Performance comparison of next generation sequencing analysis pipelines for HIV-1 drug resistance testing.
Emma R LeeNeil ParkinCheryl JenningsChanson J BrummeEric EnnsMaria CasadellàMark HowisonMia CoetzerSantiago Avila-RiosRupert CapinaEric MarinierGary Van DomselaarMarc Noguera-JulianDon KirkbyJeff KnaggsRichard HarriganMiguel Quiñones-MateuRoger ParedesRami KantorPaul SandstromHezhao JiPublished in: Scientific reports (2020)
Next generation sequencing (NGS) is a trending new standard for genotypic HIV-1 drug resistance (HIVDR) testing. Many NGS HIVDR data analysis pipelines have been independently developed, each with variable outputs and data management protocols. Standardization of such analytical methods and comparison of available pipelines are lacking, yet may impact subsequent HIVDR interpretation and other downstream applications. Here we compared the performance of five NGS HIVDR pipelines using proficiency panel samples from NIAID Virology Quality Assurance (VQA) program. Ten VQA panel specimens were genotyped by each of six international laboratories using their own in-house NGS assays. Raw NGS data were then processed using each of the five different pipelines including HyDRA, MiCall, PASeq, Hivmmer and DEEPGEN. All pipelines detected amino acid variants (AAVs) at full range of frequencies (1~100%) and demonstrated good linearity as compared to the reference frequency values. While the sensitivity in detecting low abundance AAVs, with frequencies between 1~20%, is less a concern for all pipelines, their specificity dramatically decreased at AAV frequencies <2%, suggesting that 2% threshold may be a more reliable reporting threshold for ensured specificity in AAV calling and reporting. More variations were observed among the pipelines when low abundance AAVs are concerned, likely due to differences in their NGS read quality control strategies. Findings from this study highlight the need for standardized strategies for NGS HIVDR data analysis, especially for the detection of minority HIVDR variants.
Keyphrases
- data analysis
- copy number
- antiretroviral therapy
- quality control
- hiv positive
- human immunodeficiency virus
- hiv infected
- hepatitis c virus
- amino acid
- hiv testing
- electronic health record
- hiv aids
- gene expression
- adverse drug
- machine learning
- single cell
- gene therapy
- real time pcr
- sensitive detection
- artificial intelligence
- deep learning