Reference flow: reducing reference bias using multiple population genomes

Chen, Nae-Chyun; Solomon, Brad; Mun, Taher; Iyer, Sheila; Langmead, Ben

doi:10.1186/s13059-020-02229-3

Cited by 74 publications

(60 citation statements)

References 55 publications

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“…Instead, large-scale reference panels from a wide range of populations can provide similar information [5,6]. Recent studies use such information to improve alignment accuracy and reduce biases in alignment [11][12][13], but there has been little work to incorporate population data with variant calling.…”

Section: Introductionmentioning

confidence: 99%

Improving variant calling using population data and deep learning

Chen

Kolesnikov

Goel

et al. 2021

Preprint

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Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we modify DeepVariant to add a new channel encoding population allele frequencies from the 1000 Genomes Project. We show that this model reduces variant calling errors, improving both precision and recall. We assess the impact of using population-specific or diverse reference panels. We achieve the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel.

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Section: Introductionmentioning

confidence: 99%

Improving variant calling using population data and deep learning

Chen

Kolesnikov

Goel

et al. 2021

Preprint

Self Cite

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“…Full amelioration of reference bias likely requires a genome from all six species in the study, which are not currently available. If they do become available, the optimal strategy for reducing reference bias is not yet clear, but strategies like aligning the resequencing data to multiple assemblies, e.g., [ 65 , 66 ] or to a Vitis pangenome may prove fruitful. Fortunately, however, at least two studies have evaluated reference bias in population genetics analyses, and they concluded that the effect of the reference bias is unlikely to bias broad demographic and evolutionary genomic analyses [ 62 , 63 ].…”

Section: Discussionmentioning

confidence: 99%

Introgression among North American wild grapes (Vitis) fuels biotic and abiotic adaptation

et al. 2021

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Background Introgressive hybridization can reassort genetic variants into beneficial combinations, permitting adaptation to new ecological niches. To evaluate evolutionary patterns and dynamics that contribute to introgression, we investigate six wild Vitis species that are native to the Southwestern United States and useful for breeding grapevine (V. vinifera) rootstocks. Results By creating a reference genome assembly from one wild species, V. arizonica, and by resequencing 130 accessions, we focus on identifying putatively introgressed regions (pIRs) between species. We find six species pairs with signals of introgression between them, comprising up to ~ 8% of the extant genome for some pairs. The pIRs tend to be gene poor, located in regions of high recombination and enriched for genes implicated in disease resistance functions. To assess potential pIR function, we explore SNP associations to bioclimatic variables and to bacterial levels after infection with the causative agent of Pierce’s disease (Xylella fastidiosa). pIRs are enriched for SNPs associated with both climate and bacterial levels, suggesting that introgression is driven by adaptation to biotic and abiotic stressors. Conclusions Altogether, this study yields insights into the genomic extent of introgression, potential pressures that shape adaptive introgression, and the evolutionary history of economically important wild relatives of a critical crop.

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“…For years, a myriad of genomic analyses have been conducted by mapping to this genome, including functional annotation 44 , disease association [45][46][47][48] and genetic ancestry tests [49][50][51][52] . However, the nature of mapping restricts its resolution to identify novel variants other than those found in the initially recruited samples 53,54 . Our proposed GABOLA assembly integrates the merits of linked read technology and optical mapping, while providing our novelty in locating associated reads in potentially difficult-to-solve regions.…”

Section: Contribution To Human Reference Genomementioning

confidence: 99%

GABOLA: A Reliable Gap-Filling Strategy for de novo Chromosome-Level Assembly

Chuang¹,

Cheng²,

Chang³

et al. 2021

Preprint

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We propose a novel method, GABOLA, which utilizes long-range genomic information provided by accurate linked short reads jointly with long reads to improve the integrity and resolution of whole genome assemblies especially in complex genetic regions. We validated GABOLA on human and Japanese eel genomes. On the two human samples, we filled in more bases spanning 23.3 Mbp and 46.2 Mbp than Supernova assembler, covering over 3,200 functional genes which includes 8,500 exons and 15,000 transcripts. Among them, multiple genes related to various types of cancer were identified. Moreover, we discovered additional 11,031,487 base pairs of repeat sequences and 218 exclusive repeat patterns, some of which are known to be linked to several disorders such as neuron degenerative diseases. As for the eel genome, we successfully raised the genetic benchmarking score to 94.6% while adding 24.7 million base pairs. These results manifest the capability of GABOLA in the optimization of whole genome assembly and the potential in precise disease diagnosis and high-quality non-model organism breeding.

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Reference flow: reducing reference bias using multiple population genomes

Cited by 74 publications

References 55 publications

Improving variant calling using population data and deep learning

Improving variant calling using population data and deep learning

Introgression among North American wild grapes (Vitis) fuels biotic and abiotic adaptation

GABOLA: A Reliable Gap-Filling Strategy for de novo Chromosome-Level Assembly

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