Probabilistic single-individual haplotyping

Kuleshov, Volodymyr

doi:10.1093/bioinformatics/btu484

Cited by 60 publications

(77 citation statements)

References 19 publications

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“…We compared HAPCOL with three state-of-the-art haplotyping tools specifically designed for handling long reads, namely, REFHAP, which was shown to be one of the most accurate heuristic methods (Duitama et al, 2012), PROBHAP, a recent probabilistic method which has been shown to be sensibly more accurate than REFHAP (Kuleshov, 2014) and WHATSHAP, the first exact approach for the weighted MEC problem specifically designed for long reads (Patterson et al, 2014(Patterson et al, , 2015. At higher coverages, applications such as SNP calling or validating which SNPs are really heterozygous in the given sample (e.g.…”

Section: Resultsmentioning

confidence: 99%

“…Two recent articles (Kuleshov, 2014;Patterson et al, 2014) aim at processing future-generation long reads by introducing algorithms exponential in the sequencing coverage, a parameter which is not expected to grow as fast as read length with the advent of future-generation technologies. The first algorithm, called PROBHAP (Kuleshov, 2014), is a probabilistic dynamic programming algorithm that optimizes a likelihood function generalizing the objective function of MEC. Albeit PROBHAP is significantly slower than the previous heuristics, it obtained a noticeable improvement in accuracy.…”

Section: Introductionmentioning

confidence: 99%

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HapCol: accurate and memory-efficient haplotype assembly from long reads

et al. 2015

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Motivation: Haplotype assembly is the computational problem of reconstructing haplotypes in diploid organisms and is of fundamental importance for characterizing the effects of single-nucleotide polymorphisms on the expression of phenotypic traits. Haplotype assembly highly benefits from the advent of 'future-generation' sequencing technologies and their capability to produce long reads at increasing coverage. Existing methods are not able to deal with such data in a fully satisfactory way, either because accuracy or performances degrade as read length and sequencing coverage increase or because they are based on restrictive assumptions. Results: By exploiting a feature of future-generation technologies-the uniform distribution of sequencing errors-we designed an exact algorithm, called HAPCOL, that is exponential in the maximum number of corrections for each single-nucleotide polymorphism position and that minimizes the overall error-correction score. We performed an experimental analysis, comparing HAPCOL with the current state-of-the-art combinatorial methods both on real and simulated data. On a standard benchmark of real data, we show that HAPCOL is competitive with state-of-the-art methods, improving the accuracy and the number of phased positions. Furthermore, experiments on realistically simulated datasets revealed that HAPCOL requires significantly less computing resources, especially memory. Thanks to its computational efficiency, HAPCOL can overcome the limits of previous approaches, allowing to phase datasets with higher coverage and without the traditional all-heterozygous assumption.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HapCol: accurate and memory-efficient haplotype assembly from long reads

et al. 2015

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“…• they assume that the solution is a pair of haplotypes from diploid parents, and discard/alter observations until a pair of haplotypes can be determined [17,34] • they discard SNP sites that feature three or more alleles as errors [34] • they can generate a unrealistically large number of unordered potential haplotypes [4,35] • they are too computationally expensive for highdepth short read data sets [36] • they require a good quality reference genome [37] • they are no longer maintained/are specific to certain data/cannot be installed [38] It is important to note that no other tool that claims to recover haplotypes or strains from a microbial population has attempted to validate their work biologically.…”

Section: Discussion Comparison To Related Workmentioning

confidence: 99%

Recovery of gene haplotypes from a metagenome

Nicholls

Aubrey

Edwards

et al. 2017

Preprint

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Population-level diversity of natural microbiomes represent a biotechnological resource for biomining, biorefining and synthetic biology but requires the recovery of the exact DNA sequence (or "haplotype") of the genes and genomes of every individual present. Computational haplotype reconstruction is extremely difficult, complicated by environmental sequencing data (metagenomics). Current approaches cannot choose between alternative haplotype reconstructions and fail to provide biological evidence of correct predictions. To overcome this, we present Hansel and Gretel: a novel probabilistic framework that reconstructs the most likely haplotypes from complex microbiomes, is robust to sequencing error and uses all available evidence from aligned reads, without altering or discarding observed variation. We provide the first formalisation of this problem and propose "metahaplome" as a definition for the set of haplotypes for any genomic region of interest within a metagenomic dataset. Finally, we demonstrate using long-read sequencing, biological evidence of novel haplotypes of industrially important enzymes computationally predicted from a natural microbiome.Keywords: 'metagenome', 'haplotypes', 'long read sequencing', 'algorithm' Running Title: Haplotype recovery from natural microbiomes Contact: msn@aber.ac.uk (+441970 622 424) 1 . CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/223404 doi: bioRxiv preprint first posted online Nov. 22, 2017; It has become clear that population-level genetic variation drives competitiveness and niche specialisation in microbial communities [1]. Novel combinations of variants in individuals (haplotypes) are filtered by natural selection so that those that confer an advantage are retained [2]. Recovering the haplotypes of enzyme isoforms for a given gene across all organisms in a microbiome (the "metahaplome") would offer great biotechnological potential [3,4] and allow unprecedented insights into microbial ecosystems [5].Similar goals in humans are being achieved by the International HapMap Project which aims to describe the common patterns of human genetic variation that affect health, disease, responses to drugs and environmental factors [6]. However, microbial research has so far focused on higher-level characterisations of diversity, for example: the gene-set of all strains of a species (the pangenome) [7], or quantification of individual SNPs found in microbial communities (variome) [8] or in viruses, the strains related by mutations in a highly mutagenic environment (the quasispecies) [9].Reconstructing population-level variation in microbial communities is limited by our inability to culture in vitro many microbes from the environment. Researchers must instead rely on DNA isolated and sequenced directly from an environment (metagenomics) which generally results in highly fragmented and incomplete data containing sequencing errors. This...

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“…With the advent of long-read technology, ProbHap recognised a niche in applying computationally expensive dynamic programming solutions to low coverage long-reads [41]. These solutions are inappropriate for high-depth short read data sets as the run time increases exponentially with coverage.…”

Section: Methodsological Comparison Of Our Approachmentioning

confidence: 99%

“…The more recent ViQuaS [40] reports higher recall than PredictHaplo, as expected for an algorithm based on an overlap assembler. However its precision is influenced by the quality of the available reference for the postassembly filtering step, making ViQuaS less suitable for the analysis of a metagenome, where a good reference is unavailable.With the advent of long-read technology, ProbHap recognised a niche in applying computationally expensive dynamic programming solutions to low coverage long-reads [41]. These solutions are inappropriate for high-depth short read data sets as the run time increases exponentially with coverage.…”

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confidence: 99%

Probabilistic recovery of cryptic haplotypes from metagenomic data

Nicholls

Aubrey²,

Grave

et al. 2017

Preprint

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The cryptic diversity of microbial communities represent an untapped biotechnological resource for biomining, biorefining and synthetic biology. Revealing this information requires the recovery of the exact sequence of DNA bases (or "haplotype") that constitutes the genes and genomes of every individual present. This is a computationally difficult problem complicated by the requirement for environmental sequencing approaches (metagenomics) due to the resistance of the constituent organisms to culturing in vitro.Haplotypes are identified by their unique combination of DNA variants. However, standard approaches for working with metagenomic data require simplifications that violate assumptions in the process of identifying such variation. Furthermore, current haplotyping methods lack objective mechanisms for choosing between alternative haplotype reconstructions from microbial communities. To address this, we have developed a novel probabilistic approach for reconstructing haplotypes from complex microbial communities and propose the "metahaplome" as a definition for the set of haplotypes for any particular genomic region of interest within a metagenomic dataset. Implemented in the twin software tools Hansel and Gretel, the algorithm performs incremental probabilistic haplotype recovery using Naive Bayes -an efficient and effective technique. Our approach is capable of reconstructing the haplotypes with the highest likelihoods from metagenomic datasets without a priori knowledge or making assumptions of the distribution or number of variants. Additionally, the algorithm is robust to sequencing and alignment error without altering or discarding observed variation and uses all available evidence from aligned reads. We validate our approach using synthetic metahaplomes constructed from sets of real genes, and demonstrate its capability using metagenomic data from a complex HIV-1 strain mix. The results show that the likelihood framework can allow recovery from microbial communities of cryptic functional isoforms of genes with 100% accuracy. 1. CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/117838 doi: bioRxiv preprint first posted online Mar. 17, 2017; Genomic research is progressing beyond the use of consensus DNA sequences to represent species, towards the ultimate goal of complete characterisation of the genetic diversity that exists across their populations.So far, research has focused on characterising specific aspects of this diversity, for example: identifying the entire gene-set of all strains of a species (the pangenome) [1]; identifying the groups of genes (or genetic variants within) that are inherited together in organisms across entire populations (the haplome) [2] or in viruses, identifying strains related by mutations in a highly mutagenic environment (the quasispecies) [3].However many communities (and especially microbial communities) maintain a fine balance b...

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Probabilistic single-individual haplotyping

Cited by 60 publications

References 19 publications

HapCol: accurate and memory-efficient haplotype assembly from long reads

HapCol: accurate and memory-efficient haplotype assembly from long reads

Recovery of gene haplotypes from a metagenome

Probabilistic recovery of cryptic haplotypes from metagenomic data

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