Exploring large-scale gene coexpression networks in peach (Prunus persica L.): a new tool for predicting gene...

Area of Science:

• Plant genetics and genomics
• Bioinformatics
• Molecular biology

Background:

• Identifying genes for peach breeding traits is challenging.
• Gene coexpression networks (GCNs) can reveal stable gene-gene relationships.
• A robust GCN is needed to advance Prunus genetics and genomics.

Purpose of the Study:

• To create and validate a GCN for peach (Prunus) to aid in trait identification and gene function prediction.
• To assess GCN performance using a 'guilty-by-association' approach.
• To demonstrate the utility of the GCN through case studies on fruit softening and anthocyanin regulation.

Main Methods:

• Constructed four GCNs using 604 Illumina RNA-Seq libraries.
• Evaluated GCN performance in predicting functional annotations.
• Validated the best-performing GCN (COO300) with case studies on fruit softening and anthocyanin regulation.

Main Results:

• The COO300 GCN, containing 21,956 genes, demonstrated superior performance in functional annotation prediction.
• Case study 1 identified genes related to cell wall modification and phytohormone response in fruit softening.
• Case study 2 revealed conserved regulatory networks for anthocyanin biosynthesis between peach and grapevine.

Conclusions:

• COO300, now PeachGCN v1.0, is a validated, powerful tool for peach and Prunus research.
• PeachGCN v1.0 facilitates gene function discovery and trait association studies.
• The developed network and analysis scripts are publicly available for the research community.