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MapQuant: Open‐source software for large‐scale protein quantification
Author(s) -
Leptos Kyriacos C.,
Sarracino David A.,
Jaffe Jacob D.,
Krastins Bryan,
Church George M.
Publication year - 2006
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200500201
Subject(s) - a priori and a posteriori , mass spectrometry , proteome , noise (video) , cluster analysis , software , sample (material) , computer science , biological system , pattern recognition (psychology) , data mining , analytical chemistry (journal) , chemistry , artificial intelligence , chromatography , biology , image (mathematics) , bioinformatics , programming language , philosophy , epistemology
Whole‐cell protein quantification using MS has proven to be a challenging task. Detection efficiency varies significantly from peptide to peptide, molecular identities are not evident a priori , and peptides are dispersed unevenly throughout the multidimensional data space. To overcome these challenges we developed an open‐source software package, MapQuant, to quantify comprehensively organic species detected in large MS datasets. MapQuant treats an LC/MS experiment as an image and utilizes standard image processing techniques to perform noise filtering, watershed segmentation, peak finding, peak fitting, peak clustering, charge‐state determination and carbon‐content estimation. MapQuant reports abundance values that respond linearly with the amount of sample analyzed on both low‐ and high‐resolution instruments (over a 1000‐fold dynamic range). Background noise added to a sample, either as a medium‐complexity peptide mixture or as a high‐complexity trypsinized proteome, exerts negligible effects on the abundance values reported by MapQuant and with coefficients of variance comparable to other methods. Finally, MapQuant's ability to define accurate mass and retention time features of isotopic clusters on a high‐resolution mass spectrometer can increase protein sequence coverage by assigning sequence identities to observed isotopic clusters without corresponding MS/MS data.