Coala: an R framework for coalescent simulation.

UNLABELLED: Simulation programs based on the coalescent efficiently generate genetic data according to a given model of evolution. We present coala, an R package for calling coalescent simulators with a unified syntax. It can execute simulations with several programs, calculate additional summary statistics and combine multiple simulations to create biologically more realistic data. AVAILABILITY AND IMPLEMENTATION: The package is publicly available on CRAN and on https://github.com/statgenlmu/coala under the conditions of the MIT license. CONTACT: metzler@bio.lmu.de.

scrm: efficiently simulating long sequences using the approximated coalescent with recombination.

MOTIVATION: Coalescent-based simulation software for genomic sequences allows the efficient in silico generation of short- and medium-sized genetic sequences. However, the simulation of genome-size datasets as produced by next-generation sequencing is currently only possible using fairly crude approximations. RESULTS: We present the sequential coalescent with recombination model (SCRM), a new method that efficiently and accurately approximates the coalescent with recombination, closing the gap between current approximations and the exact model. We present an efficient implementation and show that it can simulate genomic-scale datasets with an essentially correct linkage structure.

Why to account for finite sites in population genetic studies and how to do this with Jaatha 2.0.

With the advent of next-generation sequencing technologies, large data sets of several thousand loci from multiple conspecific individuals are available. Such data sets should make it possible to obtain accurate estimates of population genetic parameters, even for complex models of population history. In the analyses of large data sets, it is difficult to consider finite-sites mutation models (FSMs). Here, we use extensive simulations to demonstrate that the inclusion of FSMs is necessary to avoid severe biases in the estimation of the population mutation rate θ, population divergence times, and migration rates. We present a new version of Jaatha, an efficient composite-likelihood method for estimating demographic parameters from population genetic data and evaluate the usefulness of Jaatha in two biological examples. For the first application, we infer the speciation process of two wild tomato species, Solanum chilense and Solanum peruvianum. In our second application example, we demonstrate that Jaatha is readily applicable to NGS data by analyzing genome-wide data from two southern European populations of Arabidopsis thaliana. Jaatha is now freely available as an R package from the Comprehensive R Archive Network (CRAN).

SynBioWave--a real-time communication platform for molecular and synthetic biology.

SUMMARY: Synthetic Biology is advanced by many users and relies on the assembly of genetic elements to devices, systems and finally genomes. SynBioWave is a software suite that enables multiple distributed users to analyze and construct genetic parts in real-time collaboration. It builds on Google Wave and provides an extensible robot-robot-user communication framework, a menu driven user interface, biological data handling including DAS and an internal database communication. We demonstrate its use by implementing robots for gene-data retrieval, manipulation and display. The initial development of SynBioWave demonstrates the power of the underlying Google Wave protocol for Synthetic Biology and lays the foundation for continuous and user-friendly extensions. Specialized wave-robots with a manageable set of capabilities will divide and conquer the complex task of creating a genome in silico. AVAILABILITY: The robot is available at SynBioWave@appspot.com, the source code at http://synbiowave.sourceforge.net