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Update on the moFF Algorithm for Label-Free Quantitative Proteomics.

Andrea ArgentiniAn StaesBjoern Andreas GrueningSubina MehtaCaleb W EasterlyTimothy J GriffinPratik D JagtapFrancis ImpensLennart Martens
Published in: Journal of proteome research (2018)
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.
Keyphrases
  • label free
  • mass spectrometry
  • high resolution
  • machine learning
  • electronic health record
  • liquid chromatography
  • deep learning
  • high performance liquid chromatography
  • amino acid
  • artificial intelligence