Parsing and Quantification of Raw Orbitrap Mass Spectrometer Data Using RawQuant

Effective
analysis of protein samples by mass spectrometry (MS)
requires careful selection and optimization of a range of experimental
parameters. As the output from the primary detection device, the “raw”
MS data file can be used to gauge the success of a given sample analysis.
However, the closed-source nature of the standard raw MS file can
complicate effective parsing of the data contained within. To ease
and increase the range of analyses possible, the RawQuant tool was
developed to enable parsing of raw MS files derived from Thermo Orbitrap
instruments to yield meta and scan data in an openly readable text
format. RawQuant can be commanded to export user-friendly files containing
MS1, MS2, and MS3 metadata as well
as matrices of quantification values based on isobaric tagging approaches.
In this study, the utility of RawQuant is demonstrated in several
scenarios: (1) reanalysis of shotgun proteomics data for the identification
of the human proteome, (2) reanalysis of experiments utilizing isobaric
tagging for whole-proteome quantification, and (3) analysis of a novel
bacterial proteome and synthetic peptide mixture for assessing quantification
accuracy when using isobaric tags. Together, these analyses successfully
demonstrate RawQuant for the efficient parsing and quantification
of data from raw Thermo Orbitrap MS files acquired in a range of common
proteomics experiments. In addition, the individual analyses using
RawQuant highlights parametric considerations in the different experimental
sets and suggests targetable areas to improve depth of coverage in
identification-focused studies and quantification accuracy when using
isobaric tags.