Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis

Area of Science:

• Genetics
• Molecular Biology
• Bioinformatics

Background:

• Genomic variants impacting pre-messenger RNA splicing are implicated in numerous rare genetic disorders.
• Current genetic diagnostic and variant interpretation methods often fail to identify splice-altering variants, contributing to a diagnostic gap.
• There is a growing need for advanced methods to detect and interpret pathogenic splicing variants for improved patient care and research.

Approach:

• This review summarizes recent advancements and challenges in utilizing RNA sequencing (RNA-Seq) technologies for investigating rare genetic diseases.
• It discusses the role of computational splicing prediction tools as complementary methods to identify disease-causing variants associated with splicing defects.
• The review highlights the synergy between experimental and computational approaches in understanding splicing in rare diseases.

Key Points:

• RNA sequencing offers powerful insights into gene expression and splicing patterns relevant to rare diseases.
• Computational tools are increasingly vital for predicting the functional impact of splicing variants, especially those missed by traditional analyses.
• Integrating RNA-Seq data with predictive modeling enhances the detection of pathogenic splicing variants.

Conclusions:

• Continued progress in sequencing technologies and predictive modeling will deepen our understanding of splicing regulation.
• These advancements are crucial for bridging the diagnostic gap and improving outcomes for patients with rare genetic diseases.
• A comprehensive approach combining RNA sequencing and computational analysis is essential for accurate diagnosis and research in rare genetic disorders.