Statistical Methods for Classifying Mass Spectrometry Database Search Results

2008 ◽  
pp. 357-378
Keyword(s):  
Mass Spectrometry ◽  
Statistical Methods ◽  
Database Search ◽  
Search Results

scholarly journals De-duplication of database search results for systematic reviews in EndNote

2016 ◽  
Vol 104 (3) ◽  
Author(s):  
Wichor M. Bramer ◽  
Dean Giustini ◽  
Gerdien B. De Jonge ◽  
Leslie Holland ◽  
Tanja Bekhuis
Keyword(s):  
Systematic Reviews ◽  
Database Search ◽  
Search Results

scholarly journals De-duplication of database search results for systematic reviews in EndNote

2016 ◽  
Vol 104 (3) ◽  
pp. 240-243 ◽  
Author(s):  
Wichor M. Bramer ◽  
Dean Giustini ◽  
Gerdien B. de Jonge ◽  
Leslie Holland ◽  
Tanja Bekhuis
Keyword(s):  
Systematic Reviews ◽  
Database Search ◽  
Search Results

scholarly journals Development of an MS Workflow Based on Combining Database Search Engines for Accurate Protein Identification and Its Validation to Identify the Serum Proteomic Profile in Female Stress Urinary Incontinence

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Sara El Jadid ◽  
Taoufik Bensellak ◽  
Raja Touahni ◽  
Ahmed Moussa
Keyword(s):  
Mass Spectrometry ◽  
Urinary Incontinence ◽  
Stress Urinary Incontinence ◽  
Search Engines ◽  
High Performance ◽  
Protein Identification ◽  
Computing Time ◽  
Database Search ◽  
Female Stress Urinary Incontinence ◽  
Accurate Analysis

A critical stage of shotgun proteomics is database search, a process which attempts to match the experimental spectra to the theoretical one. Given the considerable time and effort spent in analysis, it is self-evident for a researcher to aspire for rigorous computational analysis and a more confident and accurate peptide/protein identification. Mass spectrometry (MS) has been applied across several clinical disciplines. The pathophysiology of Stress Urinary Incontinence (SUI), caused by a damaged pelvic floor, has become a boundless disease altering the quality of life worldwide. Although some studies pointed markers that can be bioindicators for SUI, these findings raise the issue of sensitivity and specificity. Therefore, it is critical to have a sensitive and specific analytical approach to identify markers that have been associated with protective and deleterious associations in disease. Here, we describe our designed and developed workflow for protein identification from tandem mass spectrometry that uses multiple search engines. We apply our workflow to an existing study addressing the pathophysiology of SUI. We demonstrate how using the combined approach together with high-performance computing techniques can surmount the challenges of complex analyses and extended computing time. We also compare the relative performance of each combination. Our results suggest that a combination of MS-GF+ and COMET represents the best sensitivity-specificity trade-off, outperforming all other tested combinations. The approach was also sensitive and accurately identified a set of protein that was shown to be markers for categories of diseases associated with the pathophysiology of SUI. This workflow was developed to encourage proteomic researchers to adopt MS-based techniques for accurate analysis and to promote MS as a routine tool to the clinical cohorts.

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