A graph-based approach to diploid genome assembly

Garg, Shilpa; Rautiainen, Mikko; Novak, Adam M.; Garrison, Erik; Durbin, Richard; Marschall, Tobias

doi:10.1093/bioinformatics/bty279

Cited by 62 publications

(60 citation statements)

References 52 publications

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“…It is likely that improved alignment methods will lead to improved results here as well, and we are currently investigating this further. Lastly, GraphAligner might enable scaling the haplotype-resolved genome assembly method that we demonstrated for yeast genomes [11] to mammalian genomes.…”

Section: Discussionmentioning

confidence: 99%

“…Graphs provide a natural way of expressing variation or uncertainty in a genome [1,2]. They have been used for diverse applications such as genome assembly [3,4,5], error correction [6,7,8], short tandem repeat genotyping [9], structural variation genotyping [10] and reference-free haplotype reconstruction [11]. With the growing usage of graphs, methods for handling graphs efficiently are becoming a crucial requirement for many applications.…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

Rautiainen

Marschall

2019

Preprint

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“…This problem is exacerbated in highly heterozygous genomes, resulting in fragmented and inflated assemblies which impede downstream analyses [3,4]. Furthermore, a consensus sequence does not represent either true, parental haplotype, leading to loss of haplotype-specific information such as allelic and structural variants [5]. Whilst reducing heterozygosity by inbreeding has been a frequent approach, rearing inbred lines is unfeasible and highly time consuming for many non-model systems, and resulting genomes may no longer be representative of wild populations.…”

Section: Data Description Backgroundmentioning

confidence: 99%

A haplotype-resolved,de novogenome assembly for the wood tiger moth (Arctia plantaginis) through trio binning

Yen

McCarthy

Galarza

et al. 2020

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Background: Diploid genome assembly is typically impeded by heterozygosity, as it introduces errors when haplotypes are collapsed into a consensus sequence. Trio binning offers an innovative solution which exploits heterozygosity for assembly. Short, parental reads are used to assign parental origin to long reads from their F1 offspring before assembly, enabling complete haplotype resolution. Trio binning could therefore provide an effective strategy for assembling highly heterozygous genomes which are traditionally problematic, such as insect genomes. This includes the wood tiger moth (Arctia plantaginis), which is an evolutionary study system for warning colour polymorphism. Findings: We produced a highquality, haplotype-resolved assembly for Arctia plantaginis through trio binning. We sequenced a same-species family (F1 heterozygosity ~1.9%) and used parental Illumina reads to bin 99.98% of offspring Pacific Biosciences reads by parental origin, before assembling each haplotype separately and scaffolding with 10X linked-reads. Both assemblies are highly contiguous (mean scaffold N50: 8.2Mb) and complete (mean BUSCO completeness: 97.3%), with complete annotations and 31 chromosomes identified through karyotyping. We employed the assembly to analyse genome-wide population structure and relationships between 40 wild resequenced individuals from five populations across Europe, revealing the Georgian population as the most genetically differentiated with the lowest genetic diversity. Conclusions:We present the first invertebrate genome to be assembled via trio binning. This assembly is one of the highest quality genomes available for Lepidoptera, supporting trio binning as a potent strategy for assembling highly heterozygous genomes. Using this assembly, we provide genomic insights into geographic population structure of Arctia plantaginis.

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“…However, the methods described above collapse differences between homologous pairs into a single consensus sequence, without regards for the rich information given by the layout of different alleles along two DNA strands (Simpson and Pop, 2015). By contrast, other assemblers by Chin et al (2016); Garg et al (2018); Weisenfeld et al (2017) have been developed to generate haplotigs, haplotype-resolves assemblies for diploid genomes.…”

Section: Introductionmentioning

confidence: 99%

“…Given a pedigree graph containing a series of bubbles, long-read (PacBio) data from each individual are threaded through it; in essence, the most probable paths that the long reads trace through the bubbles in our pedigree graph, and which obey the Mendelian constraints imposed by the pedigree, represent our true haplotypes. We draw key concepts from the graph-based assembly approach for single individuals described in Garg et al (2018) and the PedMEC formulation set forth in Garg et al (2016), yet synthesize and extend them to overcome their respective shortcomings.…”

Section: Introductionmentioning

confidence: 99%

A haplotype-aware de novo assembly of related individuals using pedigree graph

Garg

Aach²,

et al. 2019

Preprint

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Motivation:Reconstructing high-quality haplotype-resolved assemblies for related individuals of various species has important applications in understanding Mendelian diseases along with evolutionary and comparative genomics. Through major genomics sequencing efforts such as the Personal Genome Project, the Vertebrate Genome Project (VGP), the Earth Biogenome Project (EBP) and the Genome in a Bottle project (GIAB), a variety of sequencing datasets from mother-father-child trios of various diploid species are becoming available. Current trio assembly approaches are not designed to incorporate long-read sequencing data from parents in a trio, and therefore require relatively high coverages of costly long-read data to produce high-quality assemblies. Thus, building a trio-aware assembler capable of producing accurate and chromosomal-scale diploid genomes in a pedigree, while being cost-effective in terms of sequencing costs, is a pressing need of the genomics community. Results: We present a novel pedigree-graph-based approach to diploid assembly using accurate Illumina data and long-read Pacific Biosciences (PacBio) data from all related individuals, thereby generalizing our previous work on single individuals. We demonstrate the effectiveness of our pedigree approach on a simulated trio of pseudo-diploid yeast genomes with different heterozygosity rates, and real data from Arabidopsis Thaliana. We show that we require as little as 30× coverage Illumina data and 15× PacBio data from each individual in a trio to generate chromosomal-scale phased assemblies. Additionally, we show that we can detect and phase variants from generated phased assemblies. Availability: https://github.com/shilpagarg/WHdenovo

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A graph-based approach to diploid genome assembly

Cited by 62 publications

References 52 publications

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

A haplotype-resolved,de novogenome assembly for the wood tiger moth (Arctia plantaginis) through trio binning

A haplotype-aware de novo assembly of related individuals using pedigree graph

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