ArticleBMC bioinformatics2020
PyBSASeq: a simple and effective algorithm for bulked segregant analysis with whole-genome sequencing data.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Identification of candidate regions associated with resistance to early blight in tomato (Solanum lycopersicum L.) using BSA-seq.Journal of applied genetics · 2026Article
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- Comparison of Recombination Rate, Reference Bias, and Unique Pangenomic Haplotypes inInternational journal of molecular sciences · 2025Article
- Physiological Analysis and Genetic Mapping of Short Hypocotyl Trait inInternational journal of molecular sciences · 2023Article
- Maize LOST SUBSIDIARY CELL encoding a large subunit of ribonucleotide reductase is required for subsidiary cell development and plant growth.Journal of experimental botany · 2023Article
- Bulk segregant linkage mapping for rodent and human malaria parasites.Parasitology international · 2022Review
- Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches.BMC genomics · 2022Article
- Predicting the genomic resolution of bulk segregant analysis.G3 (Bethesda, Md.) · 2022Article
- Next-generation sequencing-based bulked segregant analysis without sequencing the parental genomes.G3 (Bethesda, Md.) · 2022Article
- Review
- Identification and mapping of major-effect flowering time lociFrontiers in plant science · 2022Article
- Harnessing the potential of bulk segregant analysis sequencing and its related approaches in crop breeding.Frontiers in genetics · 2022Review
- dQTG.seq: A comprehensive R tool for detecting all types of QTLs using extreme phenotype individuals in bi-parental segregation populations.Computational and structural biotechnology journal · 2022Article
- Article
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2 authors.
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Abstract
backgroundBulked segregant analysis (BSA), coupled with next-generation sequencing, allows the rapid identification of both qualitative and quantitative trait loci (QTL), and this technique is referred to as BSA-Seq here. The current SNP index method and G-statistic method for BSA-Seq data analysis require relatively high sequencing coverage to detect significant single nucleotide polymorphism (SNP)-trait associations, which leads to high sequencing cost.
resultsWe developed a simple and effective algorithm for BSA-Seq data analysis and implemented it in Python; the program was named PyBSASeq. Using PyBSASeq, the significant SNPs (sSNPs), SNPs likely associated with the trait, were identified via Fisher's exact test, and then the ratio of the sSNPs to total SNPs in a chromosomal interval was used to detect the genomic regions that condition the trait of interest. The results obtained this way are similar to those generated via the current methods, but with more than five times higher sensitivity. This approach was termed the significant SNP method here.
conclusionsThe significant SNP method allows the detection of SNP-trait associations at much lower sequencing coverage than the current methods, leading to ~ 80% lower sequencing cost and making BSA-Seq more accessible to the research community and more applicable to the species with a large genome.
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