BACKGROUND: While multiple alignment is the first step of usual classification schemes for biological sequences, alignment-free methods are being increasingly used as alternatives when multiple alignments fail. Subword-based combinatorial methods are popular for their low algorithmic complexity (suffix trees ...) or exhaustivity (motif search), in general with fixed length word and/or number of mismatches. We developed previously a method to detect local similarities (the N-local decoding) based on the occurrences of repeated subwords of fixed length, which does not impose a fixed number of mismatches. The resulting similarities are, for some "good" values of N, sufficiently relevant to form the basis of a reliable alignment-free classification. The aim of this paper is to develop a method that uses the similarities detected by N-local decoding while not imposing a fixed value of N. We present a procedure that selects for every position in the sequences an adaptive value of N, and we implement it as the MS4 classification tool. RESULTS: Among the equivalence classes produced by the N-local decodings for all N, we select a (relatively) small number of "relevant" classes corresponding to variable length subwords that carry enough information to perform the classification. The parameter N, for which correct values are data-dependent and thus hard to guess, is here replaced by the average repetitivity kappa of the sequences. We show that our approach yields classifications of several sets of HIV/SIV sequences that agree with the accepted taxonomy, even on usually discarded repetitive regions (like the non-coding part of LTR). CONCLUSIONS: The method MS4 satisfactorily classifies a set of sequences that are notoriously hard to align. This suggests that our approach forms the basis of a reliable alignment-free classification tool. The only parameter kappa of MS4 seems to give reasonable results even for its default value, which can be a great advantage for sequence sets for which little information is available.

SUMMARY: The R package SIMoNe (Statistical Inference for MOdular NEtworks) enables inference of gene-regulatory networks based on partial correlation coefficients from microarray experiments. Modelling gene expression data with a Gaussian graphical model (hereafter GGM), the algorithm estimates non-zero entries of the concentration matrix, in a sparse and possibly high-dimensional setting. Its originality lies in the fact that it searches for a latent modular structure to drive the inference procedure through adaptive penalization of the concentration matrix. AVAILABILITY: Under the GNU General Public Licence at http://cran.r-project.org/web/packages/simone/

Molecular Dynamics is nowadays routinely used to complement experimental studies and overcome some of their limitations. In particular, current experimental techniques do not allow to directly observe the full dynamics of a macromolecule at atomic detail. Molecular simulation provides time-dependent atomic positions, velocities and system energies according to biophysical models. Many molecular simulation engines can now compute a molecular dynamics trajectory of interesting biological systems in interactive time. This progress has lead to a new approach called interactive molecular dynamics. It allows to control and visualise a molecular simulation in progress. We have developed a generic library, called MDDriver, to facilitate the implementation of such interactive simulations. It allows to easily create a network between a molecular user interface and a physically-based simulation. We use this library in order to study an interesting biomolecular system, simulated by various interaction-enabled molecular engines and models. We use a classical molecular visualisation tool and a haptic device to control the dynamic behavior of the molecule. This approach provides encouraging results for interacting with a biomolecule and understanding its dynamics. Starting from this initial success, we decided to use VR functionalities more intensively, by designing a VR framework dedicated to immersive and interactive molecular simulations. This framework is based on MDDriver, on the visualisation toolkit VTK, and on the vtkVRPN library, which encapsulates the VRPN library into VTK.