Error correction and statistical analyses for intra-host comparisons of feline immunodeficiency virus diversity from high-throughput sequencing data

BMC Bioinformatics

Yang Liu⁴  Francesca Chiaromonte⁴  Howard Ross²  Raunaq Malhotra³  Daniel Elleder¹  Mary Poss⁴ 

[1] Current address: Institute of Molecular Genetics, Academy of Sciences of the Czech Republic, Videnska 1083, Prague 14000, Czech Republic

[2] Bioinformatics Institute, School of Biological Sciences, University of Auckland, Auckland 1142, New Zealand

[3] Department of Computer Science and Engineering, The Pennsylvania State University, University Park 16802, PA, USA

[4] The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park 16802, PA, USA

关键词: Viral Coinfection;    Linear Mixed Model;    Error Correction;    FIV;    Next Generation Sequencing;    Virus Population Dynamics;   

Others  :  1231823
DOI  :  10.1186/s12859-015-0607-z

 received in 2014-03-06, accepted in 2015-04-29,  发布年份 2015

【 摘 要 】

Background

Infection with feline immunodeficiency virus (FIV) causes an immunosuppressive disease whose consequences are less severe if cats are co-infected with an attenuated FIV strain (PLV). We use virus diversity measurements, which reflect replication ability and the virus response to various conditions, to test whether diversity of virulent FIV in lymphoid tissues is altered in the presence of PLV. Our data consisted of the 3′ half of the FIV genome from three tissues of animals infected with FIV alone, or with FIV and PLV, sequenced by 454 technology.

Results

Since rare variants dominate virus populations, we had to carefully distinguish sequence variation from errors due to experimental protocols and sequencing. We considered an exponential-normal convolution model used for background correction of microarray data, and modified it to formulate an error correction approach for minor allele frequencies derived from high-throughput sequencing. Similar to accounting for over-dispersion in counts, this accounts for error-inflated variability in frequencies – and quite effectively reproduces empirically observed distributions. After obtaining error-corrected minor allele frequencies, we applied ANalysis Of VAriance (ANOVA) based on a linear mixed model and found that conserved sites and transition frequencies in FIV genes differ among tissues of dual and single infected cats. Furthermore, analysis of minor allele frequencies at individual FIV genome sites revealed 242 sites significantly affected by infection status (dual vs. single) or infection status by tissue interaction. All together, our results demonstrated a decrease in FIV diversity in bone marrow in the presence of PLV. Importantly, these effects were weakened or undetectable when error correction was performed with other approaches (thresholding of minor allele frequencies; probabilistic clustering of reads). We also queried the data for cytidine deaminase activity on the viral genome, which causes an asymmetric increase in G to A substitutions, but found no evidence for this host defense strategy.

Conclusions

Our error correction approach for minor allele frequencies (more sensitive and computationally efficient than other algorithms) and our statistical treatment of variation (ANOVA) were critical for effective use of high-throughput sequencing data in understanding viral diversity. We found that co-infection with PLV shifts FIV diversity from bone marrow to lymph node and spleen.

【 授权许可】

   
2015 Liu et al.

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