DebiChem Project
Summary
Analytical biochemistry
DebiChem Analytical BioChemistry

This metapackage will install packages which enable you to:

  • load and convert mass spectrometric data files;
  • edit biopolymer sequences;
  • elaborate complex mass spectrometry workflows;
  • perform protein database searches using tandem-ms data;
  • view and mine mass spectrometric data;

Description

For a better overview of the project's availability as a Debian package, each head row has a color code according to this scheme:

If you discover a project which looks like a good candidate for DebiChem to you, or if you have prepared an unofficial Debian package, please do not hesitate to send a description of that project to the DebiChem mailing list

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DebiChem Analytical biochemistry packages

Official Debian packages with high relevance

Libpwiz-tools
ProteoWizard command line tools
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The libpwiz library from the ProteoWizard project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs standard chemistry and LCMS dataset computations.

The primary goal of ProteoWizard is to eliminate the existing barriers to proteomic software development so that researchers can focus on the development of new analytic approaches, rather than having to dedicate significant resources to mundane (if important) tasks, like reading data files.

This package ships command line tools that include idconvert (conversion of MS identifications) and msconvert (conversion of MS raw data files from/to any supported format).

Please cite: M.C. Chambers, B. MacLean, R. Burke, D. Amode, D.L. Ruderman, S. Neumann, L. Gatto, B. Fischer, B. Pratt, J. Egertson, K. Hoff, D. Kessner, N. Tasman, N. Shulman, B. Frewen, T.A. Baker, M.-Y. Brusniak, C. Paulse, D. Creasy, L. Flashner, K. Kani, C. Moulding, S.L. Seymour, L.M. Nuwaysir, B. Lefebvre, F. Kuhlmann, J. Roark, P. Rainer, S. Detlev, T. Hemenway, A. Huhmer, J. Langridge, B. Connolly, T. Chadick, K. Holly, J. Eckels, E.W. Deutsch, R.L. Moritz, J.E. Katz, D.B. Agus, M. MacCoss, D.L. Tabb and P. Mallick: A cross-platform toolkit for mass spectrometry and proteomics.. (PubMed) Nature Biotechnology 30:918-920 (2012)
Lutefisk
de novo interpretation of peptide CID spectra
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Lutefisk performs a de novo interpreation of collision-induced decay (CID) spectra, providing the user with a file containing all the possible sequence candidates corresponding to the CID data.

Massxpert
linear polymer mass spectrometry software
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massXpert is a program to simulate and analyse mass spectrometric data obtained on linear (bio-)polymers. It is the successor of GNU polyxmass.

Four modules allow:

  • making brand new polymer chemistry definitions;
  • using the definitions to perform easy calculations in a desktop calculator-like manner;
  • performing sophisticated polymer sequence editing and simulations;
  • perform m/z list comparisons;

Chemical simulations encompass cleavage (either chemical or enzymatic), gas-phase fragmentations, chemical modification of any monomer in the polymer sequence, cross-linking of monomers in the sequence, arbitrary mass searches, calculation of the isotopic pattern...

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Mmass
Mass spectrometry tool for proteomics
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mMass is a free mass spectrum viewer/analyzer in which the following proteomics-related tasks can be performed:

  • Open raw text, mzXML and mzData mass spectra;
  • Define peak lists;
  • Powerful mass spectrum viewer (zoom, cursor...);
  • Data recalibration;
  • Protein-only simulations;
  • Online Mascot searches.

The software can be easily extended by additional Python modules. This package contains the platform-independent parts of the software.

Please cite: Martin Strohalm, Daniel Kavan, Petr Novák, Michael Volný and Vladimír Havlíček: mMass 3: A Cross-Platform Software Environment for Precise Analysis of Mass Spectrometric Data. (PubMed,eprint) Analytical chemistry (ACS) 82(11):4648-4651 (2010)
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Openms
package for LC/MS data management and analysis
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OpenMS is a package for LC/MS data management and analysis. OpenMS offers an infrastructure for the development of mass spectrometry-related software and powerful 2D and 3D visualization solutions.

TOPP (the OpenMS proteomic pipeline) is a pipeline for the analysis of HPLC/MS data. It consists of a set of numerous small applications that can be chained together to create analysis pipelines tailored for a specific problem.

This package is a metapackage that depends on both the libopenms library package (libOpenMS and libOpenMS_GUI) and the OpenMS Proteomic Pipeline (topp) package.

Please cite: Marc Sturm, Andreas Bertsch, Clemens Gröpl, Andreas Hildebrandt, Rene Hussong, Eva Lange, Nico Pfeifer, Ole Schulz-Trieglaff, Alexandra Zerck, Knut Reinert and Oliver Kohlbacher: OpenMS – an Open-Source Software Framework for Mass Spectrometry. (PubMed,eprint) BMC Bioinformatics 9(163) (2008)
Registry entries: Bio.Tools  SciCrunch  OMICtools 
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Python-mzml
mzML mass spectrometric data parsing
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python-mzml is an extension to Python that offers:

  • easy access to mass spectrometry (MS) data that allows the rapid development of tools;
  • a very fast parser for mzML data, the standard in mass spectrometry data format;
  • a set of functions to compare or handle spectra.
R-cran-maldiquant
GNU R package for quantitative analysis of mass spectrometry data
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MALDIquant provides a complete analysis pipeline for MALDI-TOF and other mass spectrometry data. Distinctive features include baseline subtraction using the SNIP algorithm, peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions.

Please cite: Sebastian Gibb and Korbinian Strimmer: MALDIquant: a versatile R package for the analysis of mass spectrometry data. (PubMed,eprint) Bioinformatics 28(17):2270-2271 (2012)
R-cran-maldiquantforeign
GNU R package providing import/export routines for MALDIquant
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The MALDIquantForeign package reads (tab, csv, Bruker fid, Ciphergen XML, mzXML, mzML, imzML, Analyze 7.5) and writes (tab, csv, msd, mzML) different file formats of mass spectrometry data into/from MALDIquant objects.

R-cran-mixtools
GNU R tools for analyzing finite mixture models
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The GNU R mixtools package is a collection of R functions for analyzing finite mixture models. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.

Please cite: Tatiana Benaglia, Didier Chauveau, David R. Hunter and Derek Young: mixtools: An R Package for Analyzing Finite Mixture Models. (eprint) Journal of Statistical Software 32(6):1-29 (2009)
R-cran-readbrukerflexdata
GNU R package to read Bruker Daltonics *flex format files
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The readBrukerFlexData package reads data files acquired by MALDI-TOF MS on Bruker Daltonics machines of the *flex series.

R-cran-readmzxmldata
GNU R package to read mass spectrometry data in mzXML format
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The readMzXmlData package contains functions for reading mass spectrometry data in mzXML format.

R-other-amsmercury
efficient calculation of accurate masses and abundances of isotopic peaks
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This GNU R package provides efficient calculation of accurate masses and abundances of isotopic peaks. It is a precondition for R NITPICK which does peak identification for mass spectrometry data.

R-other-curvefdp
estimation of confidence levels for peptide identifications
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This is a GNU R package for the Analytical Chemistry publication entitled 'Estimating the Confidence of Peptide Identifications without Decoy Databases'. The function curveFDR(scores) fits a gaussian mixture model to a score distribution of peptide identifications and thereby allows the estimation of confidence levels based on the false discvory proportion.

Please cite: Bernhard Y. Renard abd Wiebke Timm, Marc Kirchner, Judith A. J. Steen, Fred A. Hamprecht and Hanno Steen: Estimating the Confidence of Peptide Identifications without Decoy Databases. (eprint) Analytical Chemistry 82(11):4314-4318 (2010)
R-other-iwrlars
least angle regression, lasso, positive lasso and forward stagewise
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This GNU R package provides efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit. Least angle regression and infinitessimal forward stagewise regression are related to the lasso described in http://www-stat.stanford.edu/~hastie/Papers/#LARS.

This is a modified version of the original lars package by Hastie and Efron, providing a LARS modification for non-negative lasso.

R-other-nitpick
peak identification for mass spectrometry data
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This R package allows reliable extraction of features from mass spectra and helps in the automated analysis of proteomic mass spectrometry (MS) experiments.

This is the NITPICK implementation for peak picking in MS spectra.

Please cite: Bernhard Y Renard, Marc Kirchner, Hanno Steen, Judith AJ Steen and Fred A Hamprecht: NITPICK: peak identification for mass spectrometry data. (PubMed,eprint) BMC Bioinformatics 9:355 (2008)
Tandem-mass
mass spectrometry software for protein identification
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X! Tandem can match tandem mass spectra with peptide sequences, in a process that is commonly used to perform protein identification.

This software has a very simple, unsophisticated application programming interface (API): it simply takes an XML file of instructions on its command line, and outputs the results into an XML file, which has been specified in the input XML file. The output file format is described at http://www.thegpm.org/docs/X_series_output_form.pdf.

Unlike some earlier generation search engines, all of the X! Series search engines calculate statistical confidence (expectation values) for all of the individual spectrum-to-sequence assignments. They also reassemble all of the peptide assignments in a data set onto the known protein sequences and assign the statistical confidence that this assembly and alignment is non-random. The formula for which can be found here. Therefore, separate assembly and statistical analysis software, e.g. PeptideProphet and ProteinProphet, do not need to be used.

Official Debian packages with lower relevance

Libpwiz-dev
library to perform proteomics data analyses (devel files)
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The libpwiz library from the ProteoWizard project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs standard chemistry and LCMS dataset computations.

The primary goal of ProteoWizard is to eliminate the existing barriers to proteomic software development so that researchers can focus on the development of new analytic approaches, rather than having to dedicate significant resources to mundane (if important) tasks, like reading data files.

This package ships the library development files.

Please cite: M.C. Chambers, B. MacLean, R. Burke, D. Amode, D.L. Ruderman, S. Neumann, L. Gatto, B. Fischer, B. Pratt, J. Egertson, K. Hoff, D. Kessner, N. Tasman, N. Shulman, B. Frewen, T.A. Baker, M.-Y. Brusniak, C. Paulse, D. Creasy, L. Flashner, K. Kani, C. Moulding, S.L. Seymour, L.M. Nuwaysir, B. Lefebvre, F. Kuhlmann, J. Roark, P. Rainer, S. Detlev, T. Hemenway, A. Huhmer, J. Langridge, B. Connolly, T. Chadick, K. Holly, J. Eckels, E.W. Deutsch, R.L. Moritz, J.E. Katz, D.B. Agus, M. MacCoss, D.L. Tabb and P. Mallick: A cross-platform toolkit for mass spectrometry and proteomics.. (PubMed) Nature Biotechnology 30:918-920 (2012)

Packaging has started and developers might try the packaging code in VCS

Biceps
error-tolerant peptide identification
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BICEPS is tool for the error-tolerant identification of peptides based on a statistical regularization scheme. It balances possible improvements in peptide-spectrum-matches by allowing substitutions against the increased risk of false positives. BICEPS can identify peptides containing two or more substitutions as occuring e.g. in cross-species searches.

Please cite: Bernhard Y. Renard‡and Buote Xu, Marc Kirchner, Franziska Zickmann‡and Dominic Winter, Simone Korten‡and Norbert W. Brattig‡and Amit Tzur, Fred A. Hamprecht and Hanno Steen: Overcoming Species Boundaries in Peptide Identification with Bayesian Information Criterion-driven Error-tolerant Peptide Search (BICEPS). (PubMed,eprint) Mol Cell Proteomics ;11(7):M111.014167 (2012)
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