Haplotype-aware graph indexes

Sirén, Jouni; Garrison, Erik; Novak, Adam M.; Paten, Benedict; Durbin, Richard

doi:10.1101/559583

Cited by 35 publications

(43 citation statements)

References 36 publications

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“…Indexing variation graphs is challenging because the number of possible paths can be exponential in the number of variants encoded. Typical approaches to handle this problem are to index only some of the variation by limiting the indexed paths either heuristically [16,27,28] or by using panels of known haplotypes [29,30]. A recent method avoids the exponential blowup by dynamically indexing the graph and the reads, thereby exploiting that there can be exponentially many paths in the graphs, but not in the set of reads to be queried [31].…”

Section: Introductionmentioning

confidence: 99%

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

Rautiainen

Marschall

2019

Preprint

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Genome graphs can represent genetic variation and sequence uncertainty. Aligning sequences to genome graphs is key to many applications, including error correction, genome assembly, and genotyping of variants in a pan-genome graph. Yet, so far this step is often prohibitively slow. We present GraphAligner, a tool for aligning long reads to genome graphs. Compared to state-of-the-art tools, GraphAligner is 12x faster and uses 5x less memory, making it as efficient as aligning reads to linear reference genomes. When employing GraphAligner for error correction, we find it to be almost 3x more accurate and over 15x faster than extant tools.Availability: Package manager: https://anaconda.org/bioconda/graphaligner and source code: https://github.com/maickrau/GraphAligner

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Section: Introductionmentioning

confidence: 99%

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

Rautiainen

Marschall

2019

Preprint

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“…Nodes in the graph represent alleles at sites of variation and edges connect adjacent alleles. Once a variation-aware genome graph contains all alleles at known polymorphic sites, every haplotype can be represented as a walk through the graph [24]. However, an optimal balance between graph density and computational complexity is key to efficient whole-genome graph-based variant analysis because adding sites of variation to the graph incurs computational costs [18].…”

Section: Introductionmentioning

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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“…While the above heuristics 62 are also used in vg, they recently also proposed the use of haplotyping. In vg such haplotyping is 63 facilitated using the GBWT [17][18][19]. The GBWT is a graph extension of the positional Burrows-Wheeler 64 transform [20], that can store the haplotypes of samples as paths in the graph, allowing for haplotype 65 constrained read alignment.…”

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confidence: 99%

CHOP: Haplotype-aware path indexing in population graphs

Mokveld

Linthorst

Al-Ars

et al. 2018

Preprint

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The practical use of graph-based reference genomes depends on the ability to align reads to them.Performing substring queries to paths through these graphs lies at the core of this task. The combination of increasing pattern length and encoded variations inevitably leads to a combinatorial explosion of the search space. We propose CHOP a method that uses haplotype information to prevent this from happening. We show that CHOP can be applied to large and complex datasets, by applying it on a graph-based representation of the human genome encoding all 80 million variants reported by the 1000 Genomes project. Pangenomes and their graphical representations have become widespread in the domain of sequencing 1 analysis [1]. Part of this adoption is driven by the increased characterization of within species genomic 2 diversity. For instance, recent versions of the human reference genome (GRCh37 and up), include 3 sequences that represent highly polymorphic regions in the human population [2]. 4A pangenome can be constructed by integrating known variants in the linear reference genome. This 5 way, a pangenome can incorporate sequence diversity in ways that a typical linear reference genome 6 cannot. For example aligning reads to a linear reference genome can lead to an over-representation of 7 the reference allele. This effect, known as reference allele bias, influences highly polymorphic regions 8 and/or regions that are absent from the reference [3,4]. By integrating variants into the alignment 9 process, this bias can be reduced [5][6][7]. As a consequence, variant calling can be improved, with fewer 10 erroneous variants induced by misalignments around indels, and fewer missed variants [8]. An intuitive 11 representation for pangenomes are graph data structures, which are often referred to as population 12 graphs [1,9]. Population graphs can be understood as compressed representations of multiple genomes, 13 with sequence generally represented on the nodes. These nodes are in turn connected by directed edges, 14 such that the full sequence of any genome used to construct the graph can be determined by a specific 15 path traversal through the graph. Alternatively, an arbitrary traversal through the graph will yield a 16 mixture of genomes. 17A key application for reference genomes is read alignment. Most of the linear reference read aligners 18 follow a seed-and-extent paradigm, wherein exact matching substrings (seeds) between the read and a 19 reference are used to constrain a local alignment. To efficiently search for exactly matching substrings 20 (seeding), indexing data structures are used. The construction of these indexes generally relies on 21 one of two methods: k-mer-based indexing, where all substrings of length k are stored in a hash-map 22 along with their positions within the sequence; and sorting-based methods such as the Burrows-Wheeler 23 Transform (BWT), where the reference sequence is transformed into a self-index that supports the lookup 24 of exact-matching substrings of arbitrary length. 25Existi...

show abstract

Haplotype-aware graph indexes

Cited by 35 publications

References 36 publications

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

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

CHOP: Haplotype-aware path indexing in population graphs

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