjerregaard, A.-M., Nielsen, M., Jurtz, V., et al.: An analysis of natural T cell responses to predicted tumor neoepitopes. Front. Immunol. 8, 1566 (2017)

    Article  Google Scholar 

  26. Ott, P.A., Hu, Z., Keskin, D.B., et al.: An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547(7662), 217–221 (2017)

    Article  Google Scholar 

  27. Shah, S.P., Roth, A., Goya, R., et al.: The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486(7403), 395–399 (2012)

    Article  Google Scholar 

  28. Miklos, G.L.G.: The human cancer genome project—one more misstep in the war on cancer. Nat. Biotechnol. 23(5), 535–537 (2005)

    Article  Google Scholar 

  29. Chang, J.: Core services: Reward bioinformaticians. Nature 520(7546), 151–152 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational Health Informatics Program, Boston Children’s Hospital, Harvard Medical School, Boston, USA

    Zoltan Szallasi

Authors
  1. Zoltan Szallasi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zoltan Szallasi .

Editor information

Editors and Affiliations

  1. Federal Research Center “Computer Science and Control”, Russian Academy of Sciences, Moscow, Russia

    Leonid Kalinichenko

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. Institute of Astronomy, Russian Academy of Sciences, Moscow, Russia

    Oleg Malkov

  4. Federal Research Center “Computer Science and Control”, Russian Academy of Sciences, Moscow, Russia

    Nikolay Skvortsov

  5. Federal Research Center “Computer Science and Control”, Russian Academy of Sciences, Moscow, Russia

    Sergey Stupnikov

  6. Moscow State University, Moscow, Russia

    Vladimir Sukhomlin

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Szallasi, Z. (2018). An Introduction to the Computational Challenges in Next Generation Sequencing. In: Kalinichenko, L., Manolopoulos, Y., Malkov, O., Skvortsov, N., Stupnikov, S., Sukhomlin, V. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2017. Communications in Computer and Information Science, vol 822. Springer, Cham. https://doi.org/10.1007/978-3-319-96553-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-96553-6_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96552-9

  • Online ISBN: 978-3-319-96553-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation