Comparing time series transcriptome data between plants using a network module finding algorithm

Area of Science:

• Plant biology
• Genomics
• Bioinformatics

Background:

• Comparative transcriptome analysis aids functional gene annotation in diverse plant species.
• Conserved biological processes like embryo development pose challenges in cross-species stage mapping.
• Most plant molecular studies focus on model species (e.g., Arabidopsis, rice).

Purpose of the Study:

• To develop a method for establishing one-to-one mapping of developmental stages between species.
• To enable functional annotation of genes in non-model plant species.
• To reveal functional conservation and divergence of genes across species.

Main Methods:

• Converting gene expression patterns into co-expression networks.
• Applying network module finding algorithms to cross-species co-expression networks.
• Utilizing bash scripts for data processing and R programming with simulated annealing for module finding.

Main Results:

• A comprehensive pipeline for cross-species co-expression network analysis was developed.
• The method allows for the visualization of co-expression networks across species.
• Functional conservation and divergence of genes were demonstrated using this approach.

Conclusions:

• The pipeline provides a complete workflow from data acquisition to orthologous network identification.
• The method facilitates the understanding of gene function conservation and divergence.
• This approach enhances functional gene annotation in diverse plant species.