Low-cost assembly of a cacao crop genome is able to resolve complex heterozygous bubbles.

Cacao (Theobroma cacao) is a tropical tree that produces the essential raw material for chocolate. Because yields have been stagnant, land use has expanded to provide for increasing chocolate demand. Assembled genomes of key parents could modernize breeding programs in the remote and under-resourced locations where cacao is grown. The MinION, a long read sequencer that runs off of a laptop computer, has the potential to facilitate the assembly of the complex genomes of high-yielding F1 hybrids. Here, we validate the MinION's application to heterozygous crops by creating a de novo genome assembly of a key parent in breeding programs, the clone Pound 7. Our MinION-only assembly was 20% larger than the latest released cacao genome, with 10-fold greater contiguity, and the resolution of complex heterozygosity and repetitive elements. Polishing with Illumina short reads brought the predicted completeness of our assembly to similar levels to the previously released cacao genome assemblies. In contrast to previous cacao genome projects, our assembly required only a small scientific team and limited reagents. Our sequencing and assembly methods could easily be adopted by under-resourced breeding programs, speeding crop improvement in the developing world.

Evolution of equine influenza virus in vaccinated horses.

Influenza A viruses are characterized by their ability to evade host immunity, even in vaccinated individuals. To determine how prior immunity shapes viral diversity in vivo, we studied the intra- and interhost evolution of equine influenza virus in vaccinated horses. Although the level and structure of genetic diversity were similar to those in naïve horses, intrahost bottlenecks may be more stringent in vaccinated animals, and mutations shared among horses often fall close to putative antigenic sites.

Inferring population-level contact heterogeneity from common epidemic data.

Models of infectious disease spread that incorporate contact heterogeneity through contact networks are an important tool for epidemiologists studying disease dynamics and assessing intervention strategies. One of the challenges of contact network epidemiology has been the difficulty of collecting individual and population-level data needed to develop an accurate representation of the underlying host population's contact structure. In this study, we evaluate the utility of common epidemiological measures (R0, epidemic peak size, duration and final size) for inferring the degree of heterogeneity in a population's unobserved contact structure through a Bayesian approach. We test the method using ground truth data and find that some of these epidemiological metrics are effective at classifying contact heterogeneity. The classification is also consistent across pathogen transmission probabilities, and so can be applied even when this characteristic is unknown. In particular, the reproductive number, R0, turns out to be a poor classifier of the degree heterogeneity, while, unexpectedly, final epidemic size is a powerful predictor of network structure across the range of heterogeneity. We also evaluate our framework on empirical epidemiological data from past and recent outbreaks to demonstrate its application in practice and to gather insights about the relevance of particular contact structures for both specific systems and general classes of infectious disease. We thus introduce a simple approach that can shed light on the unobserved connectivity of a host population given epidemic data. Our study has the potential to inform future data-collection efforts and study design by driving our understanding of germane epidemic measures, and highlights a general inferential approach to learning about host contact structure in contemporary or historic populations of humans and animals.

Inferring the inter-host transmission of influenza A virus using patterns of intra-host genetic variation.

Influenza A viruses (IAVs) cause acute, highly transmissible infections in a wide range of animal species. Understanding how these viruses are transmitted within and between susceptible host populations is critical to the development of effective control strategies. While viral gene sequences have been used to make inferences about IAV transmission dynamics at the epidemiological scale, their utility in accurately determining patterns of inter-host transmission in the short-term--i.e. who infected whom--has not been strongly established. Herein, we use intra-host sequence data from the viral HA1 (hemagglutinin) gene domain from two transmission studies employing different IAV subtypes in their natural hosts--H3N8 in horses and H1N1 in pigs-to determine how well these data recapitulate the known pattern of inter-host transmission. Although no mutations were fixed over the course of either experimental transmission chain, we show that some minor, transient alleles can provide evidence of host-to-host transmission and, importantly, can be distinguished from those that cannot.

Protocols for sampling viral sequences to study epidemic dynamics.

With more emphasis being put on global infectious disease monitoring, viral genetic data are being collected at an astounding rate, both within and without the context of a long-term disease surveillance plan. Concurrent with this increase have come improvements to the sophisticated and generalized statistical techniques used for extracting population-level information from genetic sequence data. However, little research has been done on how the collection of these viral sequence data can or does affect the efficacy of the phylogenetic algorithms used to analyse and interpret them. In this study, we use epidemic simulations to consider how the collection of viral sequence data clarifies or distorts the picture, provided by the phylogenetic algorithms, of the underlying population dynamics of the simulated viral infection over many epidemic cycles. We find that sampling protocols purposefully designed to capture sequences at specific points in the epidemic cycle, such as is done for seasonal influenza surveillance, lead to a significantly better view of the underlying population dynamics than do less-focused collection protocols. Our results suggest that the temporal distribution of samples can have a significant effect on what can be inferred from genetic data, and thus highlight the importance of considering this distribution when designing or evaluating protocols and analysing the data collected thereunder.