Update on the moFF Algorithm for Label-Free Quantitative Proteomics
| Autor: | An Staes, Caleb Easterly, Lennart Martens, Subina Mehta, Pratik D. Jagtap, Francis Impens, Andrea Argentini, Björn Grüning, Timothy J. Griffin |
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| Rok vydání: | 2018 |
| Předmět: |
Data Analysis
Proteomics 0301 basic medicine 030102 biochemistry & molecular biology Computer science Quantitative proteomics General Chemistry Computational biology Biochemistry 03 medical and health sciences Label-free quantification ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Workflow Data Interpretation Statistical Peptides Algorithms Software Label free |
| Zdroj: | Journal of Proteome Research. 18:728-731 |
| ISSN: | 1535-3907 1535-3893 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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