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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: pWhatsHap: efficient haplotyping for future generation sequencing
Authors: Bracciali, Andrea
Aldinucci, Marco
Patterson, Murray
Marschall, Tobias
Pisanti, Nadia
Merelli, Ivan
Torquati, Massimo
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Keywords: Haplotyping
High-performance computing
Future generation sequencing
Issue Date: 2016
Publisher: BioMed Central
Citation: Bracciali A, Aldinucci M, Patterson M, Marschall T, Pisanti N, Merelli I & Torquati M pWhatsHap: efficient haplotyping for future generation sequencing, BMC Bioinformatics.
Abstract: Background: Haplotype 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 con dence 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.  Results: Given the potential relevance of ecient 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 le 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.  Conclusions: Due 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 eciency of the analysis of genomics information.
Type: Journal Article
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Computing Science - CSM Dept
University of Turin
Claude Bernard University Lyon 1
Saarland University
University of Pisa
Italian National Research Council (CNR)
University of Pisa

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