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DOI 10.1186/s12859-025-06189-y

Медицинская библиотека · BMC bioinformatics · 2025

HIV-phyloTSI: subtype-independent estimation of time since HIV-1 infection for cross-sectional measures of population incidence using deep sequence data.

Golubchik Tanya, Abeler-Dörner Lucie, Hall Matthew, Wymant Chris, Bonsall David, Macintyre-Cockett George, Thomson Laura, Baeten Jared M, Celum Connie L, Galiwango Ronald M, Kosloff Barry, Limbada Mohammed, Mujugira Andrew, Mugo Nelly R, Gall Astrid, Blanquart François, Bakker Margreet, Bezemer Daniela, Ong Swee Hoe, Albert Jan, Bannert Norbert, Fellay Jacques, Gunsenheimer-Bartmeyer Barbara, Günthard Huldrych F, Kivelä Pia, Kouyos Roger D, Meyer Laurence, Porter Kholoud, van Sighem Ard, van der Valk Mark, Berkhout Ben, Kellam Paul, Cornelissen Marion, Reiss Peter, Ayles Helen, Burns David N, Fidler Sarah, Grabowski Mary Kate, Hayes Richard, Herbeck Joshua T, Kagaayi Joseph, Kaleebu Pontiano, Lingappa Jairam R, Ssemwanga Deogratius, Eshleman Susan H, Cohen Myron S, Ratmann Oliver, Laeyendecker Oliver, Fraser Christophe, HPTN 071 (PopART) Phylogenetics protocol team, the BEEHIVE consortium and the PANGEA consortium

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Аннотация

BACKGROUND

Estimating the time since HIV infection (TSI) at population level is essential for tracking changes in the global HIV epidemic. Most methods for determining TSI give a binary classification of infections as recent or non-recent within a window of several months, and cannot assess the cumulative impact of an intervention.

RESULTS

We developed a Random Forest Regression model, HIV-phyloTSI, which combines measures of within-host diversity and divergence to generate continuous TSI estimates directly from viral deep-sequencing data, with no need for additional variables. HIV-phyloTSI provides a continuous measure of TSI up to 9 years, with a mean absolute error of less than 12 months overall and less than 5 months for infections with a TSI of up to a year. It performs equally well for all major HIV subtypes based on data from African and European cohorts.

CONCLUSIONS

We demonstrate how HIV-phyloTSI can be used for incidence estimates on a population level.

Для цитирования: Golubchik Tanya, Abeler-Dörner Lucie, Hall Matthew, Wymant Chris, Bonsall David, Macintyre-Cockett George, Thomson Laura, Baeten Jared M, Celum Connie L, Galiwango Ronald M, Kosloff Barry, Limbada Mohammed, Mujugira Andrew, Mugo Nelly R, Gall Astrid, Blanquart François, Bakker Margreet, Bezemer Daniela, Ong Swee Hoe, Albert Jan, Bannert Norbert, Fellay Jacques, Gunsenheimer-Bartmeyer Barbara, Günthard Huldrych F, Kivelä Pia, Kouyos Roger D, Meyer Laurence, Porter Kholoud, van Sighem Ard, van der Valk Mark, Berkhout Ben, Kellam Paul, Cornelissen Marion, Reiss Peter, Ayles Helen, Burns David N, Fidler Sarah, Grabowski Mary Kate, Hayes Richard, Herbeck Joshua T, Kagaayi Joseph, Kaleebu Pontiano, Lingappa Jairam R, Ssemwanga Deogratius, Eshleman Susan H, Cohen Myron S, Ratmann Oliver, Laeyendecker Oliver, Fraser Christophe, HPTN 071 (PopART) Phylogenetics protocol team, the BEEHIVE consortium and the PANGEA consortium. HIV-phyloTSI: subtype-independent estimation of time since HIV-1 infection for cross-sectional measures of population incidence using deep sequence data.. BMC bioinformatics. 2025. DOI: 10.1186/s12859-025-06189-y.

Предметные рубрики MeSH

HIV InfectionsHumansHIV-1IncidenceHigh-Throughput Nucleotide SequencingCross-Sectional StudiesPhylogeny
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