An integrated approach for analyzing clinical genomic variant data from next-generation sequencing

Area of Science:

• Genomic Medicine
• Bioinformatics
• Clinical Diagnostics

Background:

• Next-generation sequencing (NGS) offers high-throughput, low-cost clinical diagnostics.
• Bioinformatics analysis is a key limitation in clinical genomic NGS applications.

Purpose of the Study:

• To develop an integrated, end-to-end bioinformatics approach for clinical NGS data analysis.
• To improve variant detection, annotation, and functional profiling for rare genetic diseases.

Main Methods:

• Implemented robust pipelines for genome alignment and variant detection (SNP, InDel, CNV) from whole exome sequencing (WES) data.
• Utilized quality control metrics and a validated training dataset for data integrity.
• Developed custom algorithms for variant filtering based on quality, inheritance, and protein function impact.

Main Results:

• Integrated pipeline for variant detection to functional profiling.
• Annotation of variants with disease population and impact data.
• Linking rare variants to visualization (Integrated Genome Viewer) and information resources (iProXpress).

Conclusions:

• A novel methodology for downstream variant filtering in rare genetic diseases.
• Empowers clinicians and researchers to interpret genomic alterations more effectively.
• Facilitates improved understanding of genetic disease relevance.