ArticleBMC bioinformatics2016
PWHATSHAP: efficient haplotyping for future generation sequencing.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A chaotic viewpoint-based approach to solve haplotype assembly using hypergraph model.PloS one · 2020Article
- GenHap: a novel computational method based on genetic algorithms for haplotype assembly.BMC bioinformatics · 2019Article
- HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads.BMC bioinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHaplotype phasing is an important problem in the analysis of genomics information. Given a set of DNA fragments of an individual, it consists of determining which one of the possible alleles (alternative forms of a gene) each fragment comes from. Haplotype information is relevant to gene regulation, epigenetics, genome-wide association studies, evolutionary and population studies, and the study of mutations. Haplotyping is currently addressed as an optimisation problem aiming at solutions that minimise, for instance, error correction costs, where costs are a measure of the confidence in the accuracy of the information acquired from DNA sequencing. Solutions have typically an exponential computational complexity. WHATSHAP is a recent optimal approach which moves computational complexity from DNA fragment length to fragment overlap, i.e., coverage, and is hence of particular interest when considering sequencing technology's current trends that are producing longer fragments.
resultsGiven the potential relevance of efficient haplotyping in several analysis pipelines, we have designed and engineered PWHATSHAP, a parallel, high-performance version of WHATSHAP. PWHATSHAP is embedded in a toolkit developed in Python and supports genomics datasets in standard file formats. Building on WHATSHAP, PWHATSHAP exhibits the same complexity exploring a number of possible solutions which is exponential in the coverage of the dataset. The parallel implementation on multi-core architectures allows for a relevant reduction of the execution time for haplotyping, while the provided results enjoy the same high accuracy as that provided by WHATSHAP, which increases with coverage.
conclusionsDue to its structure and management of the large datasets, the parallelisation of WHATSHAP posed demanding technical challenges, which have been addressed exploiting a high-level parallel programming framework. The result, PWHATSHAP, is a freely available toolkit that improves the efficiency of the analysis of genomics information.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.