Sequence variation aware genome references and read mapping with the variation graph toolkit

Garrison, Erik; Sirén, Jouni; Novak, Adam M.; Hickey, Glenn; Eizenga, Jordan M.; Et, Dawson; Jones, Wendy D.; Mei, Lin; Paten, Benedict; Durbin, Richard

doi:10.1101/234856

Cited by 24 publications

(29 citation statements)

References 34 publications

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“…While methods for including variants in the reference are growing in number [6,7,8,9,10,11,12], there is little or no work on how to choose which variants to include. Past studies have made such decisions in ad hoc ways, with some filtering according to allele frequency [13,8], ethnicity [7], or both [9].…”

Section: Introductionmentioning

confidence: 99%

FORGe: prioritizing variants for graph genomes

Pritt

Langmead

2018

Preprint

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There is growing interest in using genetic variants to augment the reference genome into a "graph genome" to improve read alignment accuracy and reduce allelic bias. While adding a variant has the positive effect of removing an undesirable alignmentscore penalty, it also increases both the ambiguity of the reference genome and the cost of storing and querying the genome index. We introduce methods and a software tool called FORGe for modeling these effects and prioritizing variants accordingly. We show that FORGe enables a range of advantageous and measurable trade-offs between accuracy and computational overhead. more variation to the reference eventually reduces alignment accuracy. We suggest efficient models for scoring variants according to the effect on accuracy and "blowup" (computational overhead), and further show that these scores can be used to achieve a balance of accuracy and overhead superior to current approaches. For example, extrapolating to a whole-human DNA sequencing experiment at 40-fold average coverage, we estimate that a well-engineered augmented reference can yield about 4.8M more correctly aligned reads and 1.2M fewer incorrectly aligned compared to the linear reference. Our methods for selecting variants also reduce reference bias, a chief goals of graph genomes. Finally, we compare the accuracy yielded by our methods to that achieved using an ideal personalized graph genome. We show that our methods approach the ideal much more closely than both linear genomes -even when they are modified to contain only major allelesand graph genomes built on different sets of variants.These methods are implemented in a new open source software tool called FORGe. We demonstrate FORGe in conjunction with the HISAT2 [12] graph aligner and with another aligner based on the Enhanced Reference Genome [7]. But FORGe's models and methods are suitable for any aligner that can include variants in the reference.

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Section: Introductionmentioning

confidence: 99%

FORGe: prioritizing variants for graph genomes

Pritt

Langmead

2018

Preprint

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“…Given a flat reference genome, and a set of sequence resolved alleles in a VCF/BCF format, we can create a reference variant graph (RVG). This RVG is an in-memory extension of a given flat reference with paths (branches) that diverge from the reference for each of these variants, creating a graph representation similar to that described in depth in [ 12 ]. Edges of the graph represent inter-base reference locations between alleles, and nodes represent nucleotide sequences.…”

Section: Biograph Coveragementioning

confidence: 99%

Leveraging a WGS compression and indexing format with dynamic graph references to call structural variants

English¹,

McCarthy²,

Flickenger³

et al. 2020

Preprint

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We introduce a novel structural variant calling pipeline (BioGraph) that leverages a read compression and indexing format to discover alleles, assess their supporting coverage, and assign useful quality scores. To evaluate BioGraph's performance, we run five sequencing replicates of the individual HG002 through the BioGraph variant calling pipeline, as well as two other short-read sv-calling pipelines. BioGraph detects the GIAB benchmark SVs at a peak sensitivity of ≈59% compared to ≈42% sensitivity from the other pipelines. The overall precision of BioGraph is lower than other pipelines (≈81% and ≈90%, respectively), however, adjusting for quality score, BioGraph calls were sensitive to a greater number of SV calls given the same false discovery rate compared to the other pipelines. Cumulatively ≈77% of GIAB benchmark SVs are discovered by BioGraph in at least one replicate. After merging discovered calls and running BioGraph Coverage to create a squared-off project-level VCF, we find ≈90% percent of discovered true positive alleles have at least 5x coverage in all replicates, thus increasing per-replicate recall of alleles having at least 1x coverage to ≈76.9%.

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“…To test our peak caller, we used vg [5] to create a whole genome Arabidopsis thaliana reference graph by using variants from The 1001 Genomes Project. We selected all transcription factors listed in the transcription factor database of Expresso [10] that also had a motif in the Jaspar database of transcription factor binding profiles [11], resulting in a set of 5 transcription factors: ERF115, SEP3, AP1, SOC1, and PI.…”

Section: Validation and Testingmentioning

confidence: 99%

“…Graph-based reference genomes offers a way to include known variants in the reference structure [3]. The software package vg supports mapping reads to a graph-based reference genome with potentially increased accuracy [4,5] as compared to mapping reads to a standard linear reference genome using tools like BWA-MEM [6] or Bowtie [7]. Several types of genomic analyses, such as variant calling and haplotyping, can now be performed using graph-based references [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…The software package vg supports mapping reads to a graph-based reference genome with potentially increased accuracy [4,5] as compared to mapping reads to a standard linear reference genome using tools like BWA-MEM [6] or Bowtie [7]. Several types of genomic analyses, such as variant calling and haplotyping, can now be performed using graph-based references [4,5]. However, no tool currently exists for performing peak calling on graph-based references.…”

Section: Introductionmentioning

confidence: 99%

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Graph Peak Caller: calling ChIP-Seq Peaks on Graph-based Reference Genomes

Grytten

Rand

Nederbragt

et al. 2018

Preprint

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Graph-based representations are considered to be the future for reference genomes, as they allow integrated representation of the steadily increasing data on individual variation. Currently available tools allow de novo assembly of graph-based reference genomes, alignment of new read sets to the graph representation as well as certain analyses like variant calling and haplotyping. We here present a first method for calling ChIP-Seq peaks on read data aligned to a graph-based reference genome. The method is a graph generalization of the peak caller MACS2, and is implemented in an open source tool, Graph Peak Caller. By using the existing tool vg to build a pan-genome of Arabidopsis thaliana, we validate our approach by showing that Graph Peak Caller with a pan-genome reference graph can trace variants within peaks that are not part of the linear reference genome, and find peaks that in general are more motif-enriched than those found by MACS2.

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Sequence variation aware genome references and read mapping with the variation graph toolkit

Cited by 24 publications

References 34 publications

FORGe: prioritizing variants for graph genomes

FORGe: prioritizing variants for graph genomes

Leveraging a WGS compression and indexing format with dynamic graph references to call structural variants

Graph Peak Caller: calling ChIP-Seq Peaks on Graph-based Reference Genomes

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