SUMMARY: Computational models in biology can increase our understanding of biological systems, be used to answer research questions, and make predictions. Accessibility and reusability of computational models is limited and often restricted to experts in programming and mathematics. This is due to the need to implement entire models and solvers from the mathematical notation models are normally presented as. Here, we present SBMLtoOdin, an R package that translates differential equation models in SBML format from the BioModels database into executable R code using the R package odin, allowing researchers to easily reuse models. We also present Menelmacar, a web-based application that provides interactive visualisations of these models by solving their differential equations in the browser. This platform allows non-experts to simulate and investigate models using an easy-to-use interface. AVAILABILITY AND IMPLEMENTATION: SBMLtoOdin is published under the open source Apache 2.0 licence at https://github.com/bacpop/SBMLtoOdin and can be installed as an R package. The code for the Menelmacar website is published under the MIT License at https://github.com/bacpop/odinviewer, and the website can be found at https://biomodels.bacpop.org/.

BACKGROUND: Reconstructing individual transmission events in an infectious disease outbreak can provide valuable information and help inform infection control policy. Recent years have seen considerable progress in the development of methodologies for reconstructing transmission chains using both epidemiological and genetic data. However, only a few of these methods have been implemented in software packages, and with little consideration for customisability and interoperability. Users are therefore limited to a small number of alternatives, incompatible tools with fixed functionality, or forced to develop their own algorithms at considerable personal effort. RESULTS: Here we present outbreaker2, a flexible framework for outbreak reconstruction. This R package re-implements and extends the original model introduced with outbreaker, but most importantly also provides a modular platform allowing users to specify custom models within an optimised inferential framework. As a proof of concept, we implement the within-host evolutionary model introduced with TransPhylo, which is very distinct from the original genetic model in outbreaker, and demonstrate how even complex model results can be successfully included with minimal effort. CONCLUSIONS: outbreaker2 provides a valuable starting point for future outbreak reconstruction tools, and represents a unifying platform that promotes customisability and interoperability. Implemented in the R software, outbreaker2 joins a growing body of tools for outbreak analysis.

SUMMARY: Phylogenetic comparative methods are essential for addressing evolutionary hypotheses with interspecific data. The scale and scope of such data have increased dramatically in the past few years. Many existing approaches are either computationally infeasible or inappropriate for data of this size. To address both of these problems, we present geiger v2.0, a complete overhaul of the popular R package geiger. We have reimplemented existing methods with more efficient algorithms and have developed several new approaches for accomodating heterogeneous models and data types. AVAILABILITY AND IMPLEMENTATION: This R package is available on the CRAN repository http://cran.r-project.org/web/packages/geiger/. All source code is also available on github http://github.com/mwpennell/geiger-v2. geiger v2.0 depends on the ape package. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.