MSQuant, an Open Source Platform for Mass Spectrometry-Based Quantitative Proteomics

TY - JOUR

T1 - MSQuant, an Open Source Platform for Mass Spectrometry-Based Quantitative Proteomics

AU - Mortensen, Peter

AU - Gouw, Joost W

AU - Olsen, Jesper V

AU - Ong, Shao-En

AU - Rigbolt, Kristoffer T G

AU - Bunkenborg, Jakob

AU - Cox, Ju¨rgen

AU - Foster, Leonard J

AU - Heck, Albert J R

AU - Blagoev, Blagoy

AU - Andersen, Jens S

AU - Mann, Matthias

PY - 2010/1/4

Y1 - 2010/1/4

N2 - Mass spectrometry-based proteomics critically depends on algorithms for data interpretation. A current bottleneck in the rapid advance of proteomics technology is the closed nature and slow development cycle of vendor-supplied software solutions. We have created an open source software environment, called MSQuant, which allows visualization and validation of peptide identification results directly on the raw mass spectrometric data. MSQuant iteratively recalibrates MS data thereby significantly increasing mass accuracy leading to fewer false positive peptide identifications. Algorithms to increase data quality include an MS3 score for peptide identification and a post-translational modification (PTM) score that determines the probability that a modification such as phosphorylation is placed at a specific residue in an identified peptide. MSQuant supports relative protein quantitation based on precursor ion intensities, including element labels (e.g., 15N), residue labels (e.g., SILAC and ICAT), termini labels (e.g., 18O), functional group labels (e.g., mTRAQ), and label-free ion intensity approaches. MSQuant is available, including an installer and supporting scripts, at http://msquant.sourceforge.net.

AB - Mass spectrometry-based proteomics critically depends on algorithms for data interpretation. A current bottleneck in the rapid advance of proteomics technology is the closed nature and slow development cycle of vendor-supplied software solutions. We have created an open source software environment, called MSQuant, which allows visualization and validation of peptide identification results directly on the raw mass spectrometric data. MSQuant iteratively recalibrates MS data thereby significantly increasing mass accuracy leading to fewer false positive peptide identifications. Algorithms to increase data quality include an MS3 score for peptide identification and a post-translational modification (PTM) score that determines the probability that a modification such as phosphorylation is placed at a specific residue in an identified peptide. MSQuant supports relative protein quantitation based on precursor ion intensities, including element labels (e.g., 15N), residue labels (e.g., SILAC and ICAT), termini labels (e.g., 18O), functional group labels (e.g., mTRAQ), and label-free ion intensity approaches. MSQuant is available, including an installer and supporting scripts, at http://msquant.sourceforge.net.

U2 - 10.1021/pr900721e

DO - 10.1021/pr900721e

M3 - Journal article

C2 - 19888749

SN - 1535-3893

JO - Journal of Proteome Research

JF - Journal of Proteome Research

ER -