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

Overview of attention for article published in BMC Bioinformatics, June 2015
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Title
Error correction and statistical analyses for intra-host comparisons of feline immunodeficiency virus diversity from high-throughput sequencing data
Published in
BMC Bioinformatics, June 2015
DOI 10.1186/s12859-015-0607-z
Pubmed ID
Authors

Yang Liu, Francesca Chiaromonte, Howard Ross, Raunaq Malhotra, Daniel Elleder, Mary Poss

Abstract

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. 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. 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.

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Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 4%
Unknown 22 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 30%
Researcher 3 13%
Student > Bachelor 2 9%
Unspecified 2 9%
Student > Doctoral Student 2 9%
Other 5 22%
Unknown 2 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 30%
Biochemistry, Genetics and Molecular Biology 5 22%
Unspecified 4 17%
Computer Science 3 13%
Immunology and Microbiology 2 9%
Other 0 0%
Unknown 2 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 July 2015.
All research outputs
#5,151,813
of 7,185,636 outputs
Outputs from BMC Bioinformatics
#2,634
of 3,300 outputs
Outputs of similar age
#141,150
of 224,201 outputs
Outputs of similar age from BMC Bioinformatics
#89
of 106 outputs
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