Quality Assessment of Tandem Mass Spectra Based on Cumulative Intensity Normalization

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1 Quality Assessment of Tandem Mass Spectra Based on Cumulative Intensity Normalization Seungjin Na and Eunok Paek* Department of Mechanical and Information Engineering, University of Seoul, Seoul, Korea Received July 4, 2006 A large proportion of MS/MS spectral analyses do not result in significant matches because their spectral quality is too poor to produce meaningful identification. Throughput of peptide identification can be greatly improved, if one can filter out, in advance, spectra that would lead to wrong identification. We introduce here an innovative approach to assess spectral quality utilizing a new spectral feature called Xrea, based on cumulative intensity normalization. Keywords: tandem mass spectra intensity normalization spectral quality peptide identification Introduction Various approaches have been introduced to interpret tandem mass spectra (MS/MS) for peptide identification. Database search methods such as SEQUEST 1 and Mascot 2 compare an experimental MS/MS spectrum with theoretical MS/MS spectra, generated from candidate peptides found in a database. On the other hand, in de novo sequencing approaches such as Lutefisk 3 and PEAKS, 4 a peptide sequence is directly inferred from each spectrum without resorting to a peptide sequence database. Approaches combining these two methods have also been proposed. 5,6 A single spectrum may be associated with many candidate peptides. A scoring scheme is applied to these candidate peptides to find a peptide that most likely corresponds to a spectrum. The peptide with the highest score among candidates is regarded as the correct match, when its score is considered to be significant. Many algorithms have therefore been proposed for scoring, which is a critical step in peptide identification. Intensity-based scoring algorithms 7-10 have been proposed, and they have produced substantial improvements in peptide identification. It is difficult, however, to apply raw intensities of fragment ion peaks to a scoring algorithm because they vary considerably from spectrum to spectrum. Intensity normalization is required for incorporating intensity information in a spectrum into a scoring algorithm, and many methods have been proposed for this purpose. Existing methods mainly normalize intensity with specific information about raw intensities, for example, the intensity of the most abundant peak. But such methods are very sensitive to variations in raw intensities. In this paper, we propose a new intensity normalization method, called cumulative intensity normalization, which considers both the magnitude of individual fragment ion peaks * To whom correspondence should be addressed. Eunok Paek, Dept. of Mechanical and Information Engineering, University of Seoul, 90 Jeonnongdong, Dongdaemun-gu, Seoul, Korea, Tel, ; fax, ; , paek@uos.ac.kr. and their ranking in raw intensities and, thus, overcomes the shortcomings of existing normalization methods. Effectiveness of this newly proposed method is demonstrated by an experiment that compares SEQUEST search results when different normalization methods are applied. In addition to its contribution to better scoring, cumulative intensity normalization can also be useful when estimating the quality of a spectrum. In mass spectrometry-based proteomics, a high percentage (e.g., 80-90%) of MS/MS data does not result in significant matches and is discarded. Thus, it will be useful if low-quality spectra that are not likely to lead to any useful identification can be localized and filtered out. Recently, there have been many reports assessing data quality of peptide mass spectra. In some of these research efforts, machine learning approaches are adopted using various spectral features. Machine learning methods vary from a genetic algorithm 11 to a support vector machine, 12 and several different learning methods are compared in terms of their performances. 13 Other approaches use a score function based on spatial distribution patterns of peaks 14 and maximum length of peptide sequence tags. 15 These results are used for filtering noisy spectrum data via spectral quality assessment. Another study reports that unassigned spectra of high quality can be identified by additional extended searching. 16 In this paper, we propose a method for the assessment of spectral quality that addresses the problem of computational efficiency encountered by MS/MS spectra analyses in highthroughput proteomics. Several spectral features are introduced to assess the quality of tandem mass spectra. First, we present a novel spectral feature measuring patterns in peak intensity distribution of a spectrum. This measure, named Xrea, uses the properties of cumulative intensity normalization. In addition, we use features to measure the abundance of potentially interpretable peak pairs in a spectrum. Our approach, combining features involving intensity and m/z difference, greatly enhances the computational throughput by filtering out useless data prior to database searching. We evaluated the effectiveness of our method against two different MS/MS data sets /pr CCC: $ American Chemical Society Journal of Proteome Research 2006, 5, Published on Web 11/09/2006

2 research articles Na and Paek Figure 1. MS/MS spectra of various qualities. Experimental Section We used two different publicly available MS/MS data sets from ion trap tandem mass spectrometers. One data set, from ISB protein mixture, was used as control data to demonstrate the effectiveness of newly proposed intensity normalization, to characterize spectral quality by newly introduced features, and to optimize the quality assessment algorithm. The other set, from human K562 cells, was used as test data to validate the performance and usefulness of our algorithm. 1. ISB Data. The ISB protein mixture data set consists of MS/MS spectra 17 obtained by mixing together 18 purified proteins and performing mass spectrometry analysis on an ESI-ITMS (ThermoFinnigan, San Jose, CA). Combined tandem mass spectra from this control mixture were searched using SEQUEST against a human protein database appended with sequences of the 18 control mixture proteins. This analysis produced assignments to doubly charged spectra, to triply charged spectra, and 504 to singly charged spectra (a spectrum representing a charge state greater than 1+ is searched twice). In total, 1656 peptide assignments to doubly charged spectra, 984 to triply charged spectra, and 125 to singly charged spectra were determined to be correct after manual inspection Human Erythroleukemia K562 Cell Line. This data set 18 is available in the data repository from PeptideAtlas ( An extract of the erythroleukemia cell line K562 grown in suspension was analyzed on an LCQ Classic ion trap mass spectrometer. Spectra were searched using SEQUEST against the IPI human protein database (version 2.18). This analysis produced assignments to doubly charged spectra, to triply charged spectra, and 1644 to singly charged spectra. Of those, 1692 peptide assignments to doubly charged spectra and 1790 to triply charged spectra were regarded as ones with high confidence over the XCorr score threshold. In general, multiply charged spectra from ion trap mass spectrometers were searched twice, assuming 2+ or 3+ charge state, resulting in waste of computational resources. This calls for filtering out many spectra from these multiply charged spectra. Furthermore, singly charged spectra constitute only a small fraction of the entire set. Therefore, we restricted our analysis to multiply charged tandem mass spectra. To establish a model for spectral quality assessment, we used support vector machine (SVM) as a machine learning method. From the ISB data set, 2640 spectra that were identified to be correct were labeled GOOD (identifiable). The remaining spectra were labeled BAD (unidentifiable). The SVM classifier was trained on this labeled spectra using 5-fold cross validation. Further, the learned SVM classifier was applied to a more typical high-throughput proteomics data set (human erythroleukemia K562 cell line) to test our algorithm, and its classification power is presented. Results and Discussion Intensity Normalization. For peptide identification, many scoring algorithms 1-6 have been developed to evaluate a match between a spectrum and a peptide. Early scoring algorithms focused only on the presence or absence of peaks at specific m/z and did not take into account their intensities. However, it has been suggested that intensity-based scoring algorithms 7-10 can provide significant improvements in peptide identification. It is difficult to apply raw intensities to a scoring algorithm, however, because raw intensities of fragment ion peaks are highly variable from spectrum to spectrum. Therefore, intensity normalization is necessary for incorporating raw intensities of fragment ion peaks into algorithms. Most existing methods of analysis use relative intensity, defined as the raw intensity normalized by the intensity of the most abundant peak or the total intensity of all the peaks in a spectrum. But this relative intensity normalization has some shortcomings. First, when the single most abundant peak is very strong relative to the rest of the peaks, as observed quite often, intensities of the remaining peaks may be hard to distinguish from the background noise and can thus be ignored, as in the case of the spectrum in Figure 1c. In Figure 1c, the original spectrum is redrawn in a small dashed box after 3242 Journal of Proteome Research Vol. 5, No. 12, 2006

3 Quality Assessment of Tandem Mass Spectra research articles Figure 2. Transformation of relative normalization into cumulative normalization. Two curves are shown, the curves of normalized peak intensities when they are arranged in ascending order according to each of the relative and cumulative methods. If all the peaks in a spectrum are equal, the cumulative curve will be a diagonal line. The cumulative curve cannot extend over this diagonal line. removing the most intense peak. In this spectrum displayed inside the inset box, high and low peaks are more easily distinguishable than the original spectrum. Second, when raw intensities are small over the entire spectrum, values of relative intensities will be more or less the same, and the peaks may therefore look indistinguishable as in Figure 1d. To overcome these shortcomings, a different normalization approach, called rank-based intensity normalization, which relies entirely on the rank of a fragment ion peak in a spectrum without any regard to the magnitude of raw intensity, has been proposed. 12 But, it has been reported previously that fragment ion intensities are reproducible, 10,19-20 and experts in mass spectrometry believe that intensities of fragment ion peaks have useful information even if the fragmentation process of collision-induced dissociation is not exactly quantitative. We propose here, a new intensity normalization method, called cumulative intensity normalization. In this approach, relative intensities (raw intensities divided by the total intensity) are cumulated so that intensity of the nth highest peak is defined as the sum of relative intensities of all the peaks, the intensities of which are smaller than or equal to that of the nth highest peak. Equation 1 shown below defines cumulative intensity. Cumulative normalized intensity of the nth highest peak ) {I raw (x) Rank(x) g n} TIC where I raw(x) is raw intensity of a fragment ion at x (m/z), TIC (total ion current) is the total intensity of a spectrum, and Rank- (x) represents the order of a fragment ion at x when sorted by magnitude of raw intensities in descending order. The most intense peak has rank 1, the second most intense peak has rank 2, and so forth. In accordance with this definition, the most intense peak is always normalized to 1. Figure 2 compares curves of normalized peak intensities when they are arranged in ascending order, applying each of the two different normalization methods. The curve drawn using cumulative normalization represents gradients in raw intensities in a spectrum. In the cumulative curve, the difference between nth and (n - 1)th peak values is the nth RI bytic, raw intensity divided by TIC. Thus, if every peak in a spectrum is equal in its raw intensity, the increasing rate of the curve is a constant CS, where CS is RI bytic, which is the same for all the fragment ion peaks in this case. Accordingly, the cumulative curve will be a diagonal line shown in Figure 2, and its slope (1) Figure 3. Cumulative and relative curves for the four spectra presented in Figure 1. will be CS. But, given that the total intensity of a spectrum is constant and peaks raw intensities are not all equal, if there are peaks whose RI bytic values are higher than CS, there must be peaks whose RI bytic are smaller than CS, because the sum of all RI bytic values in a spectrum is a constant 1. Because the normalized intensities are arranged by their magnitudes in this curve, no cumulative curve extends over this diagonal line. A more rigorous proof of this property can be found in Supporting Information. Figure 3 shows relative and cumulative curves for the spectra shown in Figure 1. The cumulative peak intensities have rankbased normalized intensities (diagonal line) as their upper bounds. In contrast, the relative curve in Figure 3d extends over the diagonal. In other words, when raw intensities are small over the entire spectrum as in Figure 1d, relative intensities will be more or less the same, while cumulative intensities will have obvious differences due to their ranking. Thus, the cumulative normalization complies with the individual ranking of each peak, taking into account individual intensity magnitudes as well. It must also be noted that the cumulative intensity normalization does not suffer from the problem that normalized intensity is very sensitive to the magnitude of the most abundant peak. To account for this effectiveness, if one assumes that the intensity of the most abundant peak is R, that there are N peaks other than the most abundant peak, and that intensities of those N peaks are all β, which is a lot smaller than R (β,r), the total intensity of the spectrum is R+N β. In cumulative normalization, cumulative intensity of the second most abundant peak is CI 2 ) N β/(r +N β). Given that R is a constant, CI 2 increases as N β increases. N β is proportional not only to β but also to N. If the number of peaks in a spectrum increases, N β increases. That is, even if the magnitude of β is very small, the influence of the most abundant peak can be reduced by an increase in the number of peaks. But, in relative normalization, relative intensity of the second most abundant peak is RI 2 ) β/r. Because it is determined entirely by the magnitude of β given a constant R, the smaller β is, the smaller RI 2 becomes. As a result, as shown in Figure 1c, while relative normalization is very sensitive to the most abundant peak, cumulative normalization can be relatively free from such influence. The curves in Figure 3c exemplify such effect very well. In other words, normalized peak intensities are relatively stable from spectrum to spectrum. Effectiveness of Cumulative Intensity Normalization. To demonstrate the effectiveness of cumulative intensity normalization, we performed a SEQUEST search on the ISB dataset twice against a human protein database (40 Mb) (extracted from ftp://ftp.ncicrf.gov/pub/nonredun/protein.nrdb.z) appended with sequences of the 18 control mixture proteins, Journal of Proteome Research Vol. 5, No. 12,

4 research articles using no enzyme search while tolerating up to 4 miscleavages. The first search was a regular SEQUEST search, and the second was done by replacing all the intensities in the *.dta file (spectrum file format for SEQUEST) with cumulative intensities, while all the search parameters remained the same. To measure the performance of each search result, we regarded peptide assignments corresponding to 18 proteins in the control mixture as correct. From the search with the unprocessed spectra, 1728 peptide assignments among 2+ spectra and 1078 assignments among 3+ spectra corresponded to the control mixture. In contrast, with cumulatively normalized spectra, 1811 and 1122 peptide assignments among 2+ and 3+ spectra, respectively, corresponded to the control mixture. Further detailed investigation showed that we obtained more meaningful search results, in addition to an increase in the number of correct assignments to the control proteins. Figure 4 shows distributions of XCorr scores from two different SEQUEST searches for doubly charged spectra. The inset box shows the distribution of spectra whose peptide assignments correspond to the control mixture proteins. It shows not only an increase in the number of assignments to correct proteins, but also an increase in match scores of peptides for the search results with cumulative intensities. When the SEQUEST XCorr threshold was set at 2.5, for significant peptide hits, a larger number of correct peptide hits existed over the threshold line in the case of the search using cumulatively normalized spectra than that of the regular search. The number of unassigned spectra over the threshold increased as well. But this increase was relatively small compared with the increase in the number of assigned spectra. A search with triply charged spectra also showed a similar trend in distribution. These results were further analyzed using PeptideProphet. 22 When PeptideProphet probability score of 0.9 was used for thresholding, it resulted in 2405 peptide assignments in unprocessed search and 2498 assignments in a search using cumulative intensity. Of these, the number of peptide assignments corresponding to the control mixture was 2346 in unprocessed search and 2454 in a search using cumulative intensity (59 vs 44 assignments corresponding to proteins other than control mixture). When applied to a tandem mass spectra analysis, cumulative intensity normalization enabled us to identify more peptide assignments with higher confidence when compared with the results from applying existing normalization method. Novel Feature for Spectral Quality. The features that are known to relate to quality of mass spectra include number of peaks, total ion current, signal-to-noise level, how likely two fragment ion peaks are to differ by the mass of an amino acid, and the existence of isotope and neutral loss peaks Generally, noise peaks in a spectrum have low intensities and signal peaks have high intensities, although with some exceptions. It can be said that the quality of a mass spectrum is good when one can clearly distinguish peaks with high intensity from those with low intensities, that is, when signal and noise peaks are clearly distinguishable. Figure 1a is an example of a good quality spectrum, in which one can distinguish peaks with high intensities from peaks with low intensities, while Figure 1d is an example of a bad quality spectrum. In this paper, we propose a new feature to assess the quality of mass spectra using properties of cumulative curve. As a spectrum shows more prominent differences between high and low peaks as in Figure 1a, its cumulative curve gets closer to the bottom right corner (Figure 3a). Since the cumulative curve Figure 4. Distributions of XCorr scores from two different SEQUEST searches using different normalization schemes (relative vs cumulative) for 2+ spectra. The inset box represents the distribution of peptide assignment (to a protein in the control mixture) scores. It shows more correct assignments to control proteins (1811 vs 1728) when cumulatively normalized spectra were used. It also shows an increase in match scores of peptide hits for the correct assignments. Dashed line represents XCorr value of 2.5 as the threshold of significant peptide hit, which is taken from DTASelect algorithm. 21 More correct peptide hits exist over the threshold in a search with cumulatively normalized spectra than in regular search (1436 vs 1091). represents gradients in raw intensities in a spectrum, with lower intensity values, the cumulative curve gets closer to the bottom, while with higher values, it gets closer to the right. As a result, if a spectrum has low intensities for noise peaks and high intensities for signal peaks, the cumulative curve will be closer to the bottom right corner. Figure 5 shows cumulative curves for the spectra shown in Figure 1. Here, we hypothesize that the larger the area XX is, the better the quality of a spectrum is, where XX is defined as the area between the diagonal and the cumulative curve, as marked in Figure 5. On the basis of this hypothesis, we propose a new feature called Xrea, defined in eq 2 and propose to use it when evaluating the quality of a mass spectrum. the area of XX Xrea ) the area of a lower right triangle +R Na and Paek (2) 3244 Journal of Proteome Research Vol. 5, No. 12, 2006

5 Quality Assessment of Tandem Mass Spectra research articles Figure 5. Cumulative curves. XX is defined as the area between the cumulative curve and the diagonal line. The larger the area XX is, the better the quality of a mass spectrum is. where the area of cumulative curve is computed using strip method of numerical integration. Bin width is fixed as 1/n, where n is the number of fragment ion peaks. R is a penalty factor defined as the relative magnitude of the most abundant peak. The difference between the most and the second most intense cumulative intensity is the most intense RI bytic (see the definition of cumulative intensity). The more intense the magnitude of the most abundant peak is, the larger the area of XX, and thus, the spectrum will be regarded as having better quality. To prevent this tendency, R is employed, and its value is the most intense RI bytic in each spectrum. As the spectrum in Figure 1c shows, if the single most abundant peak is very strong, it is hard to determine spectral quality, even though its influence is reduced by using cumulative intensity normalization. According to eq 2, Figure 1a with the biggest XX is the best spectrum among the four example spectra. On the other hand, Xrea will be 0 for a spectrum with the worst quality, and the cumulative curve will be equal to the diagonal line. This is the case when every peak in a spectrum is equal in its raw intensity. We assume that such a spectrum is of the worst quality because signals and noises are indistinguishable. To demonstrate that Xrea is a useful measure for spectral quality, we assessed the correlation between Xrea and SE- QUEST XCorr. Figure 6 shows the averaged XCorr scores of spectra against Xrea. The XCorr score generally increases as the Xrea gets better. If we assume that an increase in Xrea means better spectral quality, it follows that as spectral quality gets better its peptide match score will be higher. Figure 7 shows the spectra distribution against Xrea. With an increase in Xrea, it is more likely that a spectrum results in a significant match. Overall, Xrea threshold (dashed line) can be used to separate poor matches from good ones. Quality Assessment of Mass Spectra. Currently, the most popular approach to interpreting tandem mass spectra is to search protein sequence databases with experimental MS/MS data. 1,2 When the database is large, it takes many computational resources. Recent advances in mass spectrometry technology made high-throughput proteome analysis possible. When processing a large number of spectra from such highthroughput experiments, a large portion of spectra do not result in significant matches because their spectral quality is too poor Figure 6. Average XCorr score of doubly and triply charged spectra against Xrea. XCorr score generally increases as Xrea gets better. A scatter plot for doubly charged spectra is shown in Figure 10. Figure 7. Distribution of spectra against Xrea. Distribution for GOOD (black) and BAD (gray) spectra sets are shown. As Xrea gets bigger, a fraction of good spectra at a given Xrea value increases. SEQUEST results are mostly poor for the spectra below the specified threshold (dashed line) of Xrea. to produce useful identification. In Figure 7, spectra represented by a black region produce useful identification, while spectra in the gray region do not result in significant matches and are therefore discarded. If it were possible to assess the quality of tandem mass spectra, filtering out bad spectra prior to software analysis would save many computational resources and greatly improve the throughput of peptide identification. In determining the quality of a given spectrum, the aforementioned feature, Xrea, is used together with another feature called Good-Diff Fraction. 12 Good-Diff Fraction (GDFR), which foretells how likely a fragment ion peak pair is to differ by the mass of an amino acid, is defined as follows: {I(x) + I(y) M(x) - M(y) ) M i } {I(x) + I(y) 56 e M(x) - M(y) e 187} where I(x) is the intensity of peak x, M(x) isthem/z value of x and M i is mass of an amino acid. The numbers 56 and 187 are masses of glycine and tryptophan. In this definition, we use cumulative intensity and tolerated offsets of (0.25 Da to compare a mass. Journal of Proteome Research Vol. 5, No. 12,

6 research articles Na and Paek Figure 8. Spectra distribution against GDFR defined for singly charged fragments (1+ fragments). GOOD (black) and BAD (gray) assignments are shown. For Good assignments, GDFR(1+ fragments) of doubly charged peptide is higher than that of triply charged peptide. Multiply charged spectra include multiply charged fragment ions. Thus, it is reasonable to extend the definition of GDFR to include mass differences between multiply charged fragment ions as well, so that M i/2 or M i/3 is used as well as M i in the GDFR definition. However, a previous investigation on peptide fragmentation indicates that singly charged fragments are much more abundant than doubly charged fragments when precursor ions are doubly charged. 19 Therefore, it would be sufficient to use GDFR defined only for singly charged fragments as a feature for the quality of 2+ spectra. Figure 8a shows distributions of 2+ spectra against GDFR of singly charged fragments after filtering out low-quality spectra based on the Xrea threshold value. It is easy to see that GDFR is a discriminant feature between GOOD and BAD spectra. Figure 8b shows the same distribution for 3+ spectra. In this case, however, GDFR does not seem to be a useful feature for discerning between GOOD and BAD sets. This is due to the fact that doubly charged fragments are more abundant than singly charged fragments when precursor ions are triply charged. It is necessary to consider GDFR for doubly charged fragments as well as for singly charged fragments. Quality assessment was conducted with the multiply charged tandem mass spectra. The data set was partitioned into GOOD and BAD spectra, as explained in Experimental Section. For classification, we used SVM (support vector machine). As an input to the SVM classifier generator, Xrea values and GDFR for singly and doubly charged fragments were used. To avoid any bias in the training set during learning, 5-fold cross validation was adopted. The training set consisted of 2640 and 2688 spectra of GOOD and BAD sets, respectively. To test the learned model, MS/MS spectra from human erythroleukemia K562 cell line 18 were searched using SEQUEST against the IPI human protein database. Of assignments to doubly charged spectra and to triply charged spectra, 1692 peptide assignments to doubly charged spectra and 1790 to triply charged spectra were regarded as GOOD. The XCorr thresholds used were 3.22 ([M + H 2] 2 + ) and 3.45 ([M + H 3] 3 + ), which were determined by comparing search results against normal and reverse database. 18 By assessing the quality of each spectrum using the SVM classifier, we could filter out 75% of unidentifiable spectra while losing only 10% of identifiable spectra. Figure 9 shows the overall performance of SVM classifier by means of a receiver operator characteristic (ROC) curve. Even if only 2% loss of GOOD identifiable spectra is allowed, it can filter out about 60% of BAD unidentifiable ones. Figure 9. ROC curve for SVM classifier tested on human erythroleukemia data set. The inset box represents ROC curve while keeping more than 90% of GOOD spectra. Our method makes it possible to filter out 75% of unidentifiable spectra while losing only 10% of identifiable spectra. Even if only 2% loss of GOOD identifiable spectra is allowed, it can filter out about 60% of BAD unidentifiable ones. Applications of the Model and Future Directions. The proposed spectral quality measure predicts reasonably well how likely a spectrum will result in a correct identification and thus can be used to filter out low-quality spectra. But there are quite a few spectra for which this measure does not correspond to the likelihood of correct identification. The most interesting examples of such spectra are those high-quality spectra whose match scores are poor (those in the marked section A of Figure 10). In database searching approaches, peptide sequencing is impossible when peptides are not in the searched database or if peptides are post-translationally modified. In such cases, the search will provide incorrect identification and insignificant scores. However, the peptide-spectrum match score can be improved either by extending the database or considering posttranslational modifications during the analysis. If the spectral quality assessment results in high values, it will be helpful to conduct a further in-depth analysis by putting more compu Journal of Proteome Research Vol. 5, No. 12, 2006

7 Quality Assessment of Tandem Mass Spectra research articles spectral quality evaluation while its search results score well, it may require manual inspection by a mass spectrometry expert or at least by some other validation process, as it may well be an indication that the search results are not entirely credible. Conclusions Figure 10. XCorr score distribution against Xrea for doubly charged spectra from the ISB dataset. The dashed line represents XCorr score of 2.5. Spectra with very high XCorr are associated with very high Xrea values, and almost all the spectra below the specified Xrea threshold have poor XCorr values. Section A represents high-quality spectra whose match scores are poor, which warrants a more in-depth analysis. Section B represents low-quality spectra whose match scores are good, of which search results are suspicious. We developed a useful quality measure to overcome problems in peptide identification by MS/MS analysis. New intensity normalization, using cumulative intensity, enabled us to identify potential peptides, likely to be lost in database search due to poor spectral quality. The proposed spectral quality filtering method proved exceptional for filtering out poor-quality spectra that could lead to wrong identification in large shotgun proteomic datasets, and greatly improved throughput of peptide identification. Evaluation of the method using ISB and human erythroleukemia datasets established the utility of our method and its applicability to MS/MS data under different conditions. We expect that intensity normalization and quality assessment will be useful for designing efficient scoring algorithms and search strategies in software development for the analysis of shotgun proteomic data. Acknowledgment. This work was supported by 21C Frontier Functional Proteomics Project from Korean Ministry of Science & Technology (FPR05A2-340) and an Academic Research Grant (2005) from University of Seoul. Supporting Information Available: A detailed proof of the property of cumulative intensity normalization can be found. This material is available free of charge via the Internet at References Figure 11. Peptide-spectrum match shown as a colored peak (red for y-ion and blue for b-ion), when a peak corresponds with a theoretical fragmentation site of the peptide. It is interpreted as doubly charged precursor ion with the peptide VAGTWYS- LAMAASDISLLDAQSAPLR. A high XCorr score (3.15) and DeltaCn (0.19) are assigned to this spectrum by SEQUEST, which are well over the usual stringent threshold values of XCorr of 2.5 and DeltaCn of 0.1. Experts evaluate this match as a false-positive, because noise and signal peaks are indistinguishable, random matches are made for the fragment ion peaks, and high-intensity peaks are not explained. The spectral quality assessment result by Xrea for this spectra was poor and below the threshold. tational time. In a recent work, it was shown that unassigned spectra of high quality can be identified by additional extended searching. 16 Another interesting set of spectra arises when its match score is high while its spectral quality is evaluated as poor, in spite of the general expectation that good-quality spectra lead to good search results. We investigated the spectra with high XCorr value and poor Xrea estimation, that is, those in section B of Figure 10. Figure 11 shows a typical example of such a spectrum. It was observed that, in most such cases, the peptide mass is relatively heavy. It was previously reported that XCorr value correlates with peptide mass, and high mass peptides get better XCorr values. 23 Thus, if a spectrum receives poor (1) Eng, J. K.; McCormack, A. L.; Yates, J. R., III. J. Am. Soc. Mass Spectrom. 1994, 5, (2) Perkins, D. N.; Pappin, D. J. C.; Creasy, D. M.; Cottrell, J. S. Electrophoresis 1999, 20, (3) Taylor, J. A.; Johnson, R. S. Anal. 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