BC Centre for Excellence in HIV/AIDS, Vancouver, British Columbia, Canada.
J Clin Microbiol. 2012 Jun;50(6):1936-42. doi: 10.1128/JCM.06689-11. Epub 2012 Mar 7.
Genotypic HIV drug resistance testing is routinely used to guide clinical decisions. While genotyping methods can be standardized, a slow, labor-intensive, and subjective manual sequence interpretation step is required. We therefore performed external validation of our custom software RECall, a fully automated sequence analysis pipeline. HIV-1 drug resistance genotyping was performed on 981 clinical samples at the Stanford Diagnostic Virology Laboratory. Sequencing trace files were first interpreted manually by a laboratory technician and subsequently reanalyzed by RECall, without intervention. The relative performances of the two methods were assessed by determination of the concordance of nucleotide base calls, identification of key resistance-associated substitutions, and HIV drug resistance susceptibility scoring by the Stanford Sierra algorithm. RECall is freely available at http://pssm.cfenet.ubc.ca. In total, 875 of 981 sequences were analyzed by both human and RECall interpretation. RECall analysis required minimal hands-on time and resulted in a 25-fold improvement in processing speed (∼150 technician-hours versus ∼6 computation-hours). Excellent concordance was obtained between human and automated RECall interpretation (99.7% agreement for >1,000,000 bases compared). Nearly all discordances (99.4%) were due to nucleotide mixtures being called by one method but not the other. Similarly, 98.6% of key antiretroviral resistance-associated mutations observed were identified by both methods, resulting in 98.5% concordance of resistance susceptibility interpretations. This automated sequence analysis tool provides both standardization of analysis and a significant improvement in data workflow. The time-consuming, error-prone, and dreadfully boring manual sequence analysis step is replaced with a fully automated system without compromising the accuracy of reported HIV drug resistance data.
基因 HIV 耐药性检测通常用于指导临床决策。虽然基因分型方法可以标准化，但需要进行缓慢、劳动密集型且主观的手动序列解释步骤。因此，我们对我们的定制软件 RECall 进行了外部验证，这是一个完全自动化的序列分析管道。在斯坦福诊断病毒学实验室对 981 例临床样本进行了 HIV-1 耐药基因分型。首先由实验室技术员手动解释测序迹线文件，然后由 RECall 进行重新分析，无需干预。通过确定核苷酸碱基调用的一致性、鉴定关键耐药相关替换以及斯坦福塞拉算法对 HIV 药物耐药性敏感性评分，评估了两种方法的相对性能。RECall 可在 http://pssm.cfenet.ubc.ca 免费获得。总共，981 个序列中有 875 个被人类和 RECall 解释分析。RECall 分析所需的人工干预最少，处理速度提高了 25 倍（约 150 个技术人员小时对约 6 个计算小时）。人类和自动 RECall 解释之间获得了极好的一致性（比较 100 多万个碱基时，99.7%的一致性）。几乎所有的不一致（99.4%）是由于一种方法调用核苷酸混合物而另一种方法不调用。同样，两种方法都识别出了 98.6%的关键抗逆转录病毒耐药相关突变，导致耐药敏感性解释的一致性为 98.5%。这种自动化序列分析工具既提供了分析的标准化，又显著改善了数据工作流程。耗时、易错且枯燥乏味的手动序列分析步骤被一个完全自动化的系统所取代，而不会影响报告的 HIV 药物耐药数据的准确性。
