ipcoal: an interactive Python package for simulating and analyzing genealogies and sequences on a species tree or network

Summary ipcoal is a free and open source Python package for simulating and analyzing genealogies and sequences. It automates the task of describing complex demographic models (e.g. with divergence times, effective population sizes, migration events) to the msprime coalescent simulator by parsing a user-supplied species tree or network. Genealogies, sequences and metadata are returned in tabular format allowing for easy downstream analyses. ipcoal includes phylogenetic inference tools to automate gene tree inference from simulated sequence data, and visualization tools for analyzing results and verifying model accuracy. The ipcoal package is a powerful tool for posterior predictive data analysis, for methods validation and for teaching coalescent methods in an interactive and visual environment. Availability and implementation Source code is available from the GitHub repository (https://github.com/pmckenz1/ipcoal/) and is distributed for packaged installation with conda. Complete documentation and interactive notebooks prepared for teaching purposes, including an empirical example, are available at https://ipcoal.readthedocs.io/. Contact [email protected]

The number of spaced-word matches between two DNA sequences as a function of the underlying pattern weight

We study the number Nk of (spaced) word matches between pairs of evolutionarily related DNA sequences depending on the word length or pattern weight k, respectively. We show that, under the Jukes-Cantor model, the number of substitutions per site that occurred since two sequences evolved from their last common ancestor, can be esti-mated from the slope of a certain function of Nk. Based on these considerations, we implemented a software program for alignment-free sequence comparison called Slope-SpaM. Test runs on simulated sequence data show that Slope-SpaM can estimate phylogenetic dis-tances with high accuracy for up to around 0.5 substitutions per po-sitions. The statistical stability of our results is improved if spaced words are used instead of contiguous k-mers. Unlike previous methods that are based on the number of (spaced) word matches, our approach can deal with sequences that share only local homologies.

Downscaling rainfields in space and time, using the String of Beads model in time series mode

The String of Beads model is a space-time model of rainfields measured by weather radar. It is here driven by two auto-regressive time series models, one at the image scale, the other at the pixel scale, to model the temporal correlation structure of the wet-period process. The marginal distribution of the pixel scale intensities on a given radar-rainfall image is described by a log-normal distribution. The spatial dependence structure of each image is defined by a power spectrum approximated by a power law function with a negative exponent. It is demonstrated that this stochastic modelling approach is valid because the images sampled are effectively stationary above a scale of 30 km, which is less than a quarter of the image width. By advecting a simulated sequence of images along the same cumulative advection vector as the observed event and matching the image-scale statistics of each simulated image with those of the corresponding observed image, a simulated sequence of plausible images is generated which mimics (has the same space-time statistics as) the observed event but differs from it in detail. Aggregating the pixel scale intensities in each sequence over a number of time and space intervals and then comparing their spatial and temporal statistics, demonstrates that the model captures the intermediate scale behaviour well, showing satisfactorily its ability to downscale rainfall in space and time. The model thus has potential as an operational space-time model of rainfields. Keywords: Space-time, rainfield modelling, weather radar, multifractals, Gaussian random fields