Indel detection from DNA and RNA sequencing data with transIndel

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Insertions and deletions (indels) are key genomic variations linked to human diseases.
• Detecting indels from DNA sequencing (DNA-seq) is common, but their RNA-level effects are challenging to study due to difficulties distinguishing them from splicing events in RNA sequencing (RNA-seq) data.

Purpose of the Study:

• To develop a splice-aware algorithm for accurate detection of medium-sized and large indels from both DNA-seq and RNA-seq data.
• To explore the transcriptional consequences of indels and identify novel RNA splicing events.

Main Methods:

• Developed transIndel, a splice-aware algorithm that reconstructs indels by analyzing chimeric alignments and linear alignments of split reads.
• Validated transIndel's performance against eight state-of-the-art indel detection tools using synthetic and real DNA-seq data.
• Applied transIndel to DNA-seq and RNA-seq datasets from prostate cancer patients.

Main Results:

• TransIndel demonstrated competitive or superior performance compared to existing indel detection tools.
• Application to prostate cancer data identified recurrent FOXA1 indels and exitron splicing events in disease-related genes.
• Enhanced the understanding of DNA- and RNA-level alterations in prostate cancer.

Conclusions:

• TransIndel is a robust tool for identifying medium- and large-sized indels from both DNA-seq and RNA-seq.
• Integrating RNA-seq data significantly improves the sensitivity of indel detection, capturing events missed by DNA-seq alone.
• The study highlights the utility of RNA-seq in discovering indels and uncovering non-canonical splicing events relevant to disease pathology.