WhatsHap: Haplotype Assembly for Future-Generation Sequencing Reads
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.
Unable to display preview. Download preview PDF.
- 2.Aguiar, D., Istrail, S.: Haplotype assembly in polyploid genomes and identical by descent shared tracts. Bioinformatics 360, i352–i360 (2013)Google Scholar
- 3.Bansal, V., Bafna, V.: HapCUT: an efficient and accurate algorithm for the haplotype assembly problem. Bioinformatics 24(16), i153–i159 (2008)Google Scholar
- 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
- 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.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
- 13.Hartl, D., Clark, A.: Principles of Population Genetics. Sinauer Associates, Inc., Sunderland (2007)Google Scholar
- 16.He, D., et al.: Optimal algorithms for haplotype assembly from whole-genome sequence data. Bioinformatics 26(12), i183–i190 (2010)Google Scholar
- 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
- 19.Levy, S., et al.: The diploid genome sequence of an individual human. PLoS Bio. (2007), doi:10.1371/journal.pbio.0050254Google Scholar
- 20.Li, H.: Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv (1303.3997) (2013)Google Scholar
- 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.The International HapMap Consortium: A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007)Google Scholar
- 30.The International HapMap Consortium: Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010)Google Scholar