VirVarSeq: a low frequency Virus Variant detection pipeline for Illumina Sequencing using adaptive base-calling accuracy filtering. Bioinformatics. 2014

MOTIVATION:

In virology, massively parallel sequencing (MPS) opens many opportunities for studying viral quasi-species, e.g. in HIV-1 and HCV-infected patients. This is essential for understanding pathways to resistance, which can substantially improve treatment. Although MPS platforms allow in-depth characterization of sequence variation, their measurements still involve substantial technical noise. For Illumina sequencing, single base substitutions are the main error source and impede powerful assessment of low-frequency mutations. Fortunately, base calls are complemented with quality scores that are useful for differentiating errors from the real low-frequency mutations.

RESULTS:

A variant calling tool, Q-cpileup, is proposed, which exploits the quality scores of nucleotides in a filtering strategy to increase specificity. The tool is imbedded in an open source pipeline, VirVarSeq, which allows variant calling starting from fastq files. Using both plasmid mixtures and clinical samples we show that Q-cpileup is able to reduce the number of false positives. The filtering strategy is adaptive and provides an optimized threshold for individual samples in each sequencing run. Additionally, linkage information is kept between single nucleotide polymorphisms as variants are called at codon level. This enables virologists to have an immediate biological interpretation of the reported variants with respect to their anti-viral drug responses. A comparison with existing SNP-callers tools reveals that calling variants at codon level with Q-cpileup results in an outstanding sensitivity while maintaining a good specificity for variants with frequencies down to 0.5%. Availability: The VirVarSeq is available, together with a users guide and test data at sourceforge: http://sourceforge.net/projects/virtools/?source=directory CONTACT: bie.verbist@ugent.be.

Source: www.ncbi.nlm.nih.gov

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