Nadia Pisanti,

Universitá di Pisa


The human genome is diploid, which requires to assign heterozygous single nucleotide polymorphisms (SNPs) to the two copies of the genome. The resulting haplotypes, lists of SNPs belonging to each copy, are crucial for downstream analyses in population genetics. Currently, statistical approaches, which are oblivious to 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.
While future-technology sequencing reads will contain sufficient amounts of SNPs per read for phasing, they are also likely to suffer from higher sequencing error rates.
I will describe WhatsHap, the first approach that yields provably optimal solutions to the weighted minimum error correction problem in runtime linear in the number of SNPs. WhatsHap is a fixed parameter tractable (FPT) approach with coverage as the parameter. We demonstrate that WhatsHap can handle datasets of coverage up to 15x, and that 15x are generally enough for reliably phasing long reads, even at significantly elevated sequencing error rates.
I will then show some theoretical results on the optimization problem that lead to HapCol, a fixed parameter algorithm and tool with the number of errors as the parameter. HapCol can handle coverage higher than WhatsHap while being more sensible to the error rate.



Date: 2016-Jun-29     Time: 14:00:00     Room: 408

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