WhatsHap: Haplotype Assembly for Future-Generation Sequencing Reads

  • Murray Patterson
  • Tobias Marschall
  • Nadia Pisanti
  • Leo van Iersel
  • Leen Stougie
  • Gunnar W. Klau
  • Alexander Schönhuth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8394)


The human genome is diploid, that is each of its chromosomes comes in two copies. This requires to phase the single nucleotide polymorphisms (SNPs), that is, to assign them to the two copies, beyond just detecting them. The resulting haplotypes, lists of SNPs belonging to each copy, are crucial for downstream analyses in population genetics. Currently, statistical approaches, which avoid making use of direct read information, constitute the state-of-the-art. Haplotype assembly, which addresses phasing directly from sequencing reads, suffers from the fact that sequencing reads of the current generation are too short to serve the purposes of genome-wide phasing.

Future sequencing technologies, however, bear the promise to generate reads of lengths and error rates that allow to bridge all SNP positions in the genome at sufficient amounts of SNPs per read. Existing haplotype assembly approaches, however, profit precisely, in terms of computational complexity, from the limited length of current-generation reads, because their runtime is usually exponential in the number of SNPs per sequencing read. This implies that such approaches will not be able to exploit the benefits of long enough, future-generation reads.

Here, we suggest WhatsHap, a novel dynamic programming approach to haplotype assembly. It is the first approach that yields provably optimal solutions to the weighted minimum error correction (wMEC) problem in runtime linear in the number of SNPs per sequencing read, making it suitable for future-generation reads. WhatsHap is a fixed parameter tractable (FPT) approach with coverage as the parameter. We demonstrate that WhatsHap can handle datasets of coverage up to 20x, processing chromosomes on standard workstations in only 1-2 hours. Our simulation study shows that the quality of haplotypes assembled by WhatsHap significantly improves with increasing read length, both in terms of genome coverage as well as in terms of switch errors. The switch error rates we achieve in our simulations are superior to those obtained by state-of-the-art statistical phasers.


Gray Code Dynamic Programming Approach Fixed Parameter Tractable International HapMap Consortium Dynamic Programming Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aguiar, D., Istrail, S.: Hapcompass: A fast cycle basis algorithm for accurate haplotype assembly of sequence data. J. of Comp. Biol. 19(6), 577–590 (2012)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Aguiar, D., Istrail, S.: Haplotype assembly in polyploid genomes and identical by descent shared tracts. Bioinformatics 360, i352–i360 (2013)Google Scholar
  3. 3.
    Bansal, V., Bafna, V.: HapCUT: an efficient and accurate algorithm for the haplotype assembly problem. Bioinformatics 24(16), i153–i159 (2008)Google Scholar
  4. 4.
    Bansal, V., et al.: An MCMC algorithm for haplotype assembly from whole-genome sequence data. Genome Research 18(8), 1336–1346 (2008)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Boomsma, D.I., et al.: The Genome of the Netherlands: design, and project goals. European Journal of Human Genetics (2013), doi:10.1038/ejhg.2013.118Google Scholar
  6. 6.
    Chen, Z.Z., Deng, F., Wang, L.: Exact algorithms for haplotype assembly from whole-genome sequence data. Bioinformatics 29(16), 1938–1945 (2013)CrossRefGoogle Scholar
  7. 7.
    Cilibrasi, R., van Iersel, L., Kelk, S., Tromp, J.: On the complexity of several haplotyping problems. In: Casadio, R., Myers, G. (eds.) WABI 2005. LNCS (LNBI), vol. 3692, pp. 128–139. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Delaneau, O., Howie, B., Cox, A., Zagury, J., Marchini, J.: Haplotype estimation using sequencing reads. Am. J. of Human Genetics 93(4), 687–696 (2013)CrossRefGoogle Scholar
  9. 9.
    Deng, F., Cui, W., Wang, L.: A highly accurate heuristic algorithm for the haplotype assembly problem. BMC Genomics 14(suppl. 2), S2 (2013)Google Scholar
  10. 10.
    Earl, D.A., et al.: Assemblathon 1: A competitive assessment of de novo short read assembly methods. Genome Research (2011), doi:10.1101/gr.126599.111Google Scholar
  11. 11.
    Fouilhoux, P., Mahjoub, A.: Solving VLSI design and DNA sequencing problems using bipartization of graphs. Comp. Optim. and Appl. 51(2), 749–781 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Greenberg, H., Hart, W., Lancia, G.: Opportunities for combinatorial optimization in computational biology. Informs J. on Computing 16(3), 211–231 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Hartl, D., Clark, A.: Principles of Population Genetics. Sinauer Associates, Inc., Sunderland (2007)Google Scholar
  14. 14.
    He, D., Eskin, E.: Hap-seqX: expedite algorithm for haplotype phasing with imputataion using sequence data. Gene. 518(1), 2–6 (2013)CrossRefGoogle Scholar
  15. 15.
    He, D., Han, B., Eskin, E.: Hap-seq: an optimal algorithm for haplotype phasing with imputation using sequencing data. J. Comp. Biol. 20(2), 80–92 (2013)CrossRefMathSciNetGoogle Scholar
  16. 16.
    He, D., et al.: Optimal algorithms for haplotype assembly from whole-genome sequence data. Bioinformatics 26(12), i183–i190 (2010)Google Scholar
  17. 17.
    Howie, B., Donnelly, P., Marchini, J.: A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genetics 5(6), e1000529 (2009)Google Scholar
  18. 18.
    Lancia, G., Bafna, V., Istrail, S., Lippert, R., Schwartz, R.: SNPs problems, complexity and algorithms. In: Meyer auf der Heide, F. (ed.) ESA 2001. LNCS, vol. 2161, pp. 182–193. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  19. 19.
    Levy, S., et al.: The diploid genome sequence of an individual human. PLoS Bio. (2007), doi:10.1371/journal.pbio.0050254Google Scholar
  20. 20.
    Li, H.: Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv (1303.3997) (2013)Google Scholar
  21. 21.
    Li, Y., et al.: MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010)CrossRefGoogle Scholar
  22. 22.
    Lippert, R., et al.: Algorithmic strategies for the single nucleotide polymorphism haplotype assembly problem. Briefings in Bioinformatics 3(1), 23–31 (2002)CrossRefGoogle Scholar
  23. 23.
    Menelaou, A., Marchini, J.: Genotype calling and phasing using next-generation sequencing reads and a haplotype scaffold. Bioinformatics 29(1), 84–91 (2013)CrossRefGoogle Scholar
  24. 24.
    Mossige, S.: An algorithm for Gray codes. Computing 18, 89–92 (1977)CrossRefzbMATHMathSciNetGoogle Scholar
  25. 25.
    Panconesi, A., Sozio, M.: Fast hare: A fast heuristic for single individual SNP haplotype reconstruction. In: Jonassen, I., Kim, J. (eds.) WABI 2004. LNCS (LNBI), vol. 3240, pp. 266–277. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  26. 26.
    Scheet, P., Stephens, M.: A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. American Journal of Human Genetics 78, 629–644 (2006)CrossRefGoogle Scholar
  27. 27.
    Selvaraj, S., et al.: Whole-genome haplotype reconstruction using proximity-ligation and shotgun sequencing. Nature Biotechnology 31, 1111–1118 (2013)CrossRefGoogle Scholar
  28. 28.
    The 1000 Genomes Project Consortium: A map of human genome variation from population-scale sequencing. Nature 467(7319), 1061–1073 (2010)Google Scholar
  29. 29.
    The International HapMap Consortium: A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007)Google Scholar
  30. 30.
    The International HapMap Consortium: Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010)Google Scholar
  31. 31.
    Wang, R.S., Wu, L.Y., Li, Z.P., Zhang, X.S.: Haplotype reconstruction from SNP fragments by minimum error correction. Bioinformatics 21(10), 2456–2462 (2005)CrossRefGoogle Scholar
  32. 32.
    Yang, W.Y., Hormozdiari, F., Wang, Z., He, D., Pasaniuc, B., Eskin, E.: Leveraging reads that span multiple single nucleotide polymorphisms for haplotype inference from sequencing data. Bioinformatics 29(18), 2245–2252 (2013)CrossRefzbMATHGoogle Scholar
  33. 33.
    Zhang, Y.: A dynamic Bayesian Markov model for phasing and characterizing haplotypes in next-generation sequencing. Bioinformatics 29(7), 878–885 (2013)CrossRefGoogle Scholar
  34. 34.
    Zhao, Y.T., Wu, L.Y., Zhang, J.H., Wang, R.S., Zhang, X.S.: Haplotype assembly from aligned weighted SNP fragments. Computational Biology and Chemistry 29, 281–287 (2005)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Murray Patterson
    • 1
    • 2
  • Tobias Marschall
    • 1
  • Nadia Pisanti
    • 3
    • 4
  • Leo van Iersel
    • 1
  • Leen Stougie
    • 1
    • 5
  • Gunnar W. Klau
    • 1
    • 5
  • Alexander Schönhuth
    • 1
  1. 1.Life SciencesCWIAmsterdamThe Netherlands
  2. 2.LBBE, CNRSUniversité de Lyon 1VilleurbanneFrance
  3. 3.Department of Computer ScienceUniversity of PisaItaly
  4. 4.LIACSLeiden UniversityThe Netherlands
  5. 5.VU University AmsterdamThe Netherlands

Personalised recommendations