Zhen Sihan, Zhang Hongwei, Xie Yuxin, Zhang Song, Chen Yan, Gu Riliang, Liu Sanzhen, Du Xuemei, Fu Junjie
Seed Science and Technology Research Center, Beijing Innovation Center for Seed Technology (MOA), Beijing Key Laboratory for Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China.
Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Plants (Basel). 2022 Jul 11;11(14):1821. doi: 10.3390/plants11141821.
Although bulked segregant analysis (BSA) has been used extensively in genetic mapping, user-friendly tools which can integrate current algorithms for researchers with no background in bioinformatics are scarce. To address this issue, we developed an R package, PNGSeqR, which takes single-nucleotide polymorphism (SNP) markers from next-generation sequencing (NGS) data in variant call format (VCF) as the input file, provides four BSA algorithms to indicate the magnitude of genome-wide signals, and rapidly defines the candidate region through the permutation test and fractile quantile. Users can choose the analysis methods according to their data and experimental design. In addition, it also supports differential expression gene analysis (DEG) and gene ontology analysis (GO) to prioritize the target gene. Once the analysis is completed, the plots can conveniently be exported.
尽管混合分组分析法(BSA)已在基因定位中广泛应用,但针对没有生物信息学背景的研究人员,能整合当前算法的用户友好型工具却很匮乏。为解决这一问题,我们开发了一个R包PNGSeqR,它将来自以变异调用格式(VCF)存储的下一代测序(NGS)数据中的单核苷酸多态性(SNP)标记作为输入文件,提供四种BSA算法以指示全基因组信号的强度,并通过置换检验和分位数快速定义候选区域。用户可根据自身数据和实验设计选择分析方法。此外,它还支持差异表达基因分析(DEG)和基因本体分析(GO),以便对目标基因进行优先级排序。分析完成后,可方便地导出图表。