Efficient Local Alignment Discovery amongst Noisy Long Reads

  • Gene Myers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8701)

Abstract

Long read sequencers portend the possibility of producing reference quality genomes not only because the reads are long, but also because sequencing errors and read sampling are almost perfectly random. However, the error rates are as high as 15%, necessitating an efficient algorithm for finding local alignments between reads at a 30% difference rate, a level that current algorithm designs cannot handle or handle inefficiently. In this paper we present a very efficient yet highly sensitive, threaded filter, based on a novel sort and merge paradigm, that proposes seed points between pairs of reads that are likely to have a significant local alignment passing through them. We also present a linear expected-time heuristic based on the classic O(nd) difference algorithm [1] that finds a local alignment passing through a seed point that is exceedingly sensitive, failing but once every billion base pairs. These two results have been combined into a software program we call DALIGN that realizes the fastest program to date for finding overlaps and local alignments in very noisy long read DNA sequencing data sets and is thus a prelude to de novo long read assembly.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Myers, E.W.: An O(ND) difference algorithm and its variations. Algorithmica 1, 251–266 (1986)CrossRefMathSciNetMATHGoogle Scholar
  2. 2.
    Eid, R., Fehr, A., … (51 authors) … Korlach, J, Turner, S.W.: Real-Time DNA Sequencing from Single Polymerase Molecules. Science 323(5910), 133–138Google Scholar
  3. 3.
    Lander, E.S., Waterman, M.S.: Genomic mapping by fingerprinting random clones: a mathematical analysis. Genomics 2(3), 231–239 (1988)CrossRefGoogle Scholar
  4. 4.
    Churchill, G.A., Waterman, W.S.: The accuracy of DNA sequences: estimating sequence quality. Genomics 14(1), 89–98 (1992)CrossRefGoogle Scholar
  5. 5.
    Chin, C.S., Alexander, D.H., Marks, P., Klammer, A.A., Drake, J., Heiner, C., Clum, A., Copeland, A., Huddleston, J., Eichler, E.E., Turner, S.W., Korlach, J.: Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nature Methods 10, 563–569 (2013)CrossRefGoogle Scholar
  6. 6.
    Pevzner, P.A., Tang, H., Waterman, M.S.: An Eulerian path approach to DNA fragment assembly. PNAS 98(17), 9748–9753 (2001)CrossRefMathSciNetMATHGoogle Scholar
  7. 7.
    Kececioglu, J., Myers, E.W.: Combinatorial algorithms for DNA sequence assembly. Algorithmica 13, 7–51 (1995)CrossRefMathSciNetMATHGoogle Scholar
  8. 8.
    Chaisson, M.J., Tesler, G.: Mapping single molecule sequencing reads using basic local alignment with successive refinement (BLASR): application and theory. BMC Bioinformatics 13, 238–245 (2012)CrossRefGoogle Scholar
  9. 9.
    Burrows, M., Wheeler, D.J.: A block sorting lossless data compression algorithm. Technical Report 124, Digital Equipment Corporation (1994)Google Scholar
  10. 10.
  11. 11.
    Manber, U., Myers, E.: Suffix Arrays: A New Method for On-Line String Searches. SIAM Journal on Computing 22, 935–948 (1993)CrossRefMathSciNetMATHGoogle Scholar
  12. 12.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms (3rd, 3rd edn., pp. 197–204. MIT Press (2009)Google Scholar
  13. 13.

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Gene Myers
    • 1
  1. 1.MPI for Molecular Cell Biology and GeneticsDresdenGermany

Personalised recommendations