SimFFPE and FilterFFPE: improving structural variant calling in FFPE samples. Issue 9 (22nd September 2021)

• Record Type: Journal Article
• Authors: Wei, Lanying; Dugas, Martin; Sandmann, Sarah
• Is Part Of: GigaScience. Volume 10:Issue 9(2021)
• Publication Date: 2021-09-22
• Subjects: FFPE -- next-generation sequencing -- artifact removal -- structural variant calling; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giab065
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: Background: Artifact chimeric reads are enriched in next-generation sequencing data generated from formalin-fixed paraffin-embedded (FFPE) samples. Previous work indicated that these reads are characterized by erroneous split-read support that is interpreted as evidence of structural variants. Thus, a large number of false-positive structural variants are detected. To our knowledge, no tool is currently available to specifically call or filter structural variants in FFPE samples. To overcome this gap, we developed 2 R packages: SimFFPE and FilterFFPE. Results: SimFFPE is a read simulator, specifically designed for next-generation sequencing data from FFPE samples. A mixture of characteristic artifact chimeric reads, as well as normal reads, is generated. FilterFFPE is a filtration algorithm, removing artifact chimeric reads from sequencing data while keeping real chimeric reads. To evaluate the performance of FilterFFPE, we performed structural variant calling with 3 common tools (Delly, Lumpy, and Manta) with and without prior filtration with FilterFFPE. After applying FilterFFPE, the mean positive predictive value improved from 0.27 to 0.48 in simulated samples and from 0.11 to 0.27 in real samples, while sensitivity remained basically unchanged or even slightly increased. Conclusions: FilterFFPE improves the performance of SV calling in FFPE samples. It was validated by analysis of simulated and real data.