Nextstrain: real-time tracking of pathogen evolution.

Summary: Understanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualization platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, open to health professionals, epidemiologists, virologists and the public alike. Availability and implementation: All code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.

Infrared multiple photon dissociation (IRMPD) spectroscopy of oxazine dyes.

The structure and energetic properties of four common oxazine dyes, Nile red, Nile blue A, Cresyl violet, and Brilliant cresyl blue, have been probed using a combination of infrared multiple-photon dissociation (IRMPD) spectroscopy and quantum chemical calculations. IRMPD spectra of the protonated dyes, as generated from an electrospray ionization (ESI) source, were collected in the range of 900-1800 cm(-1). Vibrational band assignments related to carbonyl and substituted-amine stretches were established from a comparison of the experimental spectra of these related systems as well as from a comparison with spectra generated by density functional theory (DFT) calculations. For Nile red, the thermochemical landscape for protonation at different basic sites was probed using DFT; comparison of IRMPD and calculated IR spectra reveals the site of protonation to be at the carbonyl oxygen. The structural information obtained here in the gas phase pertaining to these important fluorophores is anticipated to provide further insight into their associated intrinsic fluorescent properties in solution.

Efficient inference, potential, and limitations of site-specific substitution models.

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. On one hand, such constraints on sequence evolution can be to infer biological function, one the other hand they need to be accounted for in phylogenetic reconstruction. Phylogenetic models often account for this complexity by partitioning sites into a small number of discrete classes with different rates and/or state preferences. Appropriate model complexity is typically determined by model selection procedures. Here, we present an efficient algorithm to estimate more complex models that allow for different preferences at every site and explore the accuracy at which such models can be estimated from simulated data. Our 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 and known topology. However, the joint estimation of site-specific rates, and site-specific preferences, and phylogenetic branch length can suffer from identifiability problems, while ignoring variation in preferences across sites results in branch length underestimates. Site-specific preferences estimated from large HIV pol alignments show qualitative concordance with intra-host estimates of fitness costs. Analysis of these 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.

TreeTime: Maximum-likelihood phylodynamic analysis.

Mutations that accumulate in the genome of cells or viruses can be used to infer their evolutionary history. In the case of rapidly evolving organisms, genomes can reveal their detailed spatiotemporal spread. Such phylodynamic analyses are particularly useful to understand the epidemiology of rapidly evolving viral pathogens. As the number of genome sequences available for different pathogens has increased dramatically over the last years, phylodynamic analysis with traditional methods becomes challenging as these methods scale poorly with growing datasets. 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 runtime of TreeTime scales linearly with dataset size.