UNLABELLED: In an attempt to make the processing of RAD-seq data easier and allow rapid and automated exploration of parameters/data for phylogenetic inference, we introduce the perl pipeline RADIS Users of RADIS can let their raw Illumina data be processed up to phylogenetic tree inference, or stop (and restart) the process at some point. Different values for key parameters can be explored in a single analysis (e.g. loci building, sample/loci selection), making possible a thorough exploration of data. RADIS relies on Stacks for demultiplexing of data, removing PCR duplicates and building individual and catalog loci. Scripts have been specifically written for trimming of reads and loci/sample selection. Finally, RAxML is used for phylogenetic inferences, though other software may be utilized. AVAILABILITY AND IMPLEMENTATION: RADIS is written in perl, designed to run on Linux and Unix platforms. RADIS and its manual are freely available from http://www1.montpellier.inra.fr/CBGP/software/RADIS/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques. RESULTS: We propose a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with RF and postponing the approximation of the posterior probability of the selected model for a second stage also relying on RF. Compared with earlier implementations of ABC model choice, the ABC RF approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least 50) and (iv) it includes an approximation of the posterior probability of the selected model. The call to RF will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets. AVAILABILITY AND IMPLEMENTATION: The proposed methodology is implemented in the R package abcrf available on the CRAN. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: DIYABC is a software package for a comprehensive analysis of population history using approximate Bayesian computation on DNA polymorphism data. Version 2.0 implements a number of new features and analytical methods. It allows (i) the analysis of single nucleotide polymorphism data at large number of loci, apart from microsatellite and DNA sequence data, (ii) efficient Bayesian model choice using linear discriminant analysis on summary statistics and (iii) the serial launching of multiple post-processing analyses. DIYABC v2.0 also includes a user-friendly graphical interface with various new options. It can be run on three operating systems: GNU/Linux, Microsoft Windows and Apple Os X. AVAILABILITY: Freely available with a detailed notice document and example projects to academic users at http://www1.montpellier.inra.fr/CBGP/diyabc CONTACT: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

UNLABELLED: With the development of next-generation sequencing and genotyping approaches, large single nucleotide polymorphism haplotype datasets are becoming available in a growing number of both model and non-model species. Identifying genomic regions with unexpectedly high local haplotype homozygosity relatively to neutral expectation represents a powerful strategy to ascertain candidate genes responding to natural or artificial selection. To facilitate genome-wide scans of selection based on the analysis of long-range haplotypes, we developed the R package rehh. It provides a versatile tool to detect the footprints of recent or ongoing selection with several graphical functions that help visual interpretation of the results. AVAILABILITY AND IMPLEMENTATION: Stable version is available from CRAN: http://cran.r-project.org/. Development version is available from the R-forge repository: http://r-forge.r-project.org/projects/rehh. Both versions can be installed directly from R. Function documentation and example data files are provided within the package and a tutorial is available as Supplementary Material. rehh is distributed under the GNU General Public Licence (GPL ≥ 2).

This paper describes a 6 DOFs haptic cooperative platform dedicated to CAD objects simulation and virtual prototyping tasks. This system is based on several distributed physical engines linked together by a network subject to time delay. To deal with the destabilizing effect of time delay, a wave variable transformation is used. The wave variable formalism is adapted to the platform, therefore the behavior of each physical engine is similar. Several prototyping tasks have been tested on this system. A case is presented where two users are manipulating two distinct objects and interacting with each other.

In this paper, we propose to evaluate the benefits of event-based haptic rendering in virtual environments subject to high damping or low contact stiffness. In such context, the degraded perception of contact impairs the manipulation performance and the overall user's experience. Haptic rendering techniques known as event-based (or open-loop) rendering have been proposed to improve the realism of contacts in such contexts.