Indel sensitive and comprehensive variant/mutation detection from RNA sequencing data for precision medicine.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• RNA sequencing (RNA-seq) is a primary tool for gene expression analysis and detecting structural variants like SNVs, Indels, and fusion transcripts.
• Detecting these variants from RNA-seq data presents significant challenges.
• A novel analytical pipeline is presented to address these complexities for translational precision medicine.

Purpose of the Study:

• To develop and evaluate a sensitive and accurate analytical pipeline for simultaneous detection of multiple mutation types from RNA-seq data.
• To enable comprehensive mutation profiling for applications in precision medicine.

Main Methods:

• The pipeline integrates sensitive aligners for Indels, best practices for RNA-seq data preprocessing and variant calling, and STAR-fusion for chimeric transcript detection.
• Variant annotation and extraction of key genes for clinical action are incorporated.
• Performance was evaluated using three datasets, including well-characterized variants and cancer-specific mutations.

Main Results:

• High sensitivities achieved for SNVs (~95%) and Indels (~80%) compared to reference data.
• The pipeline successfully detected known oncogenic mutations in a lung cancer dataset, with GSNAP aligner identifying all.
• Actionable fusions (EML4-ALK) and spiked-in fusion transcripts were detected, demonstrating pipeline efficacy across various concentrations.

Conclusions:

• The developed RNA-seq workflow provides accurate and comprehensive mutation profiling.
• Reliable detection of key and actionable mutations from RNA-seq data supports its use in personalized medicine.
• This approach offers a practical alternative for identifying clinically relevant genetic alterations.