University of Illinois at Chicago
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A dynamic noise level algorithm for spectral screening of peptide MS/MS spectra

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journal contribution
posted on 2011-05-27, 00:00 authored by Hua Xu, Michael A. Freitas
Background: High-throughput shotgun proteomics data contain a significant number of spectra from non-peptide ions or spectra of too poor quality to obtain highly confident peptide identifications. These spectra cannot be identified with any positive peptide matches in some database search programs or are identified with false positives in others. Removing these spectra can improve the database search results and lower computational expense. Results: A new algorithm has been developed to filter tandem mass spectra of poor quality from shotgun proteomic experiments. The algorithm determines the noise level dynamically and independently for each spectrum in a tandem mass spectrometric data set. Spectra are filtered based on a minimum number of required signal peaks with a signal-to-noise ratio of 2. The algorithm was tested with 23 sample data sets containing 62,117 total spectra. Conclusions: The spectral screening removed 89.0% of the tandem mass spectra that did not yield a peptide match when searched with the MassMatrix database search software. Only 6.0% of tandem mass spectra that yielded peptide matches considered to be true positive matches were lost after spectral screening. The algorithm was found to be very effective at removal of unidentified spectra in other database search programs including Mascot, OMSSA, and X!Tandem (75.93%-91.00%) with a small loss (3.59%-9.40%) of true positive matches.


The study was funded by The Ohio State University, the National Institutes of Health (CA107106, CA101956), the V Foundation (AACR Translational Cancer Research Grant), the Leukemia & Lymphoma Society, (SCOR), the University of Illinois at Chicago, and the Searle Funds at the Chicago Community Trust to the Chicago Biomedical Consortium.


Publisher Statement

© 2010 Xu and Michael; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The original work is available through BioMed Central at DOI: 10.1186/1471-2105-11-436


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