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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

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

Casper van Mourik^1,2, Rezvan Ehsani^3,4,5, Finn Drabløs⁶

¹Department of Cancer Research and Molecular Medicine, NTNU-Norwegian University of Science and Technology, 7491, Trondheim, Norway.

BMC Research Notes

|May 1, 2021

Summary

This study introduces GAPGOM, an R package for predicting Gene Ontology (GO) annotations for long non-coding RNAs (lncRNAs) using co-expression data. It enhances functional annotation discovery for genes with limited information.

Keywords:

Annotation Gene ontology Long non-coding RNAs Prediction

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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.