ArticleBMC bioinformatics2008
Quality assessment of peptide tandem mass spectra.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19).Clinical proteomics · 2021Article
- A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19).medRxiv : the preprint server for health sciences · 2021Article
- An unsupervised machine learning method for assessing quality of tandem mass spectra.Proteome science · 2012Article
- Peptide charge state determination of tandem mass spectra from low-resolution collision induced dissociation.Proteome science · 2011Article
- Features-based deisotoping method for tandem mass spectra.Advances in bioinformatics · 2011Article
- A novel approach to denoising ion trap tandem mass spectra.Proteome science · 2009Article
- Quality assessment of tandem mass spectra using support vector machine (SVM).BMC bioinformatics · 2009Article
- Highlighting computations in bioscience and bioinformatics: review of the Symposium of Computations in Bioinformatics and Bioscience (SCBB07).BMC bioinformatics · 2008Article
- Defining Spectral Quality in Mass Spectrometry-Based Proteomics: A Retrospective Review.Mass spectrometry reviewsReview
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4 authors.
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No grant is acknowledged in the PubMed record.
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.
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