proFIA

proFIA processes raw Flow Injection Analysis–High-Resolution Mass Spectrometry (FIA-HRMS) data to perform preprocessing and generate peak tables for high-throughput metabolomics.


Key Features:

  • Raw FIA-HRMS preprocessing: Implements preprocessing methods specifically for Flow Injection Analysis–High-Resolution Mass Spectrometry datasets.
  • Peak table generation: Extracts mass spectral features and generates peak tables for downstream metabolomics analysis.
  • File format support: Reads netCDF, mzData, mzXML, and mzML mass spectrometry raw data formats.
  • High-throughput processing: Designed for rapid processing of FIA-HRMS datasets in high-throughput metabolomics studies.
  • FIA-specific algorithms: Applies methods tailored to the absence of chromatographic separation in FIA data rather than liquid chromatography data.
  • Workflow interoperability: Produces outputs suitable for integration into existing metabolomics analysis pipelines.

Scientific Applications:

  • High-throughput metabolomics: Enables preprocessing of FIA-HRMS data to support large-scale metabolomic profiling studies.
  • Metabolomic feature extraction: Produces peak tables to facilitate downstream statistical analysis and compound identification workflows.

Methodology:

Reads netCDF, mzData, mzXML, and mzML FIA-HRMS raw files and applies FIA-specific preprocessing to extract mass spectral features and generate peak tables.

Topics

Collections

Details

License:
CECILL-2.1
Tool Type:
command-line tool, library
Operating Systems:
Linux, Windows, Mac
Programming Languages:
R
Added:
1/17/2017
Last Updated:
11/25/2024

Operations

Data Inputs & Outputs

Publications

Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M. Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods. 2015;12(2):115-121. doi:10.1038/nmeth.3252. PMID:25633503. PMCID:PMC4509590.

Documentation

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