Abstract
BackgroundTandem mass spectrometry has emerged as a cornerstone of high throughput proteomic studies owing in part to various high throughput search engines which are used to interpret these tandem mass spectra. However, majority of experimental tandem mass spectra cannot be interpreted by any existing methods. There are many reasons why this happens. However, one of the most important reasons is that majority of experimental spectra are of too poor quality to be interpretable. It wastes time to interpret these "uninterpretable" spectra by any methods. On the other hand, some spectra of high quality are not able to get a score high enough to be interpreted by existing search engines because there are many similar peptides in the searched database. However, such spectra may be good enough to be interpreted by de novo methods or manually verifying methods. Therefore, it is worth in developing a method for assessing spectral quality, which can used for filtering the spectra of poor quality before any interpretation attempts or for finding the most potential candidates for de novo methods or manually verifying methods.ResultsThis paper develops a novel method to assess the quality of tandem mass spectra, which can eliminate majority of poor quality spectra while losing very minority of high quality spectra. First, a number of features are proposed to describe the quality of tandem mass spectra. The proposed method maps each tandem spectrum into a feature vector. Then Fisher linear discriminant analysis (FLDA) is employed to construct the classifier (the filter) which discriminates the high quality spectra from the poor quality ones. The proposed method has been tested on two tandem mass spectra datasets acquired by ion trap mass spectrometers.ConclusionComputational experiments illustrate that the proposed method outperforms the existing ones. The proposed method is generic, and is expected to be applicable to assessing the quality of spectra acquired by instruments other than ion trap mass spectrometers.
Highlights
Tandem mass spectrometry has emerged as a cornerstone of high throughput proteomic studies owing in part to various high throughput search engines which are used to interpret these tandem mass spectra
The proposed method has been tested on two tandem mass spectra datasets acquired by ion trap mass spectrometers: TOV dataset and Standard protein mixture (SPM) dataset
We have done an initial research on assessing the quality of peptide collision-induced dissociation (CID) spectra produced by tandem mass spectrometry
Summary
Tandem mass spectrometry has emerged as a cornerstone of high throughput proteomic studies owing in part to various high throughput search engines which are used to interpret these tandem mass spectra. Some spectra of high quality are not able to get a score high enough to be interpreted by existing search engines because there are many similar peptides in the searched database Such spectra may be good enough to be interpreted by de novo methods or manually verifying methods. Many automated database search engines have been developed for assigning peptide CID mass spectra, for example SEQUEST [4], Mascot [5] and Sonar [6]. Even if high quality CID spectra that cannot be identified by any database search engine, they still are potential candidates to be interpretable for de novo sequencing or manually verifying method. It is worthwhile to develop a powerful filter that masks out the poor quality of CID spectra before interpretation by any method for saving the time
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