PyBSASeq: a simple and effective algorithm for bulked segregant analysis with whole-genome sequencing data
作者: Jianbo ZhangDilip R. Panthee
作者单位: 1Department of Horticultural Science, North Carolina State University, Mountain Horticultural Crops Research and Extension Center, 455 Research Drive, 28759, Mills River, NC, USA
刊名: BMC Bioinformatics, 2020, Vol.21 (2), pp.6553-6568
来源数据库: Springer Nature Journal
DOI: 10.1186/s12859-020-3435-8
关键词: Bulked segregant analysisBSA-SeqPyBSASeqQTLSNP-trait association
英文摘要: Abstract(#br)Background(#br)Bulked 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. Results(#br)We 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...
全文获取路径: Springer Nature  (合作)
影响因子:3.024 (2012)