Biology subjects

Oliver Pybus

Publications and source records attributed to Oliver Pybus.

3 recordsLinked to original sources

bioRxiv2016

A phylogenetic codon substitution model for antibody lineages

Phylogenetic methods have shown promise in understanding the development of broadly neutralizing antibody lineages (bNAbs). However, the mutational process that generates these lineages - somatic hypermutation (SHM) - is biased by hotspot motifs, which violates important assumptions in most phylogenetic substitution models. Here, we develop a modified GY94-type substitution model that partially accounts for this context-dependency while preserving independence of sites during calculation. This model shows a substantially better fit to three well-characterized bNAb lineages than the standard GY94 model. We show through simulations that accounting for hotspot motifs can lead to reduced bias of other substitution parameters, and more accurate ancestral state reconstructions. We also demonstrate how our model can be used to test hypotheses concerning the roles of different hotspot and coldspot motifs in the evolution of B-cell lineages. Further, we explore the consequences of the idea that the number of hotspot motifs - and perhaps the mutation rate in general - is expected to decay over time in individual bNAb lineages.

Evolutionary Biology↗

bioRxiv2016

Modelling the decay of hotspot motifs in broadly neutralizing antibody lineages

Phylogenetic methods have shown great promise in understanding the development of broadly neutralizing antibody lineages (bNAbs). However, mutational process for generating these lineages - somatic hypermutation (SHM) - is biased by hotspot motifs, which violates important assumptions in most phylogenetic substitution models. Here, we develop a modified GY94-type substitution model which partially accounts for this context-dependency while preserving independence of sites in calculation. This model shows a substantially better fit to three well-characterized bNAb lineages than the standard GY94 model. We show through simulations that accounting for this can lead to reduced bias of other substitution parameters, and more accurate ancestral state reconstructions. We further explore other implications of this model; namely, that the number of hotspot motifs - and therefore likely the mutation rate in general - is expected to decay over time in individual bNAb lineages.

Evolutionary Biology↗

bioRxiv2015

Coalescent inference using serially sampled, high-throughput sequencing data from intra-host HIV infection

Human immunodeficiency virus (HIV) is a rapidly evolving pathogen that causes chronic infections, so genetic diversity within a single infection can be very high. High-throughput \"deep\" sequencing can now measure this diversity in unprecedented detail, particularly since it can be performed at different timepoints during an infection, and this offers a potentially powerful way to infer the evolutionary dynamics of the intra-host viral population. However, population genomic inference from HIV sequence data is challenging because of high rates of mutation and recombination, rapid demographic changes, and ongoing selective pressures. In this paper we develop a new method for inference using HIV deep sequencing data using an approach based on importance sampling of ancestral recombination graphs under a multi-locus coalescent model. The approach further extends recent progress in the approximation of so-called conditional sampling distributions, a quantity of key interest when approximating co-alescent likelihoods. The chief novelties of our method are that it is able to infer rates of recombination and mutation, as well as the effective population size, while handling sampling over different timepoints and missing data without extra computational difficulty. We apply our method to a dataset of HIV-1, in which several hundred sequences were obtained from an infected individual at seven timepoints over two years. We find mutation rate and effective population size estimates to be comparable to those produced by the software BEAST. Additionally, our method is able to produce local recombination rate estimates. The software underlying our method, Coalescenator, is freely available.

Genetics↗