CDsyn: A comprehensive database for deleterious human synonymous variation prediction

Area of Science:

• Genetics
• Bioinformatics
• Genomic Medicine

Background:

• Synonymous variations were historically underestimated for their role in disease.
• Emerging evidence highlights their significant impact on gene regulation and human diseases.

Purpose of the Study:

• To develop a comprehensive database (CDsyn) for predicting deleterious synonymous variations.
• To assess the significance and disease relevance of synonymous variations.
• To improve the understanding of synonymous variations in human diseases.

Main Methods:

• CDsyn integrates six data categories: prediction scores, conservation, translation efficiency, sequence, population frequency, and annotations.
• Emphasis on splicing mutation prediction scores for identifying pathogenic synonymous mutations.
• Development of a pathogenicity prediction algorithm for synonymous variations using CDsyn.

Main Results:

• The CDsyn-comprehensive approach outperformed existing databases (SynMICdb, dbDSM) and tools (InterVar).
• Demonstrated the effectiveness of splicing mutation prediction in identifying pathogenic variants.
• Validated the feasibility of a CDsyn-based pathogenicity prediction algorithm.

Conclusions:

• CDsyn serves as a valuable resource for prioritizing deleterious variants in clinical sequencing.
• Highlights the critical role of synonymous variations in human disease.
• Enhances the understanding and clinical utility of synonymous variation analysis.