Spectrum Deconvolution of LC-MS Data
Posted 05 Sep, 2008 in Tools
This tool version is unpublished and cannot be run. If you would like to have this version staged for you, you can put a request through support.
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This version: 5.0.
Latest version: 5.2.
| Version | 5.0 - published on 05 May. 2010, unpublished on 17 Jun. 2010 |
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| Contributor(s) | Ann Christine Catlin Rosen Center for Advanced Computing George Howlett Purdue University |
| At a glance | Spectral deconvolution differentiates analyte signals from contaminants or instrumental noise, and reduces data dimensionality to benefit downstream statistical analysis. |
| Screenshots | |
| Description | The purpose of spectral deconvolution is to differentiate signals arising from the real analyte as opposed to signals arising from contaminants or instrumental noise, and to reduce data dimensionality which will benefit downstream statistical analysis. This tool extracts peak information from thousands of raw mass spectra and reports the peak information in a simple peak table.
This tool is part of the Proteome Discovery Pipeline (PDP), a data analysis pipeline for mass spectrometry based differential proteomics. The tool, known as xMass in the PDP, was created at Bindely Bioscience Center at Purdue University, and is based on GISTool, a software package with chemical noise filtering, charge state fitting, and de-isotoping for the analysis of complex peptide samples. Overlapping peptide signals in mass spectra are deconvoluted by correlating the observed spectrum with modeled peptide isotopic peak profiles. Isotopic peak profiles for peptides are generated in silico from a protein database producing reference model distributions. The GISTool algorithm has been modified to enable the analysis of metabolomics data generated from a LC-MS analytical platform.
The latest version of the Deconvolution Tool reflects major improvements such as the capability of analyzing data generated from low resolution MS instruments. It provides for data deconvolution of overlapping mass spectral peaks, identifies doublets, and calculates the ratio of the doublets.
LC-MS Datasets for the Deconvolution Tool: Input and Output Users can access the cceHUB shared data repository to search for LC-MS datasets to input into the Deconvolution Tool. Search criteria include instruments (e.g., LS-MSD TOF, XCT PLUS), data formats (e.g., mzXML, mzData, CDF), and stored data collections. Users can also access datasets stored in their own cceHUB home folder. Files in your home folder can be input into the Deconvolution Tool by selecting the option to choose your dataset from your own collection. The Deconvolution Tool generates one deconvoluted output file for each input LC-MS file. The output file can then be loaded as input to the Peak Alignment Tool, the next step in the cceHUB Discovery Pipeline. For all datasets loaded into the Deconvolution Tool from the cceHUB repository, generated output datasets are automatically available for input into the Peak Alignment Tool from the cceHUB repository. You can also download any output files from your Deconvolution Tool runs to your home cceHUB directory, where you can use them as input to Peak Alignment. See the supporting document Getting Started for more information. |
| credits | The xMass algorithm and software were developed by Xiang Zhang, based on the GISTool system created by Xiang Zhang, Jiri Adamec, et al. in 2005. The Purdue Discovery Pipeline was created by the Bindley Biosciences Center under the direction of Charles Buck. The integration of Purdue Discovery Pipeline models as cceHUB tools and the contribution of test datasets to the cceHUB respository are part of a collaborative effort with Jiri Adamec, Amber Jannasch and Paige Riley of the Bindley Biosciences Center and George Howlett of the Rosen Center for Advanced Computing. |
| references | Gough, E., Oh, C.; He, J.; Riley, C.; Buck, C.; and Zhang, X. Proteome discovery pipeline for mass spectrometry-based proteomics. Click to access the paper online. Zhang, X.; Hines, W.; Adamec, J.; Asara, J.; Naylor, S.; and Regnier, F. E. An automated method for the analysis of stable isotope labeling data for proteomics. J. Am. Soc. Mass Spectrom. 2005, 16, 1181-1191. Zhang, X; Asara, J.; Adamec, J.; Ouzzani, M.; and Elmagarmid, A. Data pre-processing in liquid chromatography–mass spectrometry-based proteomics. Bioinformatics [1367-4803]. 2005 vol:21 iss:21 pg:4054. Link to full text from Oxford University Press Journals. |
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