You can find below the list of sofware developped by the AROBAS team, accompanied by a short description and a link to download or access to this tool.
Non-coding RNAs are often involved as regulators of gene expression at the post-transcriptional level. Because their detection by experimental techniques is difficult and expensive and requires a large amount of time, computational methods represent the first step in non-coding RNA identification and analysis.
In our team we developed several methods and software for structure prediction, identification and analysis of non-coding RNAs. To make them available for the community of biologists and bioinformaticians, we developed a software plateform , called EvryRNA, where we can find:
pseudoknots (Tahi et al., IJAIT, 2005)
structure prediction by the comparative approach (Engelenand Tahi, BMC Bioinformatics, 2007).
and P-DCFold (Engelen and Tahi, NAR 2010).
Tahi, NAR 2012).
based on their relationship with transposable element sequences (Tempel and al., BMC Bioinformatics, 2012).
Reverse engineering of gene regulatory networks remains a central challenge in computational systems biology, despite recent advances facilitated by benchmark in silico challenges that have aided in calibrating their performance. Nonlinear dynamical models are particularly appropriate for this inference task, given the generation mechanism of the time-series data. We have introduced a novel nonlinear autoregressive model based on operator-valued kernels. A flexible boosting algorithm (OKVAR-Boost) that shares features from L2-boosting and randomization-based algorithms is developed to perform the tasks of parameter learning and network inference for the proposed model.
The OKVAR-Boost Matlab code refers to the following article : Lim et al., (2013) OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks. Bioinformatics 29 (11):1416-1423.
Kernel methods are well-established machine learning approaches that are able to learn complex relationships of data through high-dimensional Hilbert-spaces. Operator-valued kernel (OVK) methods extend these approaches into multi-dimensional output domains, where several target functions are predicted.
OPERA-lib will consist of various structured-learning machine learning methods utilising OVKs. The library is a Python module utilising standard open-source libraries Numpy, Scipy and Matplotlib, and is designed for compatibility to Scikit-learn machine learning library.
Non-stationary Gaussian process package for two-sample testing of time-series.
Download nsgp R package. Install as source package with