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1.  Detection of co-eluted peptides using database search methods 
Biology Direct  2008;3:27.
Current experimental techniques, especially those applying liquid chromatography mass spectrometry, have made high-throughput proteomic studies possible. The increase in throughput however also raises concerns on the accuracy of identification or quantification. Most experimental procedures select in a given MS scan only a few relatively most intense parent ions, each to be fragmented (MS2) separately, and most other minor co-eluted peptides that have similar chromatographic retention times are ignored and their information lost.
We have computationally investigated the possibility of enhancing the information retrieval during a given LC/MS experiment by selecting the two or three most intense parent ions for simultaneous fragmentation. A set of spectra is created via superimposing a number of MS2 spectra, each can be identified by all search methods tested with high confidence, to mimick the spectra of co-eluted peptides. The generated convoluted spectra were used to evaluate the capability of several database search methods – SEQUEST, Mascot, X!Tandem, OMSSA, and RAId_DbS – in identifying true peptides from superimposed spectra of co-eluted peptides. We show that using these simulated spectra, all the database search methods will gain eventually in the number of true peptides identified by using the compound spectra of co-eluted peptides.
Open peer review
Reviewed by Vlad Petyuk (nominated by Arcady Mushegian), King Jordan and Shamil Sunyaev. For the full reviews, please go to the Reviewers' comments section.
PMCID: PMC2483259  PMID: 18597684
2.  Calibrating E-values for MS2 database search methods 
Biology Direct  2007;2:26.
The key to mass-spectrometry-based proteomics is peptide identification, which relies on software analysis of tandem mass spectra. Although each search engine has its strength, combining the strengths of various search engines is not yet realizable largely due to the lack of a unified statistical framework that is applicable to any method.
We have developed a universal scheme for statistical calibration of peptide identifications. The protocol can be used for both de novo approaches as well as database search methods. We demonstrate the protocol using only the database search methods. Among seven methods -SEQUEST (v27 rev12), ProbID (v1.0), InsPecT (v20060505), Mascot (v2.1), X!Tandem (v1.0), OMSSA (v2.0) and RAId_DbS – calibrated, except for X!Tandem and RAId_DbS most methods require a rescaling according to the database size searched. We demonstrate that our calibration protocol indeed produces unified statistics both in terms of average number of false positives and in terms of the probability for a peptide hit to be a true positive. Although both the protocols for calibration and the statistics thus calibrated are universal, the calibration formulas obtained from one laboratory with data collected using either centroid or profile format may not be directly usable by the other laboratories. Thus each laboratory is encouraged to calibrate the search methods it intends to use. We also address the importance of using spectrum-specific statistics and possible improvement on the current calibration protocol. The spectra used for statistical (E-value) calibration are freely available upon request.
Open peer review
Reviewed by Dongxiao Zhu (nominated by Arcady Mushegian), Alexey Nesvizhskii (nominated by King Jordan) and Vineet Bafna. For the full reviews, please go to the Reviewers' comments section.
PMCID: PMC2206012  PMID: 17983478
3.  RAId_DbS: Peptide Identification using Database Searches with Realistic Statistics 
Biology Direct  2007;2:25.
The key to mass-spectrometry-based proteomics is peptide identification. A major challenge in peptide identification is to obtain realistic E-values when assigning statistical significance to candidate peptides.
Using a simple scoring scheme, we propose a database search method with theoretically characterized statistics. Taking into account possible skewness in the random variable distribution and the effect of finite sampling, we provide a theoretical derivation for the tail of the score distribution. For every experimental spectrum examined, we collect the scores of peptides in the database, and find good agreement between the collected score statistics and our theoretical distribution. Using Student's t-tests, we quantify the degree of agreement between the theoretical distribution and the score statistics collected. The T-tests may be used to measure the reliability of reported statistics. When combined with reported P-value for a peptide hit using a score distribution model, this new measure prevents exaggerated statistics. Another feature of RAId_DbS is its capability of detecting multiple co-eluted peptides. The peptide identification performance and statistical accuracy of RAId_DbS are assessed and compared with several other search tools. The executables and data related to RAId_DbS are freely available upon request.
PMCID: PMC2211744  PMID: 17961253

Results 1-3 (3)