Paragraph: A graph-based structural variant genotyper for short-read sequence data

Chen, Sai; Krusche, Peter; Dolzhenko, Egor; Sherman, Rachel M.; Petrovski, Roman; Schlesinger, Felix; Kirsche, Michael; Schatz, Michael C.; Sedlazeck, Fritz J.; Eberle, Michael A.

doi:10.1101/635011

Cited by 48 publications

(69 citation statements)

References 54 publications

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“…To assess the population frequency of these variants, we genotyped identified SVs affecting COSMIC genes from the three analyzed cancer samples with Paragraph 54 in the dataset of 2,504 short-read WGS samples from the recent re-sequencing of the 1000 genomes project (1KGP) samples 46 . Paragraph genotypes SVs by constructing localized sequence graphs containing the reference allele and the candidate SV allele and performs a localized realignment of paired-end short reads to the graph.…”

Section: Resultsmentioning

confidence: 99%

Comprehensive analysis of structural variants in breast cancer genomes using single molecule sequencing

Aganezov

Goodwin

Sherman

et al. 2019

Preprint

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Improved identification of structural variants (SVs) in cancer can lead to more targeted and effective treatment options as well as advance our basic understanding of disease progression. We performed whole genome sequencing of the SKBR3 breast cancer cell-line and patient-derived tumor and normal organoids from two breast cancer patients using 10X/Illumina, PacBio, and Oxford Nanopore sequencing. We then inferred SVs and large-scale allele-specific copy number variants (CNVs) using an ensemble of methods. Our findings demonstrate that long-read sequencing allows for substantially more accurate and sensitive SV detection, with between 90% and 95% of variants supported by each long-read technology also supported by the other. We also report high accuracy for long-reads even at relatively low coverage (25x-30x). Furthermore, we inferred karyotypes from these data using our enhanced RCK algorithm to present a more accurate representation of the mutated cancer genomes, and find hundreds of variants affecting known cancer-related genes detectable only through long-read sequencing. These findings highlight the need for long-read sequencing of cancer genomes for the precise analysis of their genetic instability.

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Section: Resultsmentioning

confidence: 99%

Comprehensive analysis of structural variants in breast cancer genomes using single molecule sequencing

Aganezov

Goodwin

Sherman

et al. 2019

Preprint

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“…[52]) is not available in cattle. Recent studies indicated that large structural variants can be identified accurately from genome graphs [32,[53][54][55]. Eventually, a bovine genome graph that unifies multiple breed-specific haplotype-resolved genome assemblies and their sites of variation might provide access to sources of variation that are currently neglected when short sequencing reads are aligned to a linear reference sequence [56,57].…”

Section: Discussionmentioning

confidence: 99%

Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery

Crysnanto

Pausch

2019

Preprint

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BackgroundThe current bovine genomic reference sequence was assembled from the DNA of a Hereford cow.The resulting linear assembly lacks diversity because it does not contain allelic variation. Lack of diversity is a drawback of linear references that causes reference allele bias. High nucleotide diversity and the separation of individuals by hundreds of breeds make cattle ideally suited to investigate the optimal composition of variation-aware references. ResultsWe augment the bovine linear reference sequence (ARS-UCD1.2) with variants filtered for allele frequency in dairy (Brown Swiss, Holstein) and dual-purpose (Fleckvieh, Original Braunvieh) cattle breeds to construct either breed-specific or pan-genome reference graphs using the vg toolkit. We find that read mapping is more accurate to variation-aware than linear references if pre-selected variants are used to construct the genome graphs. Graphs that contain random variants do not improve read mapping over the linear reference sequence. Breed-specific augmented and pangenome graphs enable almost similar mapping accuracy improvements over the linear reference.We construct a whole-genome graph that contains the Hereford-based reference sequence and 14 million alleles that have alternate allele frequency greater than 0.03 in the Brown Swiss cattle breed.We show that our novel variation-aware reference facilitates accurate read mapping and unbiased sequence variant genotyping for SNPs and Indels. ConclusionsWe developed the first variation-aware reference graph for an agricultural animal: https://doi.org/10.5281/zenodo.3570312. Our novel reference structure improves sequence read mapping and variant genotyping over the linear reference. Our work is a first step towards the transition from linear to variation-aware reference structures in species with high genetic diversity and many sub-populations.

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“…The hypervariability of VNTR sequences prevents a single assembly from serving as an optimal reference. Instead, to improve both alignment and genotyping, multiple assemblies may be combined into a pangenome graph (PGG) (Hickey et al 2020;Eggertsson et al 2019;Garrison et al 2018;Chen et al 2019) composed of sequence-labeled vertices connected by edges such that haplotypes correspond to paths in the graph. Sequences shared by multiple haplotypes are stored in the same vertex, and genetic variation is represented by the branching structure of the graph.…”

Section: Introductionmentioning

confidence: 99%

Profiling variable-number tandem repeat variation across populations using repeat-pangenome graphs

Chaisson

2020

Preprint

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Variable number tandem repeats (VNTR) are genetic loci composed of consecutive repeats of short segments of DNA, with hypervariable repeat count and composition across individuals. Many genes have coding VNTR sequences, and noncoding VNTR variants are associated with a wide spectrum of clinical disorders such as Alzheimer's disease, bipolar disorder and colorectal cancer. The identification of VNTR length and composition provides the basis for downstream analysis such as expression quantitative trait loci discovery and genome wide association studies. Disease studies that use high-throughput short read sequencing do not resolve the repeat structures of many VNTR loci because the VNTR sequence is missing from the reference or is too repetitive to map. We solve the VNTR mapping problem for short reads by representing a collection of genomes with a repeat-pangenome graph, a data structure that encodes both the population diversity and repeat structure of VNTR loci. We developed software to build a repeat-pangenome using haplotype-resolved single-molecule sequencing assemblies, and to estimate VNTR length and sequence composition based on the alignment of short read sequences to the graph. Using long-read assemblies as ground truth, we are able to determine which VNTR loci may be accurately profiled using repeat-pangenome graph analysis with short reads. This enabled measuring the global diversity of VNTR sequences in the 1000-Genomes Project, and the discovery of expression quantitative trait loci in the Genotype-Tissue Expression Project. This analysis reveals loci that have significant differences in length and repeat composition between continental populations. Furthermore, the repeat pangenome graph analysis establishes an association between previously inaccessible variation and gene expression. Taken together, these indicate that measuring VNTR sequence diversity with repeat-pangenome graphs will be a critical component of future studies on human diversity and disease.

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Paragraph: A graph-based structural variant genotyper for short-read sequence data

Cited by 48 publications

References 54 publications

Comprehensive analysis of structural variants in breast cancer genomes using single molecule sequencing

Comprehensive analysis of structural variants in breast cancer genomes using single molecule sequencing

Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery

Profiling variable-number tandem repeat variation across populations using repeat-pangenome graphs

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