Motivation: Single-individual haplotyping (SIH) is critical in genomic association studies and genetic diseases analysis. However, most genomic analysis studies do not perform haplotype-phasing analysis due to its complexity. Several computational methods have been developed to solve the SIH problem, but these approaches have not generated sufficiently reliable haplotypes. Results: Here, we propose a novel SIH algorithm, called PEATH (Probabilistic Evolutionary Algorithm with Toggling for Haplotyping), to achieve more accurate and reliable haplotyping. The proposed PEATH method was compared to the most recent algorithms in terms of the phased length, N50 length, switch error rate and minimum error correction. The PEATH algorithm consistently provides the best phase and N50 lengths, as long as possible, given datasets. In addition, verification of the simulation data demonstrated that the PEATH method outperforms other methods on high noisy data. Additionally, the experimental results of a real dataset confirmed that the PEATH method achieved comparable or better accuracy. Availability and implementation: Source code of PEATH is available at https://github.com/jcna99/PEATH. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

We introduce a new filtering method for approximate string matching called the suffix filter. It has some similarity with well-known filtration algorithms, which we call factor filters, and which are among the best practical algorithms for approximate string matching using a text index. Suffix filters are stronger, i.e., produce fewer false matches than factor filters. We demonstrate experimentally that suffix filters are faster in practice, too.

Summary form only given. The suffix tree is an efficient data structure used for Ziv-Lempel coding schemes. We propose a new data structure called the k-truncated suffix tree (k-TST), which is a truncated version of the suffix tree. While the suffix tree maintains all substrings of a given string, the k-TST stores the substrings of length at most k, where k is a constant. Hence the truncated suffix tree needs less space than the suffix tree.