Dataset Information

Expanded functionality, increased accuracy, and enhanced speed in the de novo genotyping-by-sequencing pipeline GBS-SNP-CROP.

ABSTRACT:

Summary

GBS-SNP-CROP is a bioinformatics pipeline originally developed to support the cost-effective genome-wide characterization of plant genetic resources through paired-end genotyping-by-sequencing (GBS), particularly in the absence of a reference genome. Since its 2016 release, the pipeline's functionality has greatly expanded, its computational efficiency has improved, and its applicability to a broad set of genomic studies for both plants and animals has been demonstrated. This note details the suite of improvements to date, as realized in GBS-SNP-CROP v.4.0, with specific attention paid to a new integrated metric that facilitates reliable variant identification despite the complications of homologs. Using the new de novo GBS read simulator GBS-Pacecar, also introduced in this note, results show an improvement in overall pipeline accuracy from 66% (v.1.0) to 84% (v.4.0), with a time saving of ∼70%. Both GBS-SNP-CROP versions significantly outperform TASSEL-UNEAK; and v.4.0 resolves the issue of non-overlapping variant calls observed between UNEAK and v.1.0.

Availability and implementation

GBS-SNP-CROP source code and user manual are available at https://github.com/halelab/GBS-SNP-CROP. The GBS read simulator GBS-Pacecar is available at https://github.com/halelab/GBS-Pacecar.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Melo ATO

PROVIDER: S-EPMC6513162 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Publications

Expanded functionality, increased accuracy, and enhanced speed in the de novo genotyping-by-sequencing pipeline GBS-SNP-CROP.

Melo Arthur T O ATO Hale Iago I

Bioinformatics (Oxford, England) 20190501 10

<h4>Summary</h4>GBS-SNP-CROP is a bioinformatics pipeline originally developed to support the cost-effective genome-wide characterization of plant genetic resources through paired-end genotyping-by-sequencing (GBS), particularly in the absence of a reference genome. Since its 2016 release, the pipeline's functionality has greatly expanded, its computational efficiency has improved, and its applicability to a broad set of genomic studies for both plants and animals has been demonstrated. This not ...[more]

PMID: 30321264

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Project description:BackgroundWith its simple library preparation and robust approach to genome reduction, genotyping-by-sequencing (GBS) is a flexible and cost-effective strategy for SNP discovery and genotyping, provided an appropriate reference genome is available. For resource-limited curation, research, and breeding programs of underutilized plant genetic resources, however, even low-depth references may not be within reach, despite declining sequencing costs. Such programs would find value in an open-source bioinformatics pipeline that can maximize GBS data usage and perform high-density SNP genotyping in the absence of a reference.ResultsThe GBS SNP-Calling Reference Optional Pipeline (GBS-SNP-CROP) developed and presented here adopts a clustering strategy to build a population-tailored "Mock Reference" from the same GBS data used for downstream SNP calling and genotyping. Designed for libraries of paired-end (PE) reads, GBS-SNP-CROP maximizes data usage by eliminating unnecessary data culling due to imposed read-length uniformity requirements. Using 150 bp PE reads from a GBS library of 48 accessions of tetraploid kiwiberry (Actinidia arguta), GBS-SNP-CROP yielded on average three times as many SNPs as TASSEL-GBS analyses (32 and 64 bp tag lengths) and over 18 times as many as TASSEL-UNEAK, with fewer genotyping errors in all cases, as evidenced by comparing the genotypic characterizations of biological replicates. Using the published reference genome of a related diploid species (A. chinensis), the reference-based version of GBS-SNP-CROP behaved similarly to TASSEL-GBS in terms of the number of SNPs called but had an improved read depth distribution and fewer genotyping errors. Our results also indicate that the sets of SNPs detected by the different pipelines above are largely orthogonal to one another; thus GBS-SNP-CROP may be used to augment the results of alternative analyses, whether or not a reference is available.ConclusionsBy achieving high-density SNP genotyping in populations for which no reference genome is available, GBS-SNP-CROP is worth consideration by curators, researchers, and breeders of under-researched plant genetic resources. In cases where a reference is available, especially if from a related species or when the target population is particularly diverse, GBS-SNP-CROP may complement other reference-based pipelines by extracting more information per sequencing dollar spent. The current version of GBS-SNP-CROP is available at https://github.com/halelab/GBS-SNP-CROP.git.

Project description:BackgroundSpeed congenics is an important tool for creating congenic mice to investigate gene functions, but current SNP genotyping methods for speed congenics are expensive. These methods usually rely on chip or array technologies, and a different assay must be developed for each backcross strain combination. "Next generation" high throughput DNA sequencing technologies have the potential to decrease cost and increase flexibility and power of speed congenics, but thus far have not been utilized for this purpose.ResultsWe took advantage of the power of high throughput sequencing technologies to develop a cost-effective, high-density SNP genotyping assay that can be used across many combinations of backcross strains. The assay surveys 1640 genome-wide SNPs known to be polymorphic across > 100 mouse strains, with an expected average of 549 ± 136 SD diagnostic SNPs between each pair of strains. We demonstrated that the assay has a high density of diagnostic SNPs for backcrossing the BALB/c strain into the C57BL/6J strain (807-819 SNPs), and a sufficient density of diagnostic SNPs for backcrossing the closely related substrains C57BL/6N and C57BL/6J (123-139 SNPs). Furthermore, the assay can easily be modified to include additional diagnostic SNPs for backcrossing other closely related substrains. We also developed a bioinformatic pipeline for SNP genotyping and calculating the percentage of alleles that match the backcross recipient strain for each sample; this information can be used to guide the selection of individuals for the next backcross, and to assess whether individuals have become congenic. We demonstrated the effectiveness of the assay and bioinformatic pipeline with a backcross experiment of BALB/c-IL4/IL13 into C57BL/6J; after six generations of backcrosses, offspring were up to 99.8% congenic.ConclusionsThe SNP genotyping assay and bioinformatic pipeline developed here present a valuable tool for increasing the power and decreasing the cost of many studies that depend on speed congenics. The assay is highly flexible and can be used for combinations of strains that are commonly used for speed congenics. The assay could also be used for other techniques including QTL mapping, standard F2 crosses, ancestry analysis, and forensics.

Dataset Information

Expanded functionality, increased accuracy, and enhanced speed in the de novo genotyping-by-sequencing pipeline GBS-SNP-CROP.

Summary

Availability and implementation

Supplementary information

Publications

Expanded functionality, increased accuracy, and enhanced speed in the de novo genotyping-by-sequencing pipeline GBS-SNP-CROP.

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