Results: 4

1.
Figure 4

Figure 4. From: RAId_DbS: Peptide Identification using Database Searches with Realistic Statistics.

Quantification of goodness of score model used for statistical significance assignment. A global study of the Mpdf accuracy using 10,000 spectra (profile mode). Panel (A) shows the histogram of the goodness number. Panel (B) shows a scattered plot of ν versus r obtained from our spectra as well as a number of curves each corresponds to a fixed PM value. Panel (C) displays the histogram of log10(PM).

Gelio Alves, et al. Biol Direct. 2007;2:25-25.
2.
Figure 1

Figure 1. From: RAId_DbS: Peptide Identification using Database Searches with Realistic Statistics.

Comparison of score histogram versus theoretical distribution. Comparison of score histogram versus theoretical distribution. A randomly picked query spectrum is used to score peptides in NCBI's nr database. For this query spectrum, nine hundred unit intensity peaks were added to the processed spectrum to match Sus. In panel (A), the red staircase represents the histogram of scores computed using Eq. (1) with wi = 1, while the blue line represents the theoretical distribution predicted from peptides with n = 44 theoretical peaks. In panel (B), scores computed using Eq. (1) with wi(mi) = exp(-Δ mi) for peptides with different numbers of theoretical peaks are collected, resulting in the overall score histogram represented by the red staircase. The solid curve plots our fitting of the histogram using Eq. (17) where the fitting variables are β, γ n/(6⟨x2β2) and .

Gelio Alves, et al. Biol Direct. 2007;2:25-25.
3.
Figure 2

Figure 2. From: RAId_DbS: Peptide Identification using Database Searches with Realistic Statistics.

Average cumulative number of false positives versus E-values. Average cumulative number of false positives versus E-values. Theoretically speaking, average number of false positives with E-values less than or equal to a cutoff Ec should be Ec provided that the number of trials is large enough. The accuracy of E-values assigned by RAId_DbS is tested along with three other methods, X! Tandem(v1.0), Mascot(v2.1) and OMSSA(v2.0). For X! Tandem, Mascot and OMSSA searches, default parameters of each program are used except the maximum number of miscleavages, which is set to 3 uniformly for this test. The diagonal solid lines in each panel are the theoretical lines. There are two curves associated with each method. The dashed line corresponds to the results using regular nr. The solid line corresponds to the results using nr with cluster removal, which we anticipate to be a better representative of a random database. See text for additional details.

Gelio Alves, et al. Biol Direct. 2007;2:25-25.
4.
Figure 3

Figure 3. From: RAId_DbS: Peptide Identification using Database Searches with Realistic Statistics.

Performance analysis of methods tested. Performance analysis of RAId_DbS, X! Tandem(v1.0), Mascot(v2.1), OMSSA(v2.0), and SEQUEST(v3.2). Panels (A) and (C) display the results from 6, 734 spectra in profile format, while panels (B) and (D) display the results from 6,592 centroidized spectra obtained from [19]. In panels (A) and (B), typical ROC curves are shown with the number of false positives (FP) plotted along the abscissa, and the number of true positives (TP) plotted along the ordinate. Thus, a curve that is more to the upper-left corner implies better performance. To unveil the information in the region of small number of false positives, usually the region of most interest, we have plotted the abscissa in log-scale. In panels (C) and (D), a different types of ROC curves are shown. Defining the cumulative number of true negatives by TN and the cumulative number of false negative by FN, the ROC cuves in panels (C) and (D) plot "1 – specificity" (FP/(FP + TN)) along the abscissa (also in log-scale), and the sensitivity (TP/(TP + FN)) along the ordinate. For each method tested, the area under curve (AUC) of this type of ROC curves, when both axes are plotted in linear scale, is also shown inside parentheses in the figure legend. All the AUC have an uncertainty about ± 0.005. Note that ROC curves of this type do not reflect the total number of correct hits and methods that report very few negatives may result in a lower specificity and superficially seems inferior. For example, X! Tandem may be victimized when evaluated using this type of ROC curves. Also note that in panel (D) the trend of AUC for Mascot, X! Tandem, and SEQUEST is consistent with previously reported results [14]. For X! Tandem, Mascot, OMSSA, and SEQUEST, the default parameters for each method were used in every search. However, the maximum number of miscleavages is set to 3 uniformly. It is observed that analysis using profile data giving rise to better ROC curves than those of centoidized data. Although this may be due to the fact that the profile data contain more information, it may also be caused by spectral quality and sample concentration variations.

Gelio Alves, et al. Biol Direct. 2007;2:25-25.

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