Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Nov 17;87(22):11361-7.
doi: 10.1021/acs.analchem.5b02721. Epub 2015 Nov 4.

Extracting Accurate Precursor Information for Tandem Mass Spectra by RawConverter

Lin He  1 , Jolene Diedrich  1 , Yen-Yin Chu  2 , John R Yates 3rd  1
Affiliations

Extracting Accurate Precursor Information for Tandem Mass Spectra by RawConverter

Lin He et al. Anal Chem. .

Abstract

Extraction of data from the proprietary RAW files generated by Thermo Fisher mass spectrometers is the primary step for subsequent data analysis. High resolution and high mass accuracy data obtained by state-of-the-art mass spectrometers (e.g., Orbitraps) can significantly improve both peptide/protein identification and quantification. We developed RawConverter, a stand-alone software tool, to improve data extraction on RAW files from high-resolution Thermo Fisher mass spectrometers. RawConverter extracts full scan and MS(n) data from RAW files like its predecessor RawXtract; most importantly, it associates the accurate precursor mass-to-charge (m/z) value with the tandem mass spectrum. RawConverter accepts RAW data generated by either data-dependent acquisition (DDA) or data-independent acquisition (DIA). It generates output into MS1/MS2/MS3, MGF, or mzXML file formats, which fulfills the format requirements for most data identification and quantification tools. Using the tandem mass spectra extracted by RawConverter with corrected m/z values, 32.8%, 27.1%, and 84.1%, peptide spectra matches (PSMs) produce 17.4% (13.0%), 14.4% (11.5%), and 45.7% (36.2%) more peptide (protein) identifications than ProteoWizard, pXtract, and RawXtract, respectively. RawConverter is implemented in C# and is freely accessible at http://fields.scripps.edu/rawconv.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Comparison of the numbers of identifications from the data sets extracted by five MS data processing software tools. (a) MS2 files were generated from the four extraction tools and then analyzed by IP2. (b) The RAW file was directly read and then analyzed by MaxQuant using the Andromeda search program but using as closely as possible the same parameters as IP2.
Figure 2
Figure 2
Comparison of identified PSMs from the data sets extracted by the four extraction software tools. (a) The overlap of identified PSMs, and (b) the score distribution of different PSMs identified from the data sets extracted by RawConverter and the other three tools.
Figure 3
Figure 3
An example of the precursor prediction function of RawConverter. Five peptide precursor m/z values and charge states were predicted and three of them (in colored boxes) were identified from the corresponding MS/MS spectrum.
Figure 4
Figure 4
Comparison of the numbers of peptides/proteins identified from DDA and DIA data sets. DIA data was acquired with three different isolation windows, 3 m/z, 5 m/z, and 10 m/z. DIA_3W_NP, DIA_5W_NP, and DIA_10W_NP are the data sets extracted by RawConverter without precursor information prediction, and DIA_3W, DIA_5W, and DIA_10W are the data sets with predicted precursor information.
See this image and copyright information in PMC

References

    1. McDonald WH, Tabb DL, Sadygov RG, MacCoss MJ, Venable J, Graumann J, Johnson JR, Cociorva D, Yates JR., III Rapid Commun Mass Spectrom. 2004;18:2162–2168. - PubMed
    1. Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, Gatto L, Fischer B, Pratt B, Egertson J, Hoff K, Kessner D, Tasman N, Shulman N, Frewen B, Baker TA, Brusniak M, Paulse C, Creasy D, Flashner L, Kani K, Moulding C, Seymour SL, Nuwaysir LM, Lefebvre B, Kuhlmann F, Roark J, Rainer P, Detlev S, Hemenway T, Huhmer A, Langridge J, Connolly B, Chadick T, Holly K, Eckels J, Deutsch EW, Moritz RL, Katz JE, Agus DB, MacCoss MJ, Tabb DL, Mallick P. Nat Biotechnol. 2012;30:918–920. - PMC - PubMed
    1. Yuan ZF, Liu C, Wang HP, Sun RX, Fu Y, Zhang JF, Wang LH, Chi H, Li Y, Xiu LY, Wang WP, He SM. Proteomics. 2012;12(2):226–235. - PubMed
    1. Hao P, Ren Y, Tam JP, Sze SK. J Proteome Res. 2013;12(12):5548–5557. - PubMed
    1. Nassar AF, Hollenberg P, Scatina J. Drug metabolism handbook: concepts and applications. John Wiley and Sons; Hoboken, New Jersey: 2009. p. 216.

Publication types