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PNGSeqR: An R Package for Rapid Candidate Gene Selection through Pooled Next-Generation Sequencing

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...

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Detalles Bibliográficos
Autores principales: Zhen, Sihan, Zhang, Hongwei, Xie, Yuxin, Zhang, Song, Chen, Yan, Gu, Riliang, Liu, Sanzhen, Du, Xuemei, Fu, Junjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315718/
https://www.ncbi.nlm.nih.gov/pubmed/35890455
http://dx.doi.org/10.3390/plants11141821
Descripción
Sumario: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.