Laboratory for Clinical and Epidemiological Virology, Rega Institute for Medical Research, Department of Microbiology and Immunology, University of Leuven, Belgium; Clinical and Molecular Infectious Diseases Group, Faculty of Sciences and Mathematics, Universidad del Rosario, Bogotá, Colombia; MyBioData, Rotselaar, Belgium; Centro de Malária e Outras Doenças Tropicais and Unidade de Microbiologia, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisboa, Portugal; Laboratory de Biologia Molecular, Centro Hospitalar de Lisboa Ocidental Lisboa, Portugal; Africa Centre for Health and Population Studies, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, South Africa; Minderbroedersstraat 10, B-3000 Leuven, Belgium; Calle 63D No. 24-31, Bogotá, Colombia; Beatrijslaan 93, 3110 Rotselaar, Belgium; Rua da Junqueira No. 100, Lisboa, Portugal; Rua da Junqueira No. 126, Lisboa, Portugal; PO Box 198, Mtubatuba 3935, South Africa
Pineda-Peña, A.-C., Laboratory for Clinical and Epidemiological Virology, Rega Institute for Medical Research, Department of Microbiology and Immunology, University of Leuven, Belgium, Clinical and Molecular Infectious Diseases Group, Faculty of Sciences and Mathematics, Universidad del Rosario, Bogotá, Colombia, Minderbroedersstraat 10, B-3000 Leuven, Belgium, Calle 63D No. 24-31, Bogotá, Colombia; Faria, N.R., Laboratory for Clinical and Epidemiological Virology, Rega Institute for Medical Research, Department of Microbiology and Immunology, University of Leuven, Belgium, Minderbroedersstraat 10, B-3000 Leuven, Belgium; Imbrechts, S., Laboratory for Clinical and Epidemiological Virology, Rega Institute for Medical Research, Department of Microbiology and Immunology, University of Leuven, Belgium, Minderbroedersstraat 10, B-3000 Leuven, Belgium; Libin, P., Laboratory for Clinical and Epidemiological Virology, Rega Institute for Medical Research, Department of Microbiology and Immunology, University of Leuven, Belgium, MyBioData, Rotselaar, Belgium, Minderbroedersstraat 10, B-3000 Leuven, Belgium, Beatrijslaan 93, 3110 Rotselaar, Belgium; Abecasis, A.B., Centro de Malária e Outras Doenças Tropicais and Unidade de Microbiologia, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisboa, Portugal, Rua da Junqueira No. 100, Lisboa, Portugal; Deforche, K., MyBioData, Rotselaar, Belgium, Beatrijslaan 93, 3110 Rotselaar, Belgium; Gómez-López, A., Clinical and Molecular Infectious Diseases Group, Faculty of Sciences and Mathematics, Universidad del Rosario, Bogotá, Colombia, Calle 63D No. 24-31, Bogotá, Colombia; Camacho, R.J., Centro de Malária e Outras Doenças Tropicais and Unidade de Microbiologia, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisboa, Portugal, Laboratory de Biologia Molecular, Centro Hospitalar de Lisboa Ocidental Lisboa, Portugal, Rua da Junqueira No. 100, Lisboa, Portugal, Rua da Junqueira No. 126, Lisboa, Portugal; De Oliveira, T., Africa Centre for Health and Population Studies, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, South Africa, PO Box 198, Mtubatuba 3935, South Africa; Vandamme, A.-M., Laboratory for Clinical and Epidemiological Virology, Rega Institute for Medical Research, Department of Microbiology and Immunology, University of Leuven, Belgium, Centro de Malária e Outras Doenças Tropicais and Unidade de Microbiologia, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisboa, Portugal
Background: To investigate differences in pathogenesis, diagnosis and resistance pathways between HIV-1 subtypes, an accurate subtyping tool for large datasets is needed. We aimed to evaluate the performance of automated subtyping tools to classify the different subtypes and circulating recombinant forms using pol, the most sequenced region in clinical practice. We also present the upgraded version 3 of the Rega HIV subtyping tool (REGAv3). Methodology: HIV-1 pol sequences (PR. +. RT) for 4674 patients retrieved from the Portuguese HIV Drug Resistance Database, and 1872 pol sequences trimmed from full-length genomes retrieved from the Los Alamos database were classified with statistical-based tools such as COMET, jpHMM and STAR; similarity-based tools such as NCBI and Stanford; and phylogenetic-based tools such as REGA version 2 (REGAv2), REGAv3, and SCUEAL. The performance of these tools, for pol, and for PR and RT separately, was compared in terms of reproducibility, sensitivity and specificity with respect to the gold standard which was manual phylogenetic analysis of the pol region. Results: The sensitivity and specificity for subtypes B and C was more than 96% for seven tools, but was variable for other subtypes such as A, D, F and G. With regard to the most common circulating recombinant forms (CRFs), the sensitivity and specificity for CRF01_AE was ~99% with statistical-based tools, with phylogenetic-based tools and with Stanford, one of the similarity based tools. CRF02_AG was correctly identified for more than 96% by COMET, REGAv3, Stanford and STAR. All the tools reached a specificity of more than 97% for most of the subtypes and the two main CRFs (CRF01_AE and CRF02_AG). Other CRFs were identified only by COMET, REGAv2, REGAv3, and SCUEAL and with variable sensitivity. When analyzing sequences for PR and RT separately, the performance for PR was generally lower and variable between the tools. Similarity and statistical-based tools were 100% reproducible, but this was lower for phylogenetic-based tools such as REGA (~99%) and SCUEAL (~96%). Conclusions: REGAv3 had an improved performance for subtype B and CRF02_AG compared to REGAv2 and is now able to also identify all epidemiologically relevant CRFs. In general the best performing tools, in alphabetical order, were COMET, jpHMM, REGAv3, and SCUEAL when analyzing pure subtypes in the pol region, and COMET and REGAv3 when analyzing most of the CRFs. Based on this study, we recommend to confirm subtyping with 2 well performing tools, and be cautious with the interpretation of short sequences. © 2013 The Authors.