ArticleFrontiers in genetics2022
Combinatorial and Machine Learning Approaches for Improved Somatic Variant Calling From Formalin-Fixed Paraffin-Embedded Genome Sequence Data.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Whole-Exome Sequencing Identifies Candidate Genomic Features Associated with Response to Platinum-Based Chemotherapy and Ixabepilone-Based Treatment in Ovarian Cancer.International journal of molecular sciences · 2026Article
- FFixR: a machine learning framework for accurate somatic mutation calling from FFPE RNA-seq data in cancer.Bioinformatics (Oxford, England) · 2026Article
- Enabling whole genome sequencing analysis from FFPE specimens in clinical oncology.Nature communications · 2025Article
- Article
- Statistical and Evolutionary Analysis of Sequenced DNA from Breast Cancer FFPE Specimens.bioRxiv : the preprint server for biology · 2025Article
- Personalizing DNA Cancer Vaccines.Journal of personalized medicine · 2025Review
- Fast and efficient method for parallel construction of targeted exome and methylome single-stranded DNA sequencing libraries.Scientific reports · 2025Article
- Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens.Journal of biomedical science · 2024Review
- DEEPOMICS FFPE, a deep neural network model, identifies DNA sequencing artifacts from formalin fixed paraffin embedded tissue with high accuracy.Scientific reports · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Formalin fixation of paraffin-embedded tissue samples is a well-established method for preserving tissue and is routinely used in clinical settings. Although formalin-fixed, paraffin-embedded (FFPE) tissues are deemed crucial for research and clinical applications, the fixation process results in molecular damage to nucleic acids, thus confounding their use in genome sequence analysis. Methods to improve genomic data quality from FFPE tissues have emerged, but there remains significant room for improvement. Here, we use whole-genome sequencing (WGS) data from matched Fresh Frozen (FF) and FFPE tissue samples to optimize a sensitive and precise FFPE single nucleotide variant (SNV) calling approach. We present methods to reduce the prevalence of false-positive SNVs by applying combinatorial techniques to five publicly available variant callers. We also introduce FFPolish, a novel variant classification method that efficiently classifies FFPE-specific false-positive variants. Our combinatorial and statistical techniques improve precision and F1 scores compared to the results of publicly available tools when tested individually.
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