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A Dynamic Noise Level Algorithm for Spectral Screening of Peptide MS/MS Spectra

Hua Xu12 and Michael A Freitas3

Author Affiliations

1 Proteomics and Informatics Services Facility, University of Illinois at Chicago, Chicago, IL 60612, USA

2 Department of Medicinal Chemistry and Pharmacognosy, University of Illinois at Chicago, Chicago, IL 60612, USA

3 Department of Molecular Virology Immunology and Medical Genetics, Comprehensive Cancer Center, the Ohio State University Medical Center, Columbus, OH 43210, USA

BMC Bioinformatics 2010, 11:436  doi:10.1186/1471-2105-11-436

Published: 23 August 2010



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.


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.


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.