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Updated: Sep 4, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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RNA-SSNV: A Reliable Somatic Single Nucleotide Variant Identification Framework for Bulk RNA-Seq Data.

Qihan Long1,2,3, Yangyang Yuan1,2,3, Miaoxin Li1,2,3,4,5

  • 1Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China.

Frontiers in Genetics
|July 18, 2022
PubMed
Summary

We developed RNA-SSNV, a new tool to accurately identify expressed somatic mutations from RNA sequencing data. This method improves cancer driver gene discovery and analysis of carcinogenic mechanisms.

Keywords:
RNARNA-SSNVRNA-Seqcancermachine learningsomatic mutation

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Area of Science:

  • Genomics
  • Cancer Research
  • Bioinformatics

Background:

  • Expressed somatic mutations are key for identifying active cancer drivers.
  • Accurately calling mutations from RNA sequencing (RNA-seq) data is challenging due to RNA editing, reverse transcription errors, and alignment gaps.

Purpose of the Study:

  • To introduce RNA-SSNV, a novel framework for accurately calling somatic single nucleotide variants (SSNVs) from tumor bulk RNA-seq data.
  • To enhance the identification of functional cancer driver mutations and understand carcinogenic mechanisms.

Main Methods:

  • Developed a framework named RNA-SSNV.
  • Implemented a comprehensive multi-filtering strategy.
  • Utilized a machine-learning classification model trained on curated features.

Main Results:

  • RNA-SSNV achieved high precision-recall rates (0.880-0.884) in testing and maintained a 0.94 AUC in TCGA datasets.
  • Somatic mutations identified by RNA-SSNV showed higher functional impact and therapeutic potential in known driver genes.
  • Variant allele fraction analysis indicated evolutionary selection advantage for subclonal mutations and RNA's power in detecting DNA-omitted mutations.

Conclusions:

  • RNA-SSNV provides a robust approach for accurate expressed somatic mutation calling.
  • The tool facilitates more insightful analysis of cancer driver genes and carcinogenic pathways.
  • RNA-SSNV aids in uncovering mutations missed by DNA-based methods.