Related Experiment Video
Updated: Jun 25, 2026
11:02
Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
Published on: October 18, 2013
FFixR: a machine learning framework for accurate somatic mutation calling from FFPE RNA-seq data in cancer.
Or Livne1, Keren Yizhak1,2
1Department of Cell Biology and Cancer Science, The Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, 3525422, Israel.
Bioinformatics (Oxford, England)
|June 24, 2026
Summary
FFixR is a new machine learning tool that accurately removes artefacts from RNA sequencing data of formalin-fixed paraffin-embedded (FFPE) tissues. This enables reliable somatic mutation detection in archival samples without needing matched normal tissue.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Formalin-fixed paraffin-embedded (FFPE) tissues are crucial for research and clinical use.
- RNA sequencing (RNA-seq) from FFPE tissues is challenged by artefacts like cytosine deamination and strand-specific damage.
- Existing tools for DNA sequencing are unsuitable for filtering FFPE artefacts in RNA-seq data.
Purpose of the Study:
- To develop a machine learning framework, FFixR, for filtering FFPE-induced artefacts in RNA-seq data.
- To enable accurate somatic variant calling from FFPE RNA-seq without matched-normal samples.
Main Methods:
- FFixR utilizes a machine learning approach trained on FFPE melanoma samples with matched DNA.
- The framework incorporates allele-specific read counts, variant features, and mutational signature probabilities.
- FFixR was evaluated on independent cohorts to assess its performance.
Main Results:
- FFixR effectively removed up to 98% of artefactual mutations while retaining approximately 92% of true variants.
- SHAP analysis identified key features influencing the model's decision-making process.
- Application to independent cohorts restored the correlation between RNA- and DNA-derived tumor mutational burden (R2 = 0.881) and recovered meaningful mutational signatures.
Conclusions:
- FFixR provides an accurate method for somatic variant calling from FFPE RNA-seq data.
- The tool enhances the utility of archival FFPE samples for both research and clinical applications.
- FFixR is freely available, facilitating its adoption in the scientific community.
