tetano
Editor, Senior Moderator
Mol Biol Evol. 2011 Dec 8. [Epub ahead of print]
Does history repeat itself? Wavelets and the phylodynamics of influenza A.
Tom JA, Sinsheimer JS, Suchard MA.
Source
Department of Biostatistics UCLA School of Public Health Los Angeles.
Abstract
Unprecedented global surveillance of viruses will result in massive sequence datasets that require new statistical methods. These datasets press the limits of Bayesian phylogenetics as the high-dimensional parameters that comprise a phylogenetic tree increase the already sizable computational burden of these techniques. This burden often results in partitioning the dataset, e.g. by gene, and inferring the evolutionary dynamics of each partition independently, a compromise that results in stratified analyses that depend only on data within a given partition. However, parameter estimates inferred from these stratified models are likely strongly correlated considering they rely on data from a single dataset. To overcome this shortfall, we exploit the existing Monte Carlo realizations from stratified Bayesian analyses to efficiently estimate a nonparametric hierarchical wavelet-based model and learn about the time-varying parameters of effective population size that reflect levels of genetic diversity across all partitions simultaneously. Our methods are applied to complete genome influenza A sequences that span thirteen years. We find that broad peaks and trends, as opposed to seasonal spikes, in the effective population size history distinguish individual segments from the complete genome. We also address hypotheses regarding inter-segment dynamics within a formal statistical framework that accounts for correlation between segment-specific parameters.
PMID:
22160768
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/22160768
Does history repeat itself? Wavelets and the phylodynamics of influenza A.
Tom JA, Sinsheimer JS, Suchard MA.
Source
Department of Biostatistics UCLA School of Public Health Los Angeles.
Abstract
Unprecedented global surveillance of viruses will result in massive sequence datasets that require new statistical methods. These datasets press the limits of Bayesian phylogenetics as the high-dimensional parameters that comprise a phylogenetic tree increase the already sizable computational burden of these techniques. This burden often results in partitioning the dataset, e.g. by gene, and inferring the evolutionary dynamics of each partition independently, a compromise that results in stratified analyses that depend only on data within a given partition. However, parameter estimates inferred from these stratified models are likely strongly correlated considering they rely on data from a single dataset. To overcome this shortfall, we exploit the existing Monte Carlo realizations from stratified Bayesian analyses to efficiently estimate a nonparametric hierarchical wavelet-based model and learn about the time-varying parameters of effective population size that reflect levels of genetic diversity across all partitions simultaneously. Our methods are applied to complete genome influenza A sequences that span thirteen years. We find that broad peaks and trends, as opposed to seasonal spikes, in the effective population size history distinguish individual segments from the complete genome. We also address hypotheses regarding inter-segment dynamics within a formal statistical framework that accounts for correlation between segment-specific parameters.
PMID:
22160768
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/22160768