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An update on the moFF algorithm for label-free quantitative proteomics

(2019) JOURNAL OF PROTEOME RESEARCH. 18(2). p.728-731
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Abstract
moFF is a modular and operating-system-independent tool for quantitative analysis of label-free mass-spectrometry-based proteomics data. The moFF workflow, comprising matching-between-runs and apex quantification, can be applied to any upstream search engine's output, along with the corresponding Thermo or mzML raw file. We here present moFF 2.0, with improvements in speed through multithreading, the use of a new raw file access library, and a novel filtering approach in the matching-between-runs module. This filter allows moFF to correctly identify features that are present in one run but not in another, as demonstrated using spiked-in iRT peptides. Moreover, moFF 2.0 also provides a new peptide summary export that can be used in downstream statistical analysis. moFF is open source and freely available and can be downloaded from https://github.com/compomics/moFF
Keywords
bioinformatics tool, label-free quantification, MS1-peptide intensity, singleton peptides, DESIGN

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Chicago
Argentini, Andrea, An Staes, Björn Andreas Grüning, Subina Mehta, Caleb Easterly, Timothy Griffin, Pratik Jagtap, Francis Impens, and Lennart Martens. 2019. “An Update on the moFF Algorithm for Label-free Quantitative Proteomics.” Journal of Proteome Research 18 (2): 728–731.
APA
Argentini, A., Staes, A., Grüning, B. A., Mehta, S., Easterly, C., Griffin, T., Jagtap, P., et al. (2019). An update on the moFF algorithm for label-free quantitative proteomics. JOURNAL OF PROTEOME RESEARCH, 18(2), 728–731.
Vancouver
1.
Argentini A, Staes A, Grüning BA, Mehta S, Easterly C, Griffin T, et al. An update on the moFF algorithm for label-free quantitative proteomics. JOURNAL OF PROTEOME RESEARCH. 2019;18(2):728–31.
MLA
Argentini, Andrea et al. “An Update on the moFF Algorithm for Label-free Quantitative Proteomics.” JOURNAL OF PROTEOME RESEARCH 18.2 (2019): 728–731. Print.
@article{8585690,
  abstract     = {moFF is a modular and operating-system-independent tool for quantitative analysis of label-free mass-spectrometry-based proteomics data. The moFF workflow, comprising matching-between-runs and apex quantification, can be applied to any upstream search engine's output, along with the corresponding Thermo or mzML raw file. We here present moFF 2.0, with improvements in speed through multithreading, the use of a new raw file access library, and a novel filtering approach in the matching-between-runs module. This filter allows moFF to correctly identify features that are present in one run but not in another, as demonstrated using spiked-in iRT peptides. Moreover, moFF 2.0 also provides a new peptide summary export that can be used in downstream statistical analysis. moFF is open source and freely available and can be downloaded from https://github.com/compomics/moFF},
  author       = {Argentini, Andrea and Staes, An and Gr{\"u}ning, Bj{\"o}rn Andreas and Mehta, Subina and Easterly, Caleb and Griffin, Timothy  and Jagtap, Pratik and Impens, Francis and Martens, Lennart},
  issn         = {1535-3893},
  journal      = {JOURNAL OF PROTEOME RESEARCH},
  language     = {eng},
  number       = {2},
  pages        = {728--731},
  title        = {An update on the moFF algorithm for label-free quantitative proteomics},
  url          = {http://dx.doi.org/10.1021/acs.jproteome.8b00708},
  volume       = {18},
  year         = {2019},
}

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