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

7 Scopus citations

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

Original languageEnglish (US)
Pages (from-to)728-731
Number of pages4
JournalJournal of Proteome Research
Volume18
Issue number2
DOIs
StatePublished - Feb 2019

Bibliographical note

Funding Information:
This work was supported by the Research Foundation Flanders (FWO) under grant numbers G042518N and SBO S006617N, by Ghent University under Concerted Research Action BOF12/GOA/014, and by NCI-ITCR grant 1U24CA199347. The Galaxy-P project is funded by National Science Foundation (NSF) grant DBI-1458524. We acknowledge Jim Shofstahl for his help with the Thermo RawFileR-eader.

Keywords

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

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