biology

Software (mostly free software) useful to researchers and teachers in biology
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 19/11/13
  • Minor correction: 19/11/13

ScientiFig : create publication-ready scientific figures

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 2.6 - 02/10/2013
  • License(s): BSD -
    ScientiFig uses the Apache BATIK and XML-apis libraries as well as the BSD Rsession library.
  • Status: beta release
  • Support: maintained, no ongoing development
  • Designer(s): Benoit Aigouy
  • Contact designer(s): Benoit Aigouy
  • Laboratory, service:

 

General software features

Scientists often build figures for publications and talks. To create these figures, they usually rely on powerful tools that are designed for graphic designers to produce artistic figures and are therefore only poorly suited to build scientific figures.

We here present an ImageJ/FIJI plugin called ScientiFig that is devoted to the building of research figures. Our tool can assemble and maintain complex panels containing images with different aspect ratios and associate scalebars, text annotations and ROIs to these panels. Interestingly, our software will always preserve the position of these associated elements even when figure size changes. ScientiFig can export figures as png with a transparent background for a better integration in office documents and as vector graphics that can be finalized using a vector graphics editor. Last but not least, ScientiFig can format figures for various scientific reviews and for example offer to substitute fonts or to resize the figure to better match the journal guidelines (if a journal style does not exist, it can be created using the embedded editor).

For comparison, please find below two alternative tools:

Context in which the software is used

ScientiFig is a tool to buid and format images for scientific publications.

Publications related to the software

ScientiFig: a tool to build publication-ready scientific figures. Aigouy B, Mirouse V. Nat Methods. 2013 Oct 30;10(11):1048. doi: 10.1038/nmeth.2692.

Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 24/04/13
  • Minor correction: 24/04/13

X!TandemPipeline : edit, filter, merge and group your peptide/protein identifications from MS/MS mass spectra

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 3.3.0 - 7 juillet 2012
  • License(s): GPL - v3
  • Status: stable release
  • Support: maintained, ongoing development
  • Designer(s): Benoit Valot, Olivier Langella
  • Contact designer(s): olivier.langella@moulon.inra.fr
  • Laboratory, service:

 

General software features

X!TandemPipeline is a free software (GPL v3) that helps you to filter and group your peptide/protein identifications from MS/MS mass spectra.

Main features :

Context in which the software is used

X!TandemPipeline is designed for a day use by biologists. It performs MS identifications, filters and groups huge data very quickly.

Publications related to the software

Ludovic Bonhomme, Benoit Valot, Francois Tardieu, Michel Zivy. “Phosphoproteome Dynamics Upon Changes in Plant Water Status Reveal Early Events Associated with Rapid Growth Adjustment in Maize Leaves.” Molecular & Cellular Proteomics: MCP (July 10, 2012). http://www.ncbi.nlm.nih.gov/pubmed/22787273.

Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 18/04/13
  • Minor correction: 18/04/13

SVDetect : a tool to detect genomic structural variations from paired-end and mate-pair sequencing data

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 0.8 - 05/12/2011
  • License(s): GPL
  • Status: stable release
  • Support: maintained, no ongoing development
  • Designer(s): Bruno Zeitouni, Valentina Boeva
  • Contact designer(s): svdetect@curie.fr
  • Laboratory, service:

 

General software features

From NGS paired sequences and mapped onto a reference genome, SVDetect allows you to detect clusters of anomanously mapped pairs (with abnormal order, strand orientation or insert size of fragments), and to predict structural variants (SVs) such as large insertions, deletions, inversions, duplications or intra/inter-chromosomal translocations. SVDetect can also compare the results of SVs from different samples and to identify specific-sample SVs (Tumoral DNA vs Control DNA, for example).
SVDetect is compatible with any type of paired reads ("paired-end" or "mate-pair"), sequencing technology (Illumina, SOLiD, PGM, ...), or type of genome.
SVDetect can compute coverage profiles and to reveal loss or gains of genomic regions from the copy-number information.
It is available into a PERL Script and takes the BAM format as input.
SVDetect is also available at the Galaxy toolshed.

Context in which the software is used

SVDetect is an application for the isolation and the type prediction of intra- and inter-chromosomal rearrangements from paired-end/mate-pair sequencing data provided by the high-throughput sequencing technologies.
It was primarily tested in the context of whole genome resequencing projects from cancer cells, rich in chromosomal rearrangements.
SVDetect can also detect fusion genes from RNA-seq experiments.

Publications related to the software
  • SVDetect: a tool to identify genomic structural variations from paired-end and mate-pair sequencing data
    Bruno Zeitouni; Valentina Boeva; Isabelle Janoueix-Lerosey; Sophie Loeillet; Patricia Legoix-ne; Alain Nicolas; Olivier Delattre; Emmanuel Barillot, Bioinformatics 2010 26: 1895-1896, http://www.hal.inserm.fr/inserm-00508372
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 29/08/12
  • Minor correction: 29/08/12

PFIM : population design evaluation and optimisation

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: PFIM INTERFACE 3.1 and PFIM 3.2.2 - March 2011
  • License(s): GPL
  • Status: stable release, under development
  • Support: maintained, ongoing development
  • Designer(s): Caroline Bazzoli, Thu Thuy Nguyen, Anne Dubois, Sylvie Retout, Emanuelle Comets, HervĂ© Le Nagard, France MentrĂ©
  • Contact designer(s): caroline.bazzoli@imag.fr, thu-thuy.nguyen@inserm.fr, france.mentre@inserm.fr
  • Laboratory, service:

 

General software features

PFIM (Population Fisher Information Matrix) is a R function dedicated to evaluation and optimisation of designs (number of subjects, number of samples per subject and their allocation in time) for nonlinear mixed effects models (population approach). This function is based on the developpement of an approximation of the Fisher information matrix in these models.

Two latest versions of PFIM are currently available :

  • a graphical user interface package using the R software (PFIM Interface 3.1),
  • an R script version (PFIM 3.2.2) requiring some knowledge in R use but which benefits of the latest methodological developments performed in the research team.
Context in which the software is used

PFIM is mainly used to design informative studies in pharmacology for the analyses of dose-concentration-effect (pharmacokinetics/pharmacodynamics) relationtionships of drugs.

PFIM is subject to statistical research (developement of the Fihser Infromation matrix, UMR 738 INSERM-Université Paris-Diderot) but also to pharmacology research.

Publications related to the software

Methodology

  • Nguyen TT, Bazzoli C, MentrĂ© F. Design evaluation and optimisation in crossover pharmacokinetic studies analysed by nonlinear mixed effects models, Nguyen TT, Bazzoli C, MentrĂ© F, 2011, [Epub ahead of print].
  • Bazzoli C, Retout S, MentrĂ© F. Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0, Computer Methods and Programs in Biomedicine, 2010, 98 : 55-65.
  • Retout S, Comets E, Bazzoli C, MentrĂ© F. Design optimisation in nonlinear mixed effects models using cost functions:application to a joint model of infliximab and methotrexate pharmacokinetics, Communication in Statistics: Theory and Methods, 2009, 38 : 3351–3368.
  • Bazzoli C, Retout S, MentrĂ© F. Fisher information matrix for nonlinear mixed effects multiple response models: evaluation of the appropriateness of the first order linearization using a pharmacokinetic/pharmacodynamic model, Statistics in Medicine, 2009, 28 : 1940-1956.
  • Retout S, Comets E, Samson A, MentrĂ© F. Design in nonlinear mixed effects models: optimization using the Fedorov-Wynn algorithm and power of the Wald test for binary covariates, Statistics in Medicine, 2007, 26: 5162-5179.
  • Retout S, MentrĂ© F. Optimisation of individual and population designs using Splus, Journal of Pharmacokinetic and Pharmacodynamics, 2003, 30: 417-443.
  • Retout S, MentrĂ© F. Further developments of the Fisher information matrix in nonlinear mixed-effects models with evaluation in population pharmacokinetics, Journal of Biopharmaceutical Statistics, 2003, 13: 209-227.
  • Retout S, MentrĂ© F, Bruno R. Fisher information matrix for nonlinear mixed-effects models: evaluation and application for optimal design of enoxaparin population, Statistics in Medicine, 2002, 21: 2623-2639.
  • Retout S, Duffull S, MentrĂ© F. Development and implementation of the population Fisher information matrix for evaluation of population pharmacokinetic designs, Computer Methods and Programs in Biomedicine, 2001, 65: 141-151.
  • MentrĂ© F, Mallet A, Baccar D. Optimal design in random-effects regression models, Biometrika, 1997, 84 : 429-442.

Applications of PFIM

  • Delavenne X, Zufferey P, Nguyen P, Rosencher N, Samama C.M,Bazzoli C, Mismett P, Laporte S. Pharmacokinetics of fondaparinux 1.5 mg once daily in a real-world cohort of patients with renal impairment undergoing major orthopaedic surgery. Pharmacokinetics and disposition, 2012, [Epub ahead of print].
  • Sherwin CM, Ding L, Kaplan J, Spigarelli MG, Vinks AA. Optimal study design for pioglitazone in septic pediatric patients. Journal of Pharmacokinetics and Pharmacodynamics, 2011, 38 : 433-447.
  • Guedj J, Bazzoli C, Neuman A.U, MentrĂ© F. Design evaluation and optimization for models of hepatitis C
    viral dynamics. Statistics in Medicine, 2011, 30 : 1045-1056.
  • Bazzoli C, Retout S, MentrĂ© F. Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0, Computer Methods and Programs in Biomedicine, 2010, 98 : 55-65.
  • Retout, S., Comets, E., Bazzoli, C. et MentrĂ©, F. Design optimisation in nonlinear mixed effects models using cost functions: application to a joint model of infliximab and methotrexate pharmacokinetics, Communication in
    Statistics: Theory and Methods, 2009, 38 : 3351–3368.
  • Bazzoli C, Retout S, MentrĂ© F. Fisher information matrix for nonlinear mixed effects multiple response models: evaluation of the appropriateness of the first order linearization using a pharmacokinetic/pharmacodynamic model, Statistics in Medicine, 2009, 28 : 1940-1956.
  • Retout S, Comets E, Samson A, MentrĂ© F. Design in nonlinear mixed effects models: optimization using the Fedorov-Wynn algorithm and power of the Wald test for binary covariates, Statistics in Medicine, 2007, 26 : 5162-5179.
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 20/01/12
  • Minor correction: 20/01/12

QTLMap : detection of QTL from experimental designs in outbred population

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 0.8.3 - 14 october 2010
  • License(s): CeCILL
  • Status: beta release
  • Support: maintained, ongoing development
  • Designer(s): Pascale Le Roy, Jean-Michel Elsen, Helene Gilbert, Carole Moreno, Andres Legarra, Olivier Filangi
  • Contact designer(s): olivier.filangi@rennes.inra.fr
  • Laboratory, service:

 

General software features

Description

QTLMap is a software dedicated to the detection of QTL from experimental designs in outbred population. QTLMap software is developed by the Animal Genetics Division at INRA (French National Institute for Agronomical Research). The statistical techniques used are linkage analysis (LA) and linkage disequilibrium linkage analysis (LDLA) using interval mapping. Different versions of the LA are proposed from a quasi Maximum Likelihood approach to a fully linear (regression) model. The LDLA is a regression approach (Legarra and Fernando, 2009). The population may be sets of half-sib families or mixture of full- and half- sib families. The computations of Phase and Transmission probabilities are optimized to be rapid and as exact as possible. QTLMap is able to deal with large numbers of markers (SNP) and traits (eQTL).

Functionnalities

  • QTL detection in half-sib families or mixture of full- and half-sib families
  • One or several linked QTL segregating in the population
  • Single trait or multiple trait
  • Nuisance parameters (e.g. sex, batch, weight...) and their interactions with QTL can be included in the analysis
  • Gaussian, discrete or survival (Cox model) data
  • Familial heterogeneity of variances (heteroscedasticity)
  • Can handle eQTL analyses
  • Computation of transmission and phase probabilities adapted to high throughput genotyping (SNP)
  • Empirical thresholds are estimated using simulations under the null hypothesis or permutations of trait values
  • Computation of power and accuracy of your design or any simulated design
Context in which the software is used

QTLMap source code is available under the CeCILL version 2.0 license, a GPL like license.

Utilisateurs

This software is used by genetic researchers to detect a region of the genome that controls an agronomic trait

Cluster Infrastructures

Software dependencies

Installation

  • Suite gcc (>=4.4)
  • CMake 2.6.4

Support

Users mailing list : inscription

Publications related to the software

Legarra A, Fernando RL, 2009. Linear models for joint association and linkage QTL mapping. Genet Sel Evol., 41:43.

Elsen JM, Filangi O, Gilbert H, Le Roy P, Moreno C, 2009. A fast algorithm for estimating transmission probabilities in QTL detection designs with dense maps. Genet Sel Evol., 41:50.

Gilbert H., Le Roy P., Moreno C., Robelin D., Elsen J. M., 2008. QTLMAP, a software for QTL detection in outbred population. Annals of Human Genetics, 72(5): 694.

Gilbert H, Le Roy P., 2007. Methods for the detection of multiple linked QTL applied to a mixture of full and half sib families. Genet Sel Evol., 39(2):139-58.

Moreno C.R., Elsen J.M., Le Roy P., Ducrocq V., 2005. Interval mapping methods for detecting QTL affecting survival and time–to–event phenotypes. Genet. Res. Camb., 85 : 139-149.

Goffinet B, Le Roy P, Boichard D, Elsen JM, Mangin B, 1999. Alternative models for QTL detection in livestock. III. Heteroskedastic model and models corresponding to several distributions of the QTL effect.. Genet. Sel. Evol., 31, 341-350.

Mangin B, Goffinet B, Le Roy P, Boichard D, Elsen JM, 1999. Alternative models for QTL detection in livestock. II. Likelihood approximations and sire marker genotype estimations. Genet. Sel. Evol., 31, 225-237.

Elsen JM, Mangin B, Goffinet B, Boichard D, Le Roy P, 1999. Alternative models for QTL detection in livestock. I. General introduction. Genet. Sel. Evol., 31, 213-224

Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 14/12/11
  • Minor correction: 07/09/13

pyFAI : azimuthal integration for 2D detectors

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 0.9.0 - July 30; 2013
  • License(s): GPL
  • Status: stable release
  • Support: maintained, ongoing development
  • Designer(s): JĂ©rĂ´me Kieffer (Python code), Dimitris Karkoulis (OpenMP & OpenCL), Peter Bösecke (Geometry), V. Armando SolĂ© (Image processing), Manuel Sánchez del RĂ­o (Original idea), Jonathan P. Wright (Ideas), FrĂ©dĂ©ric Emmanuel Picca (Documentation and ideas)
  • Contact designer(s): Jerome.Kieffer@esrf.fr
  • Laboratory, service:

 

General software features

PyFAI is a Python library for azimuthal integration; it allows the conversion of diffraction images taken with 2D detectors like CCD cameras into X-Ray powder patterns that can be used by other software like Rietveld refinement tools (i.e. FullProf), phase analysis or texture analysis.

As PyFAI is a library, its main goal is to be integrated in other tools like PyMca or EDNA. To perform online data analysis, the precise description of the experimental setup has to be known. This is the reason why PyFAI includes geometry optimization code working on "powder rings" of reference samples. Alternatively, PyFAI can also import geometries fitted with other tools like Fit2D.

PyFAI has been designed to work with any kind of detector with any geometry (transmission, reflection, off-axis, ...). It uses the Python library Fabio to read most images taken by diffractometer (Fabio officially supports 12 manufacturers and 20 different image formats).

Context in which the software is used

2D detectors (CCD, CMOS or pixel detectors, ...) have progressively replaced punctual detectors over the 15 last years in the world of diffraction (single crystal, powder diffraction WAXS or small angle scattering SAXS). Those detectors, with wide sensitive area, have spatial resolution of dozens of microns and provide millions of pixels. PyFAI can be used on SAXS and WAXS data to reduce them into 1D (azimuthal integration) or 2D (transformation known as caking).

In order to transform detector images into data to be used by scientists it is necessary to:

  • subtract dark current (correction for the read-out noise)
  • divide by flat-field (correction for the relative sensitivity of pixels or scintillator inhomogeneities)
  • correct for the pixel position (defects of the optical fiber taper)
  • mask out dead pixels
  • convert pixel position from Cartesian space (x,y) to Polar space (2theta, chi)

PyFAI is able to compute all those corrections. Special care has been taken to conserve intensity and surface density by pixel splitting during the re-binning process (which is close to a histogram, but with pixel splitting). The algorithm used is implemented in numpy to provide a bullet-proof version, but faster and more precise version have been implemented in Cython and in OpenCL to achieve best performances with modern graphic cards.

An additional piece of software allows a fast and reliable calibration of the geometry of the experimental setup; allowing online analysis of the data.

Publications related to the software
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 18/11/11
  • Minor correction: 22/02/12

Ed'Nimbus : DNA sequence filtering

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 1.0 - july 2006
  • License(s): not yet chosen -
  • Status: stable release
  • Support: not maintained, no ongoing development
  • Designer(s): Pierre Peterlongo
  • Contact designer(s): Pierre.Peterlongo @ univ-mlv.fr
  • Laboratory, service:

 

General software features

The goal of this filter of DNA sequences is to filter sequences to extract some multiple repeats that full fill the specifications given by users. Ed'Nimbus can be used to find at least two repeats in a sequence, but also to find repeats in a set of sequences.

Context in which the software is used

Project HELIX of INRIA.

Publications related to the software
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 18/11/11
  • Minor correction: 18/11/11

Minbrkpts : performance testing of heuristics for the linearization of partial orders

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • System:
  • Current version: minbrkpts-1.0.0 - september 2006
  • License(s): not yet chosen - You can contact the author to get the binary file.
  • Status: internal use
  • Support: not maintained, no ongoing development
  • Designer(s): Pierre Guillon, Guillaume Blin
  • Contact designer(s): Pierre.Guillon @ univ-mlv.fr
  • Laboratory, service:

 

General software features

Linearization of partial orders.

Context in which the software is used

To get complexity results and to study the algorithmics of the problem of linearization of partial orders.
With this software we have validated research results of the following publications.

Publications related to the software
  • Guillaume Blin, Eric Blais, Pierre Guillon, Mathieu Blanchette, and Nadia El-Mabrouk. Inferring gene orders from gene maps using the breakpoint distance. In, Guillaume Bourque, Nadia El-Mabrouk, editors, 4th Annual RECOMB Satellite Workshop on Comparative Genomics (RECOMB-CG'06). vol. 4205. LNBI. Montreal, Quebec. September 2006. pp. 99--112 Springer-Verlag.
  • Guillaume Blin, Eric Blais, Danny Hermelin, Pierre Guillon, Mathieu Blanchette, and Nadia El-Mabrouk. Gene maps linearization using genomic rearrangement distances. Journal of Computational Biology. Vol. 14. (4). 2007. pp. 394--407.
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 16/11/11
  • Minor correction: 16/11/11

NemoFish : three-dimensional fluorescence in situ hybridization (3D-FISH)

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 1.5 - 2009/12/07
  • License(s): Other - Creatice Commons V2 (freeware, not open source)
  • Status: stable release
  • Support: maintained, ongoing development
  • Designer(s): Eddie Iannuccelli, Thomas Boudier
  • Contact designer(s): eddie.iannuccelli@toulouse.inra.fr
  • Laboratory, service:

 

General software features

Three-dimensional fluorescence in situ hybridization (3D-FISH) is used to study the organization and the positioning of chromosomes or specific sequences such as genes or RNA in cell nuclei. Many different programs (commercial or free) allow image analysis for 3D-FISH experiments. One of the more efficient open-source programs for automatically processing 3D-FISH microscopy images is Smart 3D-FISH, an ImageJ plug-in designed to automatically analyze distances between genes. One of the drawbacks of Smart 3D-FISH is that it has a rather basic user interface and produces its results in various text and image files thus making the data post-processing step time consuming. We developed a new Smart 3D-FISH graphical user interface, NEMO, which provides all information in the same place so that results can be checked and validated efficiently. NEMO gives users the ability to drive their experiments analysis in either automatic, semi-automatic or manual detection mode. We also tuned Smart 3D-FISH to better analyze chromosome territories.

Context in which the software is used

Three-dimensional fluorescence in situ hybridization (3D-FISH)

Publications related to the software

NEMO: a tool for analyzing gene and chromosome territory distributions from 3D-FISH experiments
E. Iannuccelli; F. Mompart; J. Gellin; Y. Lahbib-Mansais; M. Yerle; T. Boudier
Bioinformatics 2010; doi: 10.1093/bioinformatics/btq013

Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 03/10/11
  • Minor correction: 08/11/11

masschroq : analysis and quantification of mass spectrometry data

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 1.1 - September 2011
  • License(s): GPL
  • Status: stable release
  • Support: maintained, ongoing development
  • Designer(s): Olivier Langella, Edlira Nano, BenoĂ®t Valot et Michel Zivy.
  • Contact designer(s): enano@moulon.inra.fr
  • Laboratory, service:

 

General software features

MassChroQ (Mass Chromatogram Quantification) is an open source software that performs quantification of mass spectrometry data, in paticular it has been designed to handle the analysis of proteomics data obtained from Liquid Chromatography - Mass Spectrometry (LC-MS) techniques. It can quantify a given list of mass over charge (m/z) values or all the identified peptides of your LC-MS data by performing retention time alignment, XIC extraction, peak detection and peak area quantification on them.

MassChroQ is very versatile: you can fully configure its parameters to best fit your data. You can quantify label-free data as well as isotopic labeled ones, data coming from low resolution spectrometers as well as high resolution ones, take into account complex data treatments as peptide or protein fractionation prior to LC-MS analysis (SCX, SDS-PAGE, ...) etc.
In addition, it is fast (takes less than 4 minutes to fully analyse 1Go of data), it is not greedy (uses at most 400 Mo of RAM at the time) and can perform automatical analysis of different groups of data samples in one shot.

MassChroQ uses and produces only open data formats : mzXML, mzML, gnumeric, csv and ods. It works by providing an input file in the masschroqML (XML) format, where the user indicates the data files to analyse (mzXML or mzML files) and the different analysis and corresponding parameters to perform on them. Sample masschroqML files are available online, you can edit and adapt them to your analysis. The identified peptides can be automatically filled in the masschroqML file in two ways: if you use X!Tandem as your identification engine, you can try the "X!Tandem pipeline" (http://pappso.inra.fr/bioinfo/xtandempipeline/) which performs peptide identification via X!Tandem, filtering and allows you to directly export the results in a masschroqML format. Otherwise, you can provide csv files containing the identified peptides (see the MassChroQ manual on how to do this).

MassChroQ is freely avalable under the GNU General Public Licence version 3 from http://pappso.inra.fr/bioinfo/masschroq/.

Context in which the software is used

MassChroQ is used in our proteomics research laboratory mainly to quantify the identified peptides of data analysed by LC-MS (Liquid Chromatography - Mass Spectrometry), with or without isotopic labelling, and sometimes with fractionation. The .raw data files coming directly from the spectrometers are first converted to mzXML or mzML formats. The peptides they contain are then identified (with the X!Tandem pipeline for example). Then the user edits a masschroqML file where he indicates the file path to the data to analyse together with the alignment, signal filtering and detection parameters he wants to use. Finally he launches MassChroQ on this file. Once MassChroQ has finished analysing the user can directly begin statistical processing on the output data. For more precise information a manual, a FAQ and various examples can be found on the software's homepage.

Publications related to the software
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