ViVaMBC: estimating viral sequence variation in complex populations from illumina deep-sequencing data using model-based clustering

BMC Bioinformatics

Bie Verbist³  Lieven Clement²  Joke Reumers⁴  Kim Thys⁴  Alexander Vapirev¹  Willem Talloen⁴  Yves Wetzels⁴  Joris Meys³  Jeroen Aerssens⁴  Luc Bijnens⁴  Olivier Thas⁵ 

[1] ExaScience Life Lab, Kapeldreef 75, Leuven 3001, Belgium

[2] Department of Applied Mathematics, Informatics and Statistics, Ghent University, Krijgslaan 281 S9, Gent 9000, Belgium

[3] Department of Mathematical Modeling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, Gent 9000, Belgium

[4] Janssen R&D, Janssen Pharmaceutical Companies of J&J, Turnhoutseweg 30, Beerse 2340, Belgium

[5] University of Wollongong, National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, NSW 2522, Australia

关键词: Viral quasispecies;    Model-based clustering;    Second best base call;    Codon;    Illumina sequencing;   

Others  :  1160562
DOI  :  10.1186/s12859-015-0458-7

 received in 2014-07-01, accepted in 2014-12-16,  发布年份 2015

【 摘 要 】

Background

Deep-sequencing allows for an in-depth characterization of sequence variation in complex populations. However, technology associated errors may impede a powerful assessment of low-frequency mutations. Fortunately, base calls are complemented with quality scores which are derived from a quadruplet of intensities, one channel for each nucleotide type for Illumina sequencing. The highest intensity of the four channels determines the base that is called. Mismatch bases can often be corrected by the second best base, i.e. the base with the second highest intensity in the quadruplet. A virus variant model-based clustering method, ViVaMBC, is presented that explores quality scores and second best base calls for identifying and quantifying viral variants. ViVaMBC is optimized to call variants at the codon level (nucleotide triplets) which enables immediate biological interpretation of the variants with respect to their antiviral drug responses.

Results

Using mixtures of HCV plasmids we show that our method accurately estimates frequencies down to 0.5%. The estimates are unbiased when average coverages of 25,000 are reached. A comparison with the SNP-callers V-Phaser2, ShoRAH, and LoFreq shows that ViVaMBC has a superb sensitivity and specificity for variants with frequencies above 0.4%. Unlike the competitors, ViVaMBC reports a higher number of false-positive findings with frequencies below 0.4% which might partially originate from picking up artificial variants introduced by errors in the sample and library preparation step.

Conclusions

ViVaMBC is the first method to call viral variants directly at the codon level. The strength of the approach lies in modeling the error probabilities based on the quality scores. Although the use of second best base calls appeared very promising in our data exploration phase, their utility was limited. They provided a slight increase in sensitivity, which however does not warrant the additional computational cost of running the offline base caller. Apparently a lot of information is already contained in the quality scores enabling the model based clustering procedure to adjust the majority of the sequencing errors. Overall the sensitivity of ViVaMBC is such that technical constraints like PCR errors start to form the bottleneck for low frequency variant detection.

【 授权许可】

   
2015 Verbist et al.; licensee BioMed Central.

【 图 表 】

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