RNA-Seq Perspectives to Improve Clinical Diagnosis

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Next-generation sequencing (NGS) has advanced genetic diagnostics, but current methods struggle to link sequence variants to regulatory events like differential splicing.
• Identifying splicing variants is crucial for a comprehensive genetic diagnosis, as it can increase diagnostic rates significantly.

Purpose of the Study:

• To highlight the necessity of RNA sequencing (RNA-seq) in clinical routine for detecting splicing events and variants.
• To discuss best practices for RNA-seq analysis in clinical settings, including bioinformatics procedures and challenges.

Main Methods:

• Review of RNA sequencing (RNA-seq) applications in clinical genetic diagnosis.
• Discussion of bioinformatics pipelines and statistical tools for analyzing complex transcriptomic data.
• Exploration of the dynamic nature of the transcriptome and its impact on gene expression and splicing.

Main Results:

• RNA-seq can increase the diagnostic rate in clinical genetics by 10-35% through the detection of splicing events and variants.
• Transcriptome analysis via RNA-seq provides a robust method to understand cellular complexity, tissue-specific variations, and environmental influences on gene regulation.
• Effective RNA-seq analysis requires specialized computational and statistical expertise tailored to specific diseases.

Conclusions:

• RNA sequencing is essential for improving diagnostic accuracy in clinical genetics by capturing crucial splicing information missed by DNA-based methods.
• Implementing RNA-seq requires addressing computational challenges and adapting bioinformatics tools to the dynamic nature of the transcriptome.
• Further development and standardization of RNA-seq analysis are needed for its widespread adoption in routine clinical practice.