Molecular Modelling Hurdle in the Next-Generation Sequencing Era

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Clinical genomics units face challenges in diagnosing rare genetic diseases.
• Next-generation sequencing (NGS) has increased variant detection but also the number of variants of uncertain significance (VUSs).
• Current predictive algorithms for VUS pathogenicity are insufficient for definitive diagnosis.

Purpose of the Study:

• To highlight the difficulties in diagnosing rare genetic diseases due to genomic variability identified by NGS.
• To emphasize the crucial role of molecular modeling in assessing mutation relevance and protein malfunction.
• To propose a multi-omics data model for improved variant pathogenicity classification in clinical settings.

Main Methods:

• Review of current genetic diagnostic techniques and challenges.
• Analysis of the impact of NGS on variant interpretation.
• Exploration of molecular modeling and multi-omics data integration for variant evaluation.

Main Results:

• NGS significantly increases the volume of genetic variants to analyze, complicating rare disease diagnosis.
• Molecular modeling is essential for understanding how mutations affect protein function.
• A multi-omics data model is proposed to enhance the classification of genomic variability.

Conclusions:

• Molecular modeling is a key component for elucidating the functional impact of genetic variants in rare diseases.
• Integrating multi-omics data into a unified model can improve the classification of VUS pathogenicity.
• Systematic incorporation of these approaches into the diagnostic pipeline is crucial for clinical genomics.