title: High-accuracy identification of incident HIV-1 infections using a sequence clustering based diversity measure.
abstract: Accurate estimates of HIV-1 incidence are essential for monitoring epidemic trends and evaluating intervention efforts. However, the long asymptomatic stage of HIV-1 infection makes it difficult to effectively distinguish incident infections from chronic ones. Current incidence assays based on serology or viral sequence diversity are both still lacking in accuracy. In the present work, a sequence clustering based diversity (SCBD) assay was devised by utilizing the fact that viral sequences derived from each transmitted/founder (T/F) strain tend to cluster together at early stage, and that only the intra-cluster diversity is correlated with the time since HIV-1 infection. The dot-matrix pairwise alignment was used to eliminate the disproportional impact of insertion/deletions (indels) and recombination events, and so was the proportion of clusterable sequences (Pc) as an index to identify late chronic infections with declined viral genetic diversity. Tested on a dataset containing 398 incident and 163 chronic infection cases collected from the Los Alamos HIV database (last modified 2/8/2012), our SCBD method achieved 99.5% sensitivity and 98.8% specificity, with an overall accuracy of 99.3%. Further analysis and evaluation also suggested its performance was not affected by host factors such as the viral subtypes and transmission routes. The SCBD method demonstrated the potential of sequencing based techniques to become useful for identifying incident infections. Its use may be most advantageous for settings with low to moderate incidence relative to available resources. The online service is available at http://www.bioinfo.tsinghua.edu.cn:8080/SCBD/index.jsp.
authors.affiliation: The Key Laboratory of Bioinformatics, Ministry of Education, School of Life Sciences, Tsinghua University, Beijing, China.
authors.forename: Xia-Yu
authors.initials: XY
authors.lastname: Xia
authors.affiliation: The Key Laboratory of Bioinformatics, Ministry of Education, School of Life Sciences, Tsinghua University, Beijing, China.
authors.forename: Meng
authors.initials: M
authors.lastname: Ge
authors.affiliation: The State Key Laboratory for Infectious Disease Prevention and Control, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Jenny H
authors.initials: JH
authors.lastname: Hsi
authors.affiliation: The State Key Laboratory for Infectious Disease Prevention and Control, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: He
authors.affiliation: The State Key Laboratory for Infectious Disease Prevention and Control, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Yu-Hua
authors.initials: YH
authors.lastname: Ruan
authors.affiliation: The Key Laboratory of Bioinformatics, Ministry of Education, School of Life Sciences, Tsinghua University, Beijing, China.
authors.forename: Zhi-Xin
authors.initials: ZX
authors.lastname: Wang
authors.affiliation: The State Key Laboratory for Infectious Disease Prevention and Control, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Yi-Ming
authors.initials: YM
authors.lastname: Shao
authors.affiliation: The Key Laboratory of Bioinformatics, Ministry of Education, School of Life Sciences, Tsinghua University, Beijing, China.
authors.forename: Xian-Ming
authors.initials: XM
authors.lastname: Pan
mesh_terms: D015492:AIDS Serodiagnosis; D015658:HIV Infections / Q000453:epidemiology* / Q000635:transmission; D015497:HIV-1 / Q000235:genetics*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D016415:Sequence Alignment; D065288:Serogroup