Biology subjects

Puller, V.

Publications and source records attributed to Puller, V..

3 recordsLinked to original sources

bioRxiv2020

Efficient inference, potential, and limitations of site-specific substitution model

Natural selection imposes a complex filter on which variants persist in a population resulting in evolutionary patterns that vary greatly along the genome. Some sites evolve close to neutrally, while others are highly conserved, allow only specific states or only change in concert with other sites. Most commonly used evolutionary models, however, ignore much of this complexity and at best account for variation in the rate at which different sites change. Here, we present an efficient algorithm to estimate more complex models that allow for site-specific preferences and explore the accuracy at which such models can be estimated from simulated data. We find that an iterative approximate maximum likelihood scheme uses information in the data efficiently and accurately estimates site-specific preferences from large data sets with moderately diverged sequences. Ignoring site-specific preferences during estimation of branch length of phylogenetic trees - an assumption of most phylogeny software - results in substantial underestimation comparable to the error incurred when ignoring rate variation. However, the joint estimation of branch lengths, site-specific rates, and site-specific preferences can suffer from identifiability problems and is typically unable to recover the correct branch lengths. Site-specific preferences estimated from large HIV pol alignments show qualitative concordance with intra-host estimates of fitness costs. Analysis of site-specific HIV substitution models suggests near saturation of divergence after a few hundred years. Such saturation can explain the inability to infer deep divergence times of HIV and SIVs using molecular clock approaches and time-dependent rate estimates.

evolutionary biology↗

bioRxiv2017

TreeTime: maximum likelihood phylodynamic analysis

Mutations that accumulate in the genome of replicating biological organisms can be used to infer their evolutionary history. In the case of measurably evolving organisms genomes often reveal their detailed spatiotemporal spread. Such phylodynamic analyses are particularly useful to understand the epidemiology of rapidly evolving viral pathogens. The number of genome sequences available for different pathogens, however, has increased dramatically over the last couple of years and traditional methods for phylodynamic analysis scale poorly with growing data sets. Here, we present TreeTime, a Python based framework for phylodynamic analysis using an approximate Maximum Likelihood approach. TreeTime can estimate ancestral states, infer evolution models, reroot trees to maximize temporal signals, estimate molecular clock phylogenies and population size histories. The run time of TreeTime scales linearly with data set size.

evolutionary biology↗

bioRxiv2017

Estimating Time Of HIV-1 Infection From Next-Generation Sequence Diversity

Estimating the time since infection (TI) in newly diagnosed HIV-1 patients is challenging, but important to understand the epidemiology of the infection. Here we explore the utility of virus diversity estimated by next-generation sequencing (NGS) as novel biomarker by using a recent genome-wide longitudinal dataset obtained from 11 untreated HIV-1-infected patients with known dates of infection. The results were validated on a second dataset from 31 patients.\n\nVirus diversity increased linearly with time, particularly at 3rd codon positions, with little inter-patient variation. The precision of the TI estimate improved with increasing sequencing depth, showing that diversity in NGS data yields superior estimates to the number of ambiguous sites in Sanger sequences, which is one of the alternative biomarkers. The full advantage of deep NGS was utilized with continuous diversity measures such as average pairwise distance or site entropy, rather than the fraction of polymorphic sites. The precision depended on the genomic region and codon position and was highest when 3rd codon positions in the entire pol gene were used. For these data, TI estimates had a mean absolute error of around 1 year. The error increased only slightly from around 0.6 years at a TI of 6 months to around 1.1 years at 6 years.\n\nOur results show that virus diversity determined by NGS can be used to estimate time since HIV-1 infection many years after the infection, in contrast to most alternative biomarkers. We provide the regression coefficients as well as web tool for TI estimation.\n\nAuthor summaryHIV-1 establishes a chronic infection, which may last for many years before the infected person is diagnosed. The resulting uncertainty in the date of infection leads to difficulties in estimating the number of infected but undiagnosed persons as well as the number of new infections, which is necessary for developing appropriate public health policies and interventions. Such estimates would be much easier if the time since HIV-1 infection for newly diagnosed cases could be accurately estimated. Three types of biomarkers have been shown to contain information about the time since HIV-1 infection, but unfortunately, they only distinguish between recent and long-term infections (concentration of HIV-1-specific antibodies) or are imprecise (immune status as measured by levels of CD4+ T-lymphocytes and viral sequence diversity measured by polymorphisms in Sanger sequences).\n\nIn this paper, we show that recent advances in sequencing technologies, i.e. the development of next generation sequencing, enable significantly more precise determination of the time since HIV-1 infection, even many years after the infection event. This is a significant advance which could translate into more effective HIV-1 prevention.

epidemiology↗