WinHAP2: an extremely fast haplotype phasing program for long genotype sequences

HaploTypo: a variant-calling pipeline for phased genomes

Summary

An increasing number of phased (i.e. with resolved haplotypes) reference genomes are available. However, the most genetic variant calling tools do not explicitly account for haplotype structure. Here, we present HaploTypo, a pipeline tailored to resolve haplotypes in genetic variation analyses. HaploTypo infers the haplotype correspondence for each heterozygous variant called on a phased reference genome.

Availability and implementation

HaploTypo is implemented in Python 2.7 and Python 3.5, and is freely available at https://github.com/gabaldonlab/haplotypo, and as a Docker image.

Supplementary information

Supplementary data are available at Bioinformatics online.

Unzipping haplotypes in diploid and polyploid genomes

Diploid genomes consist of two homologous copies of chromosomes with one from each parent while polyploid genomes contain more than two homologous sets of chromosomes. Most of the reference genome assemblies collapsed haplotypes that represent 'mosaic' sequences, ignoring allelic variants that may be involved in important cellular and biological functions. Unzipping haplotypes into distinct sets of sequences has been a growing trend in recent genome studies, as it is an essential tool towards resolving important clinical and biological questions, such as compound heterozygotes, heterosis, and evolution. Herein, we review existing methods for alignment-based and assembly-based haplotype phasing for heterozygous diploid and polyploid genomes, as well as recent advances of experimental approaches for improved genome phasing. We anticipate that full haplotype phasing could become a routine procedure in genome studies in the near future.

A New Fast Phasing Method Based On Haplotype Subtraction

We developed a novel phasing approach, based solely on molecules and genotype frequency, that does not rely on inference of new alleles. We initiated the project because of errors that were detected in the phased 1000 Genomes Project data. The algorithm first combined identical genotypes into clusters and ranked them by descending frequency. Using alleles defined in homozygotes, it combined them to produce expected genotypes that were dismissed and subtracted them from remaining genotypes to define additional new putative alleles. Putative alleles had to be confirmed by identifying them in independent genotypes, and the process was iterated until all alleles were identified. The new approach was validated using single-molecule sequencing of eight loci, 145 (8 to 35 per locus) alleles were identified, and an average 98.2% (range, 95.0% to 99.9%) of 1000 genome individuals at these loci were explained. The accuracy of the new method was compared with that from PHASE and SHAPEIT2 to the experimentally determined genotypes based on single-molecule sequencing. Our method was comparable to PHASE and SHAPEIT2 in accuracy but was, on average, 14.6- and 10.8-fold faster, respectively.

Mapping Phylogenetic Trees to Reveal Distinct Patterns of Evolution

Evolutionary relationships are frequently described by phylogenetic trees, but a central barrier in many fields is the difficulty of interpreting data containing conflicting phylogenetic signals. We present a metric-based method for comparing trees which extracts distinct alternative evolutionary relationships embedded in data. We demonstrate detection and resolution of phylogenetic uncertainty in a recent study of anole lizards, leading to alternate hypotheses about their evolutionary relationships. We use our approach to compare trees derived from different genes of Ebolavirus and find that the VP30 gene has a distinct phylogenetic signature composed of three alternatives that differ in the deep branching structure.

KEY WORDS

phylogenetics, evolution, tree metrics, genetics, sequencing.

A new mathematical modeling for pure parsimony haplotyping problem

Pure parsimony haplotyping (PPH) problem is important in bioinformatics because rational haplotyping inference plays important roles in analysis of genetic data, mapping complex genetic diseases such as Alzheimer's disease, heart disorders and etc. Haplotypes and genotypes are m-length sequences. Although several integer programing models have already been presented for PPH problem, its NP-hardness characteristic resulted in ineffectiveness of those models facing the real instances especially instances with many heterozygous sites. In this paper, we assign a corresponding number to each haplotype and genotype and based on those numbers, we set a mixed integer programing model. Using numbers, instead of sequences, would lead to less complexity of the new model in comparison with previous models in a way that there are neither constraints nor variables corresponding to heterozygous nucleotide sites in it. Experimental results approve the efficiency of the new model in producing better solution in comparison to two state-of-the art haplotyping approaches.