Effective haplotype assembly via maximum Boolean satisfiability

Mousavi, Seyed Rasoul; Mirabolghasemi, Maryam; Bargesteh, Nadia; Talebi, Majid

doi:10.1016/j.bbrc.2010.12.001

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…The Geraci's dataset [48] is one of the major benchmarks which is prepared based on Hapmap project. This dataset consists of 22 pairs of human chromosomes from four different populations and has widely been used by several researchers [14,15,17,[48][49][50]. There are three parameters related to the data set: haplotype length, error rate and coverage rate which are denoted by l, e, c, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Single Individual Haplotype Reconstruction Using Fuzzy C-Means Clustering With Minimum Error Correction

Olyaee

Khanteymoori

2020

Preprint

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Evolution of human genetics is one of the most interesting areas for researchers. Determination of Haplotypes not only makes valuable information for this purpose but also performs a major role in investigating the probable relation between diseases and genomes. Determining haplotypes by experimental methods is a time-consuming and expensive task. Recent progress in high throughput sequencing allows researchers to use computational methods for this purpose. Although, several algorithms have been proposed but they are less accurate when the error rate of input fragments increases. In this paper, first, a fuzzy conflict graph is constructed based on the similarities of all input fragments and next, the cluster centers are used as initial centers by fuzzy c-means (FCM) algorithm. The proposed method has been tested on several real datasets and compared with some current methods. The comparison with the existing approaches shows that our method can be a complementary role among the others.

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Section: Methodsmentioning

confidence: 99%

“…In order to solve haplotype reconstruction problem, various methods have been proposed. At present, there are two chief models: haplotype inference [8][9][10][11][12][13] and haplotype assembly [6,[14][15][16][17][18][19][20]. The presented method in this article is based on the haplotype assembly.…”

Section: Introductionmentioning

confidence: 99%

Single Individual Haplotype Reconstruction Using Fuzzy C-Means Clustering With Minimum Error Correction

Olyaee

Khanteymoori

2020

Preprint

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“…Improved perfomrmances do not impact on accuracy. Mousavi et al [ 30 ] reduces MEC to MAX-SAT, which is then be solved by a heuristic solver.…”

Section: Methodsmentioning

confidence: 99%

PWHATSHAP: efficient haplotyping for future generation sequencing

et al. 2016

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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.

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“…Deng et al [ 11 ] combined this dynamic programming method with a heuristic approach. Mousavi et al [ 12 ] suggested a HapSat model by converting the haplotype determination problem to a Max-2-SAT problem. HapAssembly converts the haplotype assembly problem to an integer linear programming problem for optimization [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient algorithms for polyploid haplotype phasing

Saha

Finkers

et al. 2018

BMC Genomics

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BackgroundInference of haplotypes, or the sequence of alleles along the same chromosomes, is a fundamental problem in genetics and is a key component for many analyses including admixture mapping, identifying regions of identity by descent and imputation. Haplotype phasing based on sequencing reads has attracted lots of attentions. Diploid haplotype phasing where the two haplotypes are complimentary have been studied extensively. In this work, we focused on Polyploid haplotype phasing where we aim to phase more than two haplotypes at the same time from sequencing data. The problem is much more complicated as the search space becomes much larger and the haplotypes do not need to be complimentary any more.ResultsWe proposed two algorithms, (1) Poly-Harsh, a Gibbs Sampling based algorithm which alternatively samples haplotypes and the read assignments to minimize the mismatches between the reads and the phased haplotypes, (2) An efficient algorithm to concatenate haplotype blocks into contiguous haplotypes.ConclusionsOur experiments showed that our method is able to improve the quality of the phased haplotypes over the state-of-the-art methods. To our knowledge, our algorithm for haplotype blocks concatenation is the first algorithm that leverages the shared information across multiple individuals to construct contiguous haplotypes. Our experiments showed that it is both efficient and effective.

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Effective haplotype assembly via maximum Boolean satisfiability

Cited by 13 publications

References 14 publications

Single Individual Haplotype Reconstruction Using Fuzzy C-Means Clustering With Minimum Error Correction

Single Individual Haplotype Reconstruction Using Fuzzy C-Means Clustering With Minimum Error Correction

PWHATSHAP: efficient haplotyping for future generation sequencing

Efficient algorithms for polyploid haplotype phasing

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