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

Area of Science:

• Genetics and Genomics
• Bioinformatics
• Computational Biology

Background:

• Bulked segregant analysis (BSA) is a powerful genetic mapping technique.
• Existing tools lack user-friendliness for researchers without bioinformatics expertise.
• There is a need for integrated, accessible tools for BSA using next-generation sequencing (NGS) data.

Purpose of the Study:

• To develop an R package, PNGSeqR, that simplifies BSA for genetic mapping.
• To provide user-friendly integration of current BSA algorithms for researchers.
• To enable rapid identification of candidate regions and target genes from NGS data.

Main Methods:

• Developed the R package PNGSeqR.
• Input: single-nucleotide polymorphism (SNP) markers from NGS data in VCF format.
• Implemented four BSA algorithms, permutation tests, and fractile quantiles for candidate region definition.
• Integrated differential expression gene (DEG) and gene ontology (GO) analysis.

Main Results:

• PNGSeqR provides a user-friendly interface for BSA.
• The package integrates multiple BSA algorithms for robust signal detection.
• Efficiently identifies candidate regions and prioritizes target genes.
• Supports convenient export of analysis plots.

Conclusions:

• PNGSeqR enhances accessibility of BSA for genetic mapping.
• The tool empowers researchers to conduct complex analyses without extensive bioinformatics skills.
• Facilitates efficient identification of genes associated with traits.