Superbubbles, Ultrabubbles and Cacti

Paten, Benedict; Novak, Adam M.; Garrison, Erik; Hickey, Glenn

doi:10.1101/101493

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…Both superbubbles and ultrabubbles can also identify nesting and overlapping relationships involving structural variants, which makes them a more expressive definition of a site than the reference coordinate. Paten et al (2017) show how this site nesting is naturally described by a cactus graph, a structure that globally organizes the sites without the need for any existing reference genome. However, this approach is incomplete-not all variation is nicely partitioned into these bubbles.…”

Section: Allelism In Graphsmentioning

confidence: 99%

Genome graphs and the evolution of genome inference

et al. 2017

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The human reference genome is part of the foundation of modern human biology and a monumental scientific achievement. However, because it excludes a great deal of common human variation, it introduces a pervasive reference bias into the field of human genomics. To reduce this bias, it makes sense to draw on representative collections of human genomes, brought together into reference cohorts. There are a number of techniques to represent and organize data gleaned from these cohorts, many using ideas implicitly or explicitly borrowed from graph-based models. Here, we survey various projects underway to build and apply these graph-based structures—which we collectively refer to as genome graphs—and discuss the improvements in read mapping, variant calling, and haplotype determination that genome graphs are expected to produce.

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Section: Allelism In Graphsmentioning

confidence: 99%

Genome graphs and the evolution of genome inference

et al. 2017

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“…To account for heterozygosity in a diploid genome, we perform bubble detection. The notion of bubble we use is closely based on the ultrabubble concept as defined by Paten et al (2017) . Briefly, bubbles have the following properties: Two-node-connectivity .…”

Section: Diploid Assembly Pipelinementioning

confidence: 99%

“…In the next stage, we use VG’s snarl decomposition algorithm ( Paten et al , 2017 ) to detect the regions of heterozygosity, or snarls , in the sequence graph. This results in 29 071 bubbles.…”

Section: Datasets and Experimental Setupmentioning

confidence: 99%

A graph-based approach to diploid genome assembly

et al. 2018

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MotivationConstructing high-quality haplotype-resolved de novo assemblies of diploid genomes is important for revealing the full extent of structural variation and its role in health and disease. Current assembly approaches often collapse the two sequences into one haploid consensus sequence and, therefore, fail to capture the diploid nature of the organism under study. Thus, building an assembler capable of producing accurate and complete diploid assemblies, while being resource-efficient with respect to sequencing costs, is a key challenge to be addressed by the bioinformatics community.ResultsWe present a novel graph-based approach to diploid assembly, which combines accurate Illumina data and long-read Pacific Biosciences (PacBio) data. We demonstrate the effectiveness of our method on a pseudo-diploid yeast genome and show that we require as little as 50× coverage Illumina data and 10× PacBio data to generate accurate and complete assemblies. Additionally, we show that our approach has the ability to detect and phase structural variants.Availability and implementation https://github.com/whatshap/whatshap Supplementary information Supplementary data are available at Bioinformatics online.

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“…In order to represent these more challenging types of variation, we use a variation graph . This is a type of sequence graph —a mathematical graph in which nodes represent elements of sequence, augmented with 5′ and 3′sides, and edges are drawn between sides if the adjacency of sequence is observed in the population cohort (see Paten et al , 2017 ). Haplotypes are embedded as paths through oriented nodes in the graph.…”

Section: Methodsmentioning

confidence: 99%

Modelling haplotypes with respect to reference cohort variation graphs

2017

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MotivationCurrent statistical models of haplotypes are limited to panels of haplotypes whose genetic variation can be represented by arrays of values at linearly ordered bi- or multiallelic loci. These methods cannot model structural variants or variants that nest or overlap.ResultsA variation graph is a mathematical structure that can encode arbitrarily complex genetic variation. We present the first haplotype model that operates on a variation graph-embedded population reference cohort. We describe an algorithm to calculate the likelihood that a haplotype arose from this cohort through recombinations and demonstrate time complexity linear in haplotype length and sublinear in population size. We furthermore demonstrate a method of rapidly calculating likelihoods for related haplotypes. We describe mathematical extensions to allow modelling of mutations. This work is an important incremental step for clinical genomics and genetic epidemiology since it is the first haplotype model which can represent all sorts of variation in the population.Availability and ImplementationAvailable on GitHub at https://github.com/yoheirosen/vg.Supplementary information Supplementary data are available at Bioinformatics online.

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Superbubbles, Ultrabubbles and Cacti

Cited by 9 publications

References 16 publications

Genome graphs and the evolution of genome inference

Genome graphs and the evolution of genome inference

A graph-based approach to diploid genome assembly

Modelling haplotypes with respect to reference cohort variation graphs

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