OpenVar: functional annotation of variants in non-canonical open reading frames

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Technological advances reveal thousands of functional open reading frames (ORFs) missed by standard genome annotations.
• These novel ORFs exist in various genomic locations and transcript types, often overlapping canonical ORFs.
• Lack of specialized software impedes the study of these overlooked ORFs and their role in genetic traits.

Purpose of the Study:

• To introduce OpenVar, a novel genomic variant annotator designed to address the limitations in identifying variants within non-canonical ORFs.
• To demonstrate the utility of OpenVar in discovering previously unidentified high-impact genetic variants.

Main Methods:

• Development of OpenVar, a genomic variant annotation tool.
• Analysis of variants in the Synonyms Mutation Database (SynMicDB) for cancer-associated synonymous mutations.
• Comparison of OpenVar's variant identification capabilities against established tools like Annovar, SnpEff, and VEP.

Main Results:

• OpenVar significantly increases the identification of high-impact variants compared to Annovar (33.6-fold), SnpEff (13.8-fold), and VEP (8.3-fold).
• The study identified an overlapping non-canonical ORF in the HEY2 gene with a significant clustering of variants.
• OpenVar successfully integrates non-canonical ORFs into genomic variant analysis.

Conclusions:

• OpenVar enhances the discovery of genetic variants by incorporating non-canonical ORFs.
• This tool opens new research avenues for understanding genotype-phenotype relationships.
• The findings underscore the importance of considering non-canonical ORFs in genomic studies.