Systematic refinement of gene annotations by parsing mRNA 3' end sequencing datasets

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Alternative cleavage and polyadenylation generate diverse mRNA 3' isoforms.
• Limited RNA sequencing data hinders comprehensive transcriptome interpretation in complex organisms.
• Existing gene annotation resources are often incomplete, challenging accurate quantification.

Purpose of the Study:

• To introduce 3'GAmES, a computational pipeline for identifying and quantifying novel mRNA 3'end isoforms.
• To improve the comprehensive interpretation and quantification of transcriptomes using 3'mRNA sequencing data.
• To augment cell type-specific transcript ends and enhance quantitative gene expression profiling.

Main Methods:

• Development of a stand-alone computational pipeline named 3'GAmES.
• Utilizing R and bash shell scripts within a Singularity container.
• Processing 3'mRNA sequencing data for isoform identification and quantification.

Main Results:

• 3'GAmES expands existing gene repositories with novel mRNA 3'end isoforms.
• Cost-effective 3' mRNA sequencing with 3'GAmES improves gene-tag counting.
• The pipeline enhances the sensitivity of quantitative gene expression profiling.

Conclusions:

• 3'GAmES provides a robust method for discovering and quantifying mRNA 3' isoforms.
• The pipeline effectively addresses challenges posed by incomplete gene annotations in complex transcriptomes.
• 3'GAmES improves the accuracy and depth of gene expression analysis using 3'mRNA sequencing.