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We developed mspepsearchr, an R package for mass spectrometry data analysis. It simplifies integrating NIST library searches into complex workflows, improving efficiency for identifying compounds like steroids.

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Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Analytical Chemistry

Background:

  • High-level programming languages like R and Python are crucial for mass spectrometry (MS) data processing.
  • NIST MS Search algorithms are predominant for library searching but use proprietary formats.
  • Existing tools like MSPepSearch offer flexibility but require complex command-line interactions.

Purpose of the Study:

  • To develop an R package, mspepsearchr, for streamlined integration of NIST-format mass spectral library searches.
  • To overcome the limitations of proprietary database formats and cumbersome command-line interfaces.
  • To facilitate automation and incorporation of library searching into complex analytical workflows.

Main Methods:

  • Developed the R package mspepsearchr to interface with MSPepSearch.
  • Implemented external parallelization by running multiple MSPepSearch instances from R.
  • Evaluated package performance and utility in analyzing untargeted gas chromatography-mass spectrometry (GC-MS) data.

Main Results:

  • mspepsearchr successfully integrates MSPepSearch into R workflows, simplifying library searching.
  • External parallelization via multiple R instances enhances processing efficiency.
  • The package demonstrated utility in identifying steroid-like compounds in biological samples.

Conclusions:

  • mspepsearchr provides a user-friendly solution for leveraging NIST MS library search algorithms within R.
  • The package enhances the automation capabilities for mass spectral data analysis.
  • This tool aids in the identification of compounds, such as steroids, in complex biological matrices using GC-MS.