proFIA: A data preprocessing workflow for Flow Injection Analysis coupled to High-Resolution Mass Spectrometry release_656isizzw5h7nifq7sfr76vdoq

by Alexis Delabrière, Ulli Hohenester, Benoit Colsch, Christophe Junot, François Fenaille, Etienne Thévenot

Released as a article .

2017  

Abstract

Motivation: Flow Injection Analysis coupled to High-Resolution Mass Spectrometry (FIA-HRMS) is a promising approach for high-throughput metabolomics. FIA-HRMS data, however, cannot be preprocessed with current software tools which rely on liquid chromatography separation, or handle low resolution data only. Results: We thus developed the proFIA package, which implements a suite of innovative algorithms to preprocess FIA-HRMS raw files, and generates the table of peak intensities. The workflow consists of 3 steps: i) noise estimation, peak detection and quantification, ii) peak grouping across samples, and iii) missing value imputation. In addition, we have implemented a new indicator to quantify the potential alteration of the feature peak shape due to matrix effect. The preprocessing is fast (less than 15 s per file), and the value of the main parameters (ppm and dmz) can be easily inferred from the mass resolution of the instrument. Application to two metabolomics datasets (including spiked serum samples) showed high precision (96%) and recall (98%) compared with manual integration. These results demonstrate that proFIA achieves very efficient and robust detection and quantification of FIA-HRMS data, and opens new opportunities for high-throughput phenotyping. Availability: The proFIA software (as well as the plasFIA data set) is available as an R package on the Bioconductor repository (http://bioconductor.org/packages/proFIA), and as a Galaxy module on the Main Toolshed (https://toolshed.g2.bx.psu.edu/) and on the Workflow4Metabolomics online infrastructure (http://workflow4metabolomics.org). Contacts: alexis.delabriere@cea.fr and etienne.thevenot@cea.fr.
In text/plain format

Archived Files and Locations

application/pdf  2.1 MB
file_4px25njeira7djseb2wbymqlja
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2017-08-14
Version   v1
Language   en ?
arXiv  1708.04025v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 2270bf81-8e1f-4dfa-bf68-7947efca2f4c
API URL: JSON