Huber Michael, Metzner Karin J, Geissberger Fabienne D, Shah Cyril, Leemann Christine, Klimkait Thomas, Böni Jürg, Trkola Alexandra, Zagordi Osvaldo
Institute of Medical Virology, University of Zurich, Zurich, Switzerland.
Institute of Medical Virology, University of Zurich, Zurich, Switzerland; Division of Infectious Diseases and Hospital Epidemiology, University of Zurich, Zurich, Switzerland.
J Virol Methods. 2017 Feb;240:7-13. doi: 10.1016/j.jviromet.2016.11.008. Epub 2016 Nov 17.
Genotypic monitoring of drug-resistance mutations (DRMs) in HIV-1 infected individuals is strongly recommended to guide selection of the initial antiretroviral therapy (ART) and changes of drug regimens. Traditionally, mutations conferring drug resistance are detected by population sequencing of the reverse transcribed viral RNA encoding the HIV-1 enzymes target by ART, followed by manual analysis and interpretation of Sanger sequencing traces. This process is labor intensive, relies on subjective interpretation from the operator, and offers limited sensitivity as only mutations above 20% frequency can be reliably detected. Here we present MinVar, a pipeline for the analysis of deep sequencing data, which allows reliable and automated detection of DRMs down to 5%. We evaluated MinVar with data from amplicon sequencing of defined mixtures of molecular virus clones with known DRM and plasma samples of viremic HIV-1 infected individuals and we compared it to VirVarSeq, another virus variant detection tool exclusively working on Illumina deep sequencing data. MinVar was designed to be compatible with a diverse range of sequencing platforms and allows the detection of DRMs and insertions/deletions from deep sequencing data without the need to perform additional bioinformatics analysis, a prerequisite to a widespread implementation of HIV-1 genotyping using deep sequencing in routine diagnostic settings.
强烈建议对HIV-1感染者的耐药性突变（DRM）进行基因分型监测，以指导初始抗逆转录病毒疗法（ART）的选择和药物方案的调整。传统上，通过对编码ART靶向的HIV-1酶的逆转录病毒RNA进行群体测序来检测赋予耐药性的突变，然后由操作人员对手动分析和解读Sanger测序结果。这个过程劳动强度大，依赖于操作人员的主观解读，并且灵敏度有限，因为只有频率高于20%的突变才能被可靠检测到。在此，我们展示了MinVar，一种用于深度测序数据分析的流程，它能够可靠且自动地检测低至5%的DRM。我们用来自具有已知DRM的分子病毒克隆定义混合物的扩增子测序数据以及病毒血症HIV-1感染者的血浆样本对MinVar进行了评估，并将其与VirVarSeq（另一种专门用于Illumina深度测序数据的病毒变体检测工具）进行了比较。MinVar的设计与多种测序平台兼容，能够从深度测序数据中检测DRM和插入/缺失，而无需进行额外的生物信息学分析，这是在常规诊断环境中使用深度测序广泛开展HIV-1基因分型的一个先决条件。
