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Related Experiment Video

Updated: Aug 23, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Excerno: Using Mutational Signatures in Sequencing Data to Filter False Variants Caused by Clinical Archival.

Audrey Mitchell1, Marco Ruiz1, Soua Yang1

  • 1Department of Mathematics, Statistics and Computer Science, St Olaf College, Northfield, Minnesota, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|November 2, 2022
PubMed
Summary

Excerno accurately identifies and removes artificial C>T mutations caused by formalin-fixation paraffin-embedding (FFPE) in cancer genomics. This R package improves variant detection in sequencing data from preserved pathology slides.

Keywords:
FFPENGSmutational signatures

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Accurate point mutation detection is crucial for cancer genomics and precision oncology.
  • Formalin-fixation paraffin-embedding (FFPE) is standard for tissue preservation but introduces artificial C>T mutations in sequencing data.
  • These FFPE-induced artifacts can confound variant analysis.

Purpose of the Study:

  • To develop and evaluate a computational method, excerno, for scoring and filtering FFPE-induced spurious variants.
  • To implement excerno as an R package for practical application in cancer genomics.

Main Methods:

  • Developed excerno based on the FFPE mutational signature and Bayes' formula to calculate the probability of FFPE artifact.
  • Simulated mutations across 60 baseline mutational signatures combined with FFPE signatures to test excerno's performance.
  • Assessed sensitivity and specificity based on cosine similarity and percentage of FFPE mutations.

Main Results:

  • Excerno's sensitivity and specificity are influenced by the similarity between baseline and FFPE signatures.
  • Increased percentages of FFPE mutations enhance sensitivity but reduce specificity.
  • Performance metrics were predictable using a linear model with interaction terms.

Conclusions:

  • The excerno R package effectively annotates and filters FFPE-induced mutations in cancer genomics.
  • The method demonstrates concordant trends in real RNA sequencing cancer samples.
  • Excerno provides a valuable tool for improving variant accuracy in FFPE-preserved samples.