GAPGOM-an R package for gene annotation prediction using GO Metrics.

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Gene Ontology (GO) terms are crucial for describing gene product functions.
• Functional annotation is limited for many genes, especially long non-coding RNAs (lncRNAs).
• Predicting GO annotations via co-expressed gene properties is a viable approach.

Purpose of the Study:

• To develop an integrated R package for predicting GO annotations of lncRNAs.
• To improve the performance and usability of existing annotation prediction tools.
• To facilitate the comparison and benchmarking of GO graph similarity.

Main Methods:

• Integration of lncRNA2GOA (co-expression-based annotation prediction) and TopoICSim (GO graph similarity estimation) algorithms.
• Development of a user-friendly R package named GAPGOM.
• Enhancement of original algorithms for improved performance and documentation.

Main Results:

• GAPGOM provides a unified interface for annotation prediction and GO graph comparison.
• The package offers substantial improvements in performance and documentation over original tools.
• Enables more efficient functional annotation prediction for lncRNAs.

Conclusions:

• GAPGOM enhances the prediction of functional annotations for lncRNAs.
• The R package offers improved performance and usability for bioinformatics research.
• Facilitates a deeper understanding of lncRNA function through GO annotation.