Homoeologous gene expression and co-expression network analyses and evolutionary inference in allopolyploids

Area of Science:

• Genomics
• Bioinformatics
• Evolutionary Biology

Background:

• Polyploidy, the presence of multiple sets of chromosomes, is common in eukaryotes.
• Accurate gene expression quantification in polyploids is crucial for understanding transcriptomic responses but remains challenging.
• Previous studies have not fully addressed the impact of inaccurate transcript quantification from duplicated genes.

Purpose of the Study:

• To develop and evaluate an analytical workflow for polyploid transcriptomic profiling.
• To assess bioinformatic method choices for homoeolog expression quantification and downstream analyses.
• To identify potential artifacts in polyploid gene expression studies.

Main Methods:

• Utilized transcriptomic data from the cotton genus (Gossypium) as a model system.
• Evaluated various bioinformatic pipelines for RNA-seq analysis, focusing on homoeolog expression quantification.
• Assessed co-expression network analysis methods and the impact of homoeolog read ambiguity.

Main Results:

• EAGLE-RC and GSNAP-PolyCat pipelines demonstrated superior performance in homoeolog expression quantification.
• Weighted network construction outperformed binary networks in co-expression analysis.
• Homoeolog read ambiguity can lead to overestimation of co-regulation and incorrect inference of subgenome asymmetry.

Conclusions:

• The study provides a robust workflow for accurate polyploid transcriptomic analysis.
• Recommended bioinformatic practices are crucial for reliable evolutionary studies of polyploids.
• Addressing homoeolog read ambiguity is essential for accurate interpretation of polyploid gene expression data.