Datamonkey is a popular web-based suite of phylogenetic analysis tools for use in evolutionary biology. Since the original release in 2005, we have expanded the analysis options to include recently developed algorithmic methods for recombination detection, evolutionary fingerprinting of genes, codon model selection, co-evolution between sites, identification of sites, which rapidly escape host-immune pressure and HIV-1 subtype assignment. The traditional selection tools have also been augmented to include recent developments in the field. Here, we summarize the analyses options currently available on Datamonkey, and provide guidelines for their use in evolutionary biology. Availability and documentation: http://www.datamonkey.org.

SignificanceStudies characterizing within-host latent HIV sequence diversity have yielded insight into reservoir dynamics and persistence. Our understanding of these processes, however, can be further enhanced if reservoir diversity is interpreted in context of HIV’s within-host evolutionary history. Approaches to infer the original establishment (i.e., integration) dates of individual within-host latent HIV lineages would be particularly useful in this regard. We describe a phylogenetic framework to infer latent HIV ages from viral sequence information and apply it to latent HIV sequences sampled up to 10 y on suppressive therapy to yield insights into HIV reservoir dynamics. The ability to infer within-host latent HIV ages from sequence information has broad potential applications that may advance us toward an HIV cure.

BackgroundDue to the rapid evolution of HIV, infections with similar genetic sequences are likely to be related by recent transmission events. Clusters of related infections can represent subpopulations with high rates of HIV transmission. Here we describe the implementation of an automated “near real-time” system using clustering analysis of routinely collected HIV resistance genotypes to monitor and characterize HIV transmission hotspots in British Columbia (BC).MethodsA monitoring system was implemented on the BC Drug Treatment Database, which currently holds over 32000 anonymized HIV genotypes for nearly 9000 residents of BC living with HIV. On average, five to six new HIV genotypes are deposited in the database every day, which triggers an automated re-analysis of the entire database. Clusters of five or more individuals were extracted on the basis of short phylogenetic distances between their respective HIV sequences. Monthly reports on the growth and characteristics of clusters were generated by the system and distributed to public health officers.FindingsIn June 2014, the monitoring system detected the expansion of a cluster by 11 new cases over three months, including eight cases with transmitted drug resistance. This cluster generally comprised young men who have sex with men. The subsequent report precipitated an enhanced public health follow-up to ensure linkage to care and treatment initiation in the affected subpopulation. Of the nine cases associated with this follow-up, all had already been linked to care and five cases had started treatment. Subsequent to the follow-up, three additional cases started treatment and the majority of cases achieved suppressed viral loads. Over the following 12 months, 12 new cases were detected in this cluster with a marked reduction in the onward transmission of drug resistance.InterpretationOur findings demonstrate the first application of an automated phylogenetic system monitoring a clinical database to detect a recent HIV outbreak and support the ensuing public health response. By making secondary use of routinely collected HIV genotypes, this approach is cost-effective, attains near realtime monitoring of new cases, and can be implemented in all settings where HIV genotyping is the standard of care.FundingThis work was supported by the BC Centre for Excellence in HIV/AIDS and by grants from the Canadian Institutes for Health Research (CIHR HOP-111406, HOP-107544), the Genome BC, Genome Canada and CIHR Partnership in Genomics and Personalized Health (Large-Scale Applied Research Project HIV142 contract to PRH, JSGM, and AFYP), and by the US National Institute on Drug Abuse (1-R01-DA036307-01, 5-R01-031055-02, R01-DA021525-06, and R01-DA011591).