Evidence map›Paper›PMID 40770717›Full record

ArticlePlant methods2025

The evaluation of different combinations of enzyme set, aligner and caller in GBS sequencing of soybean.

Aleksei Zamalutdinov, Stepan Boldyrev, Cécile Ben, Laurent Gentzbittel

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In one paragraph

Article in Plant methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Aleksei ZamalutdinovProject Center for Agro Technologies, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia. Alexey.Zamalutdinov@skoltech.ru.
Stepan BoldyrevProject Center for Agro Technologies, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia.
Cécile BenProject Center for Agro Technologies, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia.
Laurent GentzbittelProject Center for Agro Technologies, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenotype-by-sequencing (GBS) is a cost-effective method for large-scale genotyping, widely used across various species, particularly those with large genomes. A critical aspect of GBS lies in the selection of restriction enzymes for genome digestion and the optimization of data analysis pipelines. However, few studies have comprehensively examined the combined effects of enzyme choice and pipeline configuration.

resultsIn this study, we created GBS libraries using three commonly used restriction enzyme combinations (HindIII-NlaIII, PstI-MspI, and ApeKI) and assessed multiple SNP-calling pipelines in 15 soybean varieties. We tested four aligners (BWA-MEM, Bowtie2, BBMap, and Strobealign) and seven SNP callers (Bcftools, Stacks, DeepVariant, FreeBayes, VarScan, BBCallVariants, and GATK). Our finding reveal that enzyme choice significantly influences the number of identified SNP, gene localization preferences, and accuracy. Furthermore, the performance of SNP callers varied markedly in terms of SNP count, precision, recall, and false discovery rate (FDR). DeepVariant exhibited the highest accuracy, with 76.0% of its SNPs intersecting with whole-genome sequencing (WGS)-derived SNPs and an FDR of 0.0095, compared to FreeBayes, which had 47.8% intersection and an FDR of 0.6321.

conclusionsOur findings underscore the importance of optimizing both enzyme selection for sequencing libraries and data analysis pipelines to ensure robust and reproducible results. This study provides a general framework for designing large-scale genotyping experiments aimed to specific quality and quantity requirements in various plant species.

Indexed as

ComparisonddRADGenotype-by-sequencing (GBS)SNP callingSoybean

Identifiers

PMID40770717
PMCPMC12330036

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.