Bayesian co-estimation of selfing rate and locus-specific mutation rates for a partially selfing populationBenjamin D Redelings, Seiji Kumagai, Liuyang Wang, Andrey Tatarenkov, Ann K. Sakai, Stephen G. Weller, Theresa M. Culley, John C. Avise, Marcy K. Uyenoyama
doi: http://dx.doi.org/10.1101/020537
We present a Bayesian method for characterizing the mating system of populations reproducing through a mixture of self-fertilization and random outcrossing. Our method uses patterns of genetic variation across the genome as a basis for inference about pure hermaphroditism, androdioecy, and gynodioecy. We extend the standard coalescence model to accommodate these mating systems, accounting explicitly for multilocus identity disequilibrium, inbreeding depression, and variation in fertility among mating types. We incorporate the Ewens Sampling Formula (ESF) under the infinite-alleles model of mutation to obtain a novel expression for the likelihood of mating system parameters. Our Markov chain Monte Carlo (MCMC) algorithm assigns locus-specific mutation rates, drawn from a common mutation rate distribution that is itself estimated from the data using a Dirichlet Process Prior model. Among the parameters jointly inferred are the population-wide rate of self-fertilization, locus-specific mutation rates, and the number of generations since the most recent outcrossing event for each sampled individual.

Coalescent inference using serially sampled, high-throughput sequencing data from HIV infected patientsKevin Dialdestoro, Jonas Andreas Sibbesen, Lasse Maretty, Jayna Raghwani, Astrid Gall, Paul Kellam, Oliver Pybus, Jotun Hein, Paul Jenkins
doi: http://dx.doi.org/10.1101/020552
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 coalescent 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.

Predictable patterns of CTL escape and reversion and the limited role of epistasis in HIV-1 evolution
Duncan S. Palmer, Emily Adland, John A. Frater, Philip J.R. Goulder, Kuan-Hsiang Gary Huang, Thumbi Ndung’u, Philippa C. Matthews, Rodney E. Phillips, Roger Shapiro, Gil McVean, Angela R. McLean
Subjects: Populations and Evolution (q-bio.PE)

The twin processes of viral evolutionary escape and reversion in response to host immune pressure, in particular the cytotoxic T-lymphocyte (CTL) response, helps shape Human Immunodeficiency Virus-1 sequence evolution in infected host populations. The tempo of CTL escape and reversion is known to differ between CTL escape variants in a given host population. A wealth of epistatic effects – both intermediary sequence changes on the path to CTL escape and compensatory mutations which restore replicative capacity following viral escape – have been reported. Given the importance of epistatic effects in these processes, we ask: are rates of escape and reversion comparable across infected host populations? For three cohorts taken from three continents, we estimate escape and reversion rates at 23 escape sites in $gag$ epitopes. Surprisingly, we find highly consistent escape rate estimates across the examined cohorts. Reversion rates are also consistent between a Canadian and South African infected host population. We investigate the importance of epistasis further by examining $in$ $vitro$ replicative capacities of viral sequences with minimal variation: point escape mutants induced in a lab strain. Remarkably, despite the complexities of epistatic effects and the diversity of both hosts and viruses, CTL escape mutants which escape rapidly tend to be those with the highest replicative capacity when applied as a single point mutation. Similarly, mutants inducing the greatest costs to viral replicative capacity tend to revert more quickly. These data suggest that escape rates in $gag$ are consistent across host populations and, in general, epistatic effects do not dramatically affect escape rates.

A semi-supervised approach uncovers thousands of intragenic enhancers differentially activated in human cells
Juan Gonzalez-Vallinas, Amadís Pagès, Babita Singh, Eduardo Eyras
doi: http://dx.doi.org/10.1101/020362

Background Transcriptional enhancers are generally known to regulate gene transcription from afar. Their activation involves a series of changes in chromatin marks and recruitment of protein factors. These enhancers may also occur inside genes, but how many may be active in human cells and their effects on the regulation of the host gene remains unclear. Results We describe a novel semi-supervised method based on the relative enrichment of chromatin signals between 2 conditions to predict active enhancers. We applied this method to the tumoral K562 and the normal GM12878 cell lines to predict enhancers that are differentially active in one cell type. These predictions show enhancer-like properties according to positional distribution, correlation with gene expression and production of enhancer RNAs. Using this model, we predict 10,365 and 9,777 intragenic active enhancers in K562 and GM12878, respectively, and relate the differential activation of these enhancers to expression and splicing differences of the host genes. Conclusions We propose that the activation or silencing of intragenic transcriptional enhancers modulate the regulation of the host gene by means of a local change of the chromatin and the recruitment of enhancer-related factors that may interact with the RNA directly or through the interaction with RNA binding proteins. Predicted enhancers are available at http://regulatorygenomics.upf.edu/Projects/enhancers.html

Pyvolve: a flexible Python module for simulating sequences along phylogenies
Stephanie J Spielman, Claus O Wilke
doi: http://dx.doi.org/10.1101/020214
We introduce Pyvolve, a flexible Python module for simulating genetic data along a phylogeny according to continuous-time Markov models of sequence evolution. Pyvolve incorporates most standard models of nucleotide, amino-acid, and codon sequence evolution, and it allows users to fully customize all model parameters. Pyvolve additionally allows users to specify custom evolutionary models and incorporates several novel features, including a novel rate matrix scaling algorithm and branch-length perturbations. Easily incorporated into Python bioinformatics pipelines, Pyvolve represents a convenient and flexible alternative to third-party simulation softwares. Pyvolve is an open-source project available, along with a detailed user-manual, under a FreeBSD license from https://github.com/sjspielman/pyvolve. API documentation is available from http://sjspielman.org/pyvolve.