What question did this study set out to answer?

This research aims to develop and benchmark Vardetector for detecting DNA-called mutations from RNA reads.

June 29, 2026Open Access

Vardetector: a pure Python package to detect DNA called mutations in aligned RNA reads

JŠJulij ŠelbUniversity of Ljubljana LDLuka DejanovićUniversity Clinic of Pulmonary and Allergic Diseases Golnik KMKatja MohorčičUniversity Clinic of Pulmonary and Allergic Diseases Golnik

Key Points

This research aims to develop and benchmark Vardetector for detecting DNA-called mutations from RNA reads.
Developed Vardetector, a Python package for detecting mutations in RNA-seq data.
Compared performance with GATK HaplotypeCaller using RNA-seq from FFPE tumor samples.
Evaluated two library preparation protocols: exome-capture and ribodepletion.
Ribodepletion protocol yielded significantly higher all-reads and supporting-reads (p < 0.001).
Ribodepletion also provided a wider breadth of somatic mutation position coverage (p < 0.001).
Comparison showed Vardetector has potential to enhance neoantigen prioritization.

Abstract

We develop a freely-available Python package Vardetector ( https://github.com/julijselb/vardetector/tree/main/vardetector ) used for detecting DNA called mutations in aligned RNA reads. We benchmark it by comparing it to industry standard variant caller (GATK HaplotypeCaller; r = 0.88896/0.88859 (supporting-reads/all-reads)) and demonstrate the functionality by comparing two RNA-seq library preparation protocols for formalin fixed paraffin embedded (FFPE) tumor samples. One protocol relies on exome-capture and the other on ribosome-depletion (ribodepletion) chemistry. We call somatic mutations from DNA of tumor/normal samples of two individuals with non-small cell lung cancer and test the difference between the two protocols by quantifying all RNA reads (all-reads) and somatic mutation supporting RNA reads (supporting-reads) over the positions of the DNA-called mutations. We show that the ribodepletion protocol produces significantly higher number of all ( p < 0.001) and of supporting ( p < 0.001) reads over the mutations of interest. Moreover, the ribodepletion protocol produces significantly ( p < 0.001) wider breath of somatic mutation position coverage. The Vardetector software package and our results display a meaningful potential of the approach to improve neoantigen prioritisation pipelines.

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