Evaluation of different analysis pipelines for the detection of HIV-1 minority resistant variants

Marine Perrier, Nathalie Désiré, Alexandre Storto, Eve Todesco, Christophe Rodriguez, Mélanie Bertine, Quentin Le Hingrat, Benoit Visseaux, Vincent Calvez, Diane Descamps, Anne-Geneviève Marcelin and Charlotte Charpentier

PLOS ONE, 2018, vol. 13, issue 6, 1-13

Abstract: Objective: Reliable detection of HIV minority resistant variants (MRVs) requires bioinformatics analysis with specific algorithms to obtain good quality alignments. The aim of this study was to analyze ultra-deep sequencing (UDS) data using different analysis pipelines. Methods: HIV-1 protease, reverse transcriptase (RT) and integrase sequences from antiretroviral-naïve patients were obtained using GS-Junior® (Roche) and MiSeq® (Illumina) platforms. MRVs were defined as variants harbouring resistance-mutation present at a frequency of 1%–20%. Reads were analyzed using different alignment algorithms: Amplicon Variant Analyzer®, Geneious® compared to SmartGene® NGS HIV-1 module. Results: 101 protease and 51 RT MRVs identified in 139 protease and 124 RT sequences generated with a GS-Junior® platform were analyzed using AVA® and SmartGene® software. The correlation coefficients for the MRVs were R2 = 0.974 for protease and R2 = 0.972 for RT. Discordances (n = 13 in protease and n = 15 in RT) mainly concerned low-level MRVs (i.e., with frequencies of 1%–2%, n = 18/28) and they were located in homopolymeric regions (n = 10/15). Geneious® and SmartGene® software were used to analyze 143 protease, 45 RT and 26 integrase MRVs identified in 172 protease, 69 RT, and 72 integrase sequences generated with a MiSeq® platform. The correlation coefficients for the MRVs were R2 = 0.987 for protease, R2 = 0.995 for RT and R2 = 0.993 for integrase. Discordances (n = 9 in protease, n = 3 in RT, and n = 3 in integrase) mainly concerned low-level MRVs (n = 13/15). Conclusion: We found an excellent correlation between the various UDS analysis pipelines that we tested. However, our results indicate that specific attention should be paid to low-level MRVs, for which the use of two different analysis pipelines and visual inspection of sequences alignments might be beneficial. Thus, our results argue for use of a 2% threshold for MRV detection, rather than the 1% threshold, to minimize misalignments and time-consuming sight reading steps essential to ensure accurate results for MRV frequencies below 2%.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0198334

DOI: 10.1371/journal.pone.0198334

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