Department of Bioinformatics
The New Model Processor for Mass Spectrometry Data
Cromwell is an implementation of our algorithms for low-level processing of mass spectrometry proteomics data. Cromwell represents our third attempt (at least) to find a method to process spectra that both works quickly and achieves reasonably good results. The name is thus derived from an historical pun: just as Oliver Cromwell became ruler of England by fashioning and leading the New Model Army, we intend to lead the way toward better proteomics data processing with our "new model processor".
Cromwell is not self-contained. Since it is implemented as a set of MATLAB scripts, it requires access to a commercial software package available from The MathWorks.
Cromwell also uses the undecimated discrete wavelet transform to denoise spectra, as implemented in the Rice Wavelet Toolbox.
The Cromwell Package
The algorithms and performance of Cromwell are describe in our technical report: Coombes KR, Tsavachidis S, Morris JS, Baggerly KA, Hung MC, Kuerer HM. Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform. UTMDABTR-001-04.
The actual code for Cromwell is a set of MATLAB files packaged in a zip file. Cromwell is copyrighted by the MD Anderson Cancer Center; the licensing details are straightforward, considering that Cromwell is free software from an academic institution.
We are continuing to develop Cromwell. One of the tools we are using is a mass spectrometry simulation engine (version 2.1) implemented in the S-Plus statistical software package from Insightful Corp. Details of the model underlying the simulation and preliminary applications are described in another technical report: Coombes KR, Koomen JM, Baggerly KA, Morris JS, Kobayashi R. Understanding the characteristics of mass spectrometry data through the use of simulation. UTMDABTR-002-04.
The simnulation engine has been used extensively in the publication by Morris JS, Coombes KR, Koomen J, Baggerly KA, Kobayashi R. Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinformatics. 2005;21:1764-75. The data sets that were simulated for this publication are available from our web site.
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