Recherche

Darn! is a RNA motif search tool. It finds all the subsequences that match a specified motif on some input genomic sequence(s).

Darn! uses a weighted constraint solver to localize the portions of a genomic sequence that match a motif. The motif is expressed in a specific language (see section 5). Some motif descriptors are provided with the software for FMN, Lysine, RNaseP, SAM, snoRNA C/D-box and tRNA search.

The predictions could be available in GFF, FASTA and other formats (see section 4).

Darn! can be use on line at this address: http://carlit.toulouse.inra.fr/Darn.

Darn! was developped by Matthias Zytnicki during his PhD in the BIA lab at INRA Toulouse, Franc

RNAspace is an environment that allows to create web sites dedicated to non-protein-coding RNA (ncRNA) prediction, annotation and analysis. The web sites allow users to run a variety of tools in an integrated and flexible way. RNAspace is focused on the integration of complementary ncRNA gene finders. It also offers a set of tools for the comparison, visualization, edition and export of ncRNAs candidates. Predictions can be filtered according to a large set of characteristics.

A public web site http://rnaspace.org has been created that allows for on line annotation of a complete bacterial genome or a small eukaryotic chromosome. It has been conceived to be as parameterizable and extensible as possible.

This allows to configure web sites for special uses:

• A site dedicated to a species with limited accesses, a site shared by a group of biologists ...
• Parametrization of a site includes declaration of available gene-finders, limits for process time execution, disk space, storage duration, execution on a connected computer cluster via a job scheduler.
• It is also worth to note that the environment could be used in command line and thus inserted in a pipeline.

VLE is a multi-modelling and simulation platform. It is a powerful modeller and a simulator supporting the use of different formalisms for the specification of models and implementing the corresponding solvers in a unified manner.

In addition to the classical use of one single formalism for modelling and simulation, VLE integrates, i.e. couples, heterogeneous formalisms in one coherent simulation model. VLE supports the new generation of inter- disciplinary simulation models…

VLE is a powerful modeller and a simulator supporting the use of different formalisms for the specification of models and implementing the corresponding solvers in a unified manner. VLE proposes a lot of formalisms called DEVS extensions: Difference equation, Differential equation for the resolution of differential equation systems with QSS, Euler, Runge Kutta methods, Finite State Automate, High level Petri net, CellDEVS, CellQSS and a Decision making system.

Based on the DEVS theory, we ensure the compatibility of models and DEVS extensions at formal and operational levels.

IN WHICH CONTEXT YOU CAN USE FILTREX ?

In the microbiological context of modelisation of complex microbiological dynamic systems characterised by:

• One or several bacteria species leading eventually to several simultaneous dynamic equations.
• The growth or the inactivation are not directly observable.
• Sophisticated dilution and counting stepwise processes associated to several experimental errors.

FILTREX OBJECTIVES

• Parameter identification of the growth and inactivation models.
• Comparison and selection of these models.
• Optimal sequential sampling of experiments (three options).

FILTREX MATHEMATICAL FRAMEWORK

• A nonlinear particle filtering method based on a new nonlinear particular (nonparametrical) filtering technique using a convolution kernel approach and a particular resampling trick.
• Nonlinear autoregressive dynamic systems simultaneously defined by a stochastic state equation and an observation equation.
• Not directly observable systems.
• Non explicite likelihood function.

FILTREX ADVANTAGES

• No initial guesses for parameters are needed: postulated parameter intervals are only necessary (they can be broad in a first step if only very few information is available on parameters).
• The experimental errors (samplings, countings, ...) are better taken into account, and their coefficients of variation can be also estimated.
• It is based on published theoretical results (convergence, ...).
• In further releases, several species will be simultaneously considered.

The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.

GMDPtoolbox proposes functions related to Graph-based Markov Decision Processes (GMDP). The framework allows to represent and approximately solve Markov Decision Processes (MDP) problems with an underlying spatial structure allowing a factored representation. A factored representation can be a solution for problems too large to be represented and solved by classical MDP tools. For smaller size problems, we suggest to use the MDP toolbox.

The GPareto package for R provides multi-objective optimization algorithms for expensive black-box functions and uncertainty quantification methods. Popular methods such as EGO in the mono-objective case relies on Gaussian processes or Kriging to build surrogate models.

Driven by the prediction uncertainty given by these models, several infill criteria have also been proposed in a multi-objective setup to select new points sequentially and efficiently cope with severely limited evaluation budgets.

They are implemented in the package, in addition with estimation of the whole Pareto front location and uncertainty quantification visualization in the design and objective spaces. Finally, it attempts to fill the gap between expert use of the corresponding methods and simple usage, where many efforts have been put on providing graphical visualization, standard tuning and interactivity.

This work focuses on the issue of variable selection in functional regression. Unlike most work in this framework, our approach does not select isolated points in the definition domain of the predictors, nor does it rely on the xpansion of the predictors in a given functional basis. It provides an approach to select full intervals made of consecutive points.

This feature improves the interpretability of the estimated coefficients and is desirable in the functional framework for which small shifts are frequent when comparing one predictor (curve) to another.

Our method is described in a semiparametric framework based on Sliced Inverse Regression (SIR). SIR is an effective method for dimension reduction of high-dimensional data which computes a linear projection of the predictors in a low-dimensional space, without loss on regression information.

We extend the approaches of variable election developed for multidimensional SIR to select intervals rather than separated evaluation points in the definition domain of the functional predictors. Different and equivalent formulations of SIR are combined in a shrinkage approach with a group-Lasso-like penalty.

Finally, a fully automated iterative procedure is also proposed to find the critical (interpretable) intervals. The approach is proved efficient on simulated and real data.

The GenoToul bioinformatics facility is part of the Genotoul GIS. It has been set up in 2000. Since 2009, it is one of the 13 IBISA bioinformatics platforms. Since 2008, the plateform collaborates with the local genomic platform and processes huge volumes of data produced by second and third generation of sequencers and makes them available to biologists (ng6).

The GenoToul bioinformatics facility is part of the Genotoul GIS. It has been set up in 2000. Since 2009, it is one of the 13 IBISA bioinformatics platforms. Since 2008, the plateform collaborates with the local genomic platform and processes huge volumes of data produced by second and third generation of sequencers and makes them available to biologists (ng6).

This paper presents SOMbrero, a new R package for self-organizing maps. Along with the standard SOM algorithm for numeric data, it implements self-organizing maps for contingency tables (“Korresp”) and for dissimilarity data (“relational SOM”), all relying on stochastic (i.e., on-line) training. It offers many graphical outputs and diagnostic tools, and comes with a user-friendly web graphical interface, based on the shiny R package.

Background

Roche 454 pyrosequencing platform is often considered the most versatile of the Next Generation Sequencing technology platforms, permitting the sequencing of large genomes, the analysis of variations or the study of transcriptomes. A recent reported bias leads to the production of multiple reads for a unique DNA fragment in a random manner within a run. This bias has a direct impact on the quality of the measurement of the representation of the fragments using the reads. Other cleaning steps are usually performed on the reads before assembly or alignment.

Findings

PyroCleaner is a software module intended to clean 454 pyrosequencing reads in order to ease the assembly process. This program is a free software and is distributed under the terms of the GNU General Public License as published by the Free Software Foundation. It implements several filters using criteria such as read duplication, length, complexity, base-pair quality and number of undetermined bases. It also permits to clean flowgram files (.sff) of paired-end sequences generating on one hand validated paired-ends file and the other hand single read file.

Conclusions

Read cleaning has always been an important step in sequence analysis. The pyrocleaner python module is a Swiss knife dedicated to 454 reads cleaning. It includes commonly used filters as well as specialised ones such as duplicated read removal and paired-end read verification.

Background

Next generation sequencing platforms are now well implanted in sequencing centres and some laboratories. Upcoming smaller scale machines such as the 454 junior from Roche or the MiSeq from Illumina will increase the number of laboratories hosting a sequencer. In such a context, it is important to provide these teams with an easily manageable environment to store and process the produced reads.

Results

We describe a user-friendly information system able to manage large sets of sequencing data. It includes, on one hand, a workflow environment already containing pipelines adapted to different input formats (sff, fasta, fastq and qseq), different sequencers (Roche 454, Illumina HiSeq) and various analyses (quality control, assembly, alignment, diversity studies,…) and, on the other hand, a secured web site giving access to the results. The connected user will be able to download raw and processed data and browse through the analysis result statistics. The provided workflows can easily be modified or extended and new ones can be added. Ergatis is used as a workflow building, running and monitoring system. The analyses can be run locally or in a cluster environment using Sun Grid Engine.

Conclusions

NG6 is a complete information system designed to answer the needs of a sequencing platform. It provides a user-friendly interface to process, store and download high-throughput sequencing data.

The function selectFast selects a graph within the (data-driven) families of graphs EW, C01, and LA. The function selectQE selects a graph within the family of graphs QE and the function selectMyFam within a given family of graphs.
Other functions:
- simulateGraph generates a sparse (non-uniform) Gaussian Graphical Model; The covariance matrix can be used to generate input for the main functions.
- penalty, the penalty function.
- convertGraph converts graphs into adjacency matrices.

Introduction

The massive sequencing cost decrease has attracted a large community of new users, some of them studying organisms for which the reference genome sequence is still not available. When trying to understand mechanisms taking place at the gene level they usually would start with a de novo transcriptome assembly approach. Software packages such as Trinity or Oases are mature enough to produce reliable contigs from short reads. The analyses performed on the assembled contigs generate a large amount of heterogeneous results including variations, functional annotation and expression measurements. The processing steps of these pipelines are usually shared inside the community but the parameters, the tools and the reference databases used are specific. The results are often provided throught a WEB server including a BLAST query form and download links.

Cbrowse is a WEB environment presenting this kind of results throught graphical views and query forms. The functional annotation part is not implemented yet and the query possibilities are very limited. The Galaxy engine provides users with an interface to create and track workflow executions. It already embeds RNA-Seq analysis and assembly components. However, none of them offers user-friendly query and vizualisation features designed for RNA-Seq de novo annotation.

In its last version (0.8), biomart is developed as an easily extensible query infrastructures which can be specialized in the presentation of focused data types. On top of the database and beside the proposed query forms it is possible to add new pages as plug-ins in order to present data in a user-friendly way.

Results

RNAbrowse permits sequencing facilities and, even small, bioinformatic teams to give a user-friendly access to RNA-Seq de novo results, helping biologists to analyse and extract meaningful information from their data. RNAbrowse includes two components: a web-based user interface and an administration command line tool presented here-after.