Update on the moFF Algorithm for Label-Free Quantitative Proteomics

Andrea Argentini, An Staes, Björn Grüning, Subina Mehta, Caleb Easterly, Timothy J. Griffin, Pratik Jagtap, Francis Impens, Lennart Martens

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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

Original languageEnglish (US)
Pages (from-to)728-731
Number of pages4
JournalJournal of Proteome Research
Issue number2
StatePublished - Feb 1 2019


  • MS1-peptide intensity
  • bioinformatics tool
  • label-free quantification
  • singleton peptides

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

Fingerprint Dive into the research topics of 'Update on the moFF Algorithm for Label-Free Quantitative Proteomics'. Together they form a unique fingerprint.

Cite this