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VirVarSeq: a low-frequency virus variant detection pipeline for Illumina sequencing using adaptive base-calling
Bie M P Verbist1, Kim Thys1, Joke Reumers1
1Department of Mathematical Modeling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, 9000 Gent, Janssen R&D, Janssen Pharmaceutical Companies of Johnson & Johnson, Turnhoutseweg 30, 2340 Beerse, Applied Mathematics, Informatics and Statistics, Ghent University, Krijgslaan 281 S9, 9000 Gent, Belgium and University of Wollongong, National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, NSW 2522, Australia.
A new tool, Q-cpileup within the VirVarSeq pipeline, improves viral variant calling from sequencing data. It enhances accuracy by using quality scores to reduce errors, aiding in resistance pathway studies for better HIV and HCV treatments.
Area of Science:
- Virology
- Bioinformatics
- Genomics
Background:
- Massively parallel sequencing (MPS) in virology aids in studying viral quasi-species, crucial for understanding drug resistance in HIV-1 and HCV infections.
- MPS platforms, while powerful, generate technical noise, particularly single base substitutions in Illumina sequencing, hindering low-frequency mutation detection.
- Nucleotide quality scores (Qs) offer a means to distinguish true low-frequency mutations from sequencing errors.
Purpose of the Study:
- To develop and evaluate a variant calling tool that leverages nucleotide quality scores to enhance the specificity of viral mutation detection.
- To integrate this tool into an open-source pipeline for comprehensive viral variant analysis.
- To enable accurate identification of low-frequency mutations for improved understanding of antiviral drug resistance.
Main Methods:
- Development of Q-cpileup, a variant calling tool employing a filtering strategy based on nucleotide quality scores.
- Integration of Q-cpileup into the open-source VirVarSeq pipeline, enabling variant calling from fastq files.
- Validation using plasmid mixtures and clinical samples, comparing Q-cpileup with existing single-nucleotide polymorphism (SNP) caller tools.
Main Results:
- Q-cpileup effectively reduces false-positive findings in viral variant calling.
- The adaptive filtering strategy optimizes thresholds for individual samples, improving accuracy across sequencing runs.
- Calling variants at the codon level with Q-cpileup demonstrates outstanding sensitivity (down to 0.5% variant frequency) and good specificity, maintaining linkage information for biological interpretation.
Conclusions:
- The VirVarSeq pipeline, featuring Q-cpileup, significantly improves the accuracy and sensitivity of viral variant calling from MPS data.
- This tool facilitates a more precise understanding of viral quasi-species and drug resistance pathways.
- The codon-level variant calling provides immediate biological relevance for antiviral drug response assessment.
