SUMMARY: In solid-organ transplant recipients, a delicate balance between immunosuppression and immunocompetence must be achieved, which can be difficult to monitor in real-time. Shotgun sequencing of cell-free DNA (cfDNA) has been recently proposed as a new way to indirectly assess immune function in transplant recipients through analysis of the status of the human virome. To facilitate exploration of the utility of the human virome as an indicator of immune status, and to enable rapid, straightforward analyses by clinicians, we developed a fully automated computational pipeline, EzMap, for performing metagenomic analysis of the human virome. EzMap combines a number of tools to clean, filter, and subtract WGS reads by mapping to a reference human assembly. The relative abundance of each virus present is estimated using a maximum likelihood approach that accounts for genome size, and results are presented with interactive visualizations and taxonomy-based summaries that enable rapid insights. The pipeline is automated to run on both workstations and computing clusters for all steps. EzMap automates an otherwise tedious and time-consuming protocol and aims to facilitate rapid and reproducible insights from cfDNA. AVAILABILITY AND IMPLEMENTATION: EzMap is freely available at https://github.com/dekoning-lab/ezmap. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: The simplifying assumptions that are used widely in theoretical population genetics may not always be appropriate for empirical population genetics. General computational approaches that do not require the assumptions of classical theory are therefore quite desirable. One such general approach is provided by the theory of absorbing Markov chains, which can be used to obtain exact results by directly analyzing population genetic Markov models, such as the classic bi-allelic Wright-Fisher model. Although these approaches are sometimes used, they are usually forgone in favor of simulation methods, due to the perception that they are too computationally burdensome. Here we show that, surprisingly, direct analysis of virtually any Markov chain model in population genetics can be made quite efficient by exploiting transition matrix sparsity and by solving restricted systems of linear equations, allowing a wide variety of exact calculations (within machine precision) to be easily and rapidly made on modern workstation computers. Results: We introduce Wright-Fisher Exact Solver (WFES), a fast and scalable method for direct analysis of Markov chain models in population genetics. WFES can rapidly solve for both long-term and transient behaviours including fixation and extinction probabilities, expected times to fixation or extinction, sojourn times, expected allele age and variance, and others. Our implementation requires only seconds to minutes of runtime on modern workstations and scales to biological population sizes ranging from humans to model organisms. Availability and Implementation: The code is available at https://github.com/dekoning-lab/wfes. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: Phylogenetics, likelihood, evolution and complexity (PLEX) is a flexible and fast Bayesian Markov chain Monte Carlo software program for large-scale analysis of nucleotide and amino acid data using complex evolutionary models in a phylogenetic framework. The program gains large speed improvements over standard approaches by implementing 'partial sampling of substitution histories', a data augmentation approach that can reduce data analysis times from months to minutes on large comparative datasets. A variety of nucleotide and amino acid substitution models are currently implemented, including non-reversible and site-heterogeneous mixture models. Due to efficient algorithms that scale well with data size and model complexity, PLEX can be used to make inferences from hundreds to thousands of taxa in only minutes on a desktop computer. It also performs probabilistic ancestral sequence reconstruction. Future versions will support detection of co-evolutionary interactions between sites, probabilistic tests of convergent evolution and rigorous testing of evolutionary hypotheses in a Bayesian framework. AVAILABILITY AND IMPLEMENTATION: PLEX v1.0 is licensed under GPL. Source code and documentation will be available for download at www.evolutionarygenomics.com/ProgramsData/PLEX. PLEX is implemented in C++ and supported on Linux, Mac OS X and other platforms supporting standard C++ compilers. Example data, control files, documentation and accessory Perl scripts are available from the website. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.