A Correlation Algorithm for the Automated Quantitative Analysis of Shotgun Proteomics Data

MacCoss, Michael J.; Wu, Christine C.; Liu, Hongbin; Sadygov, Rovshan G.; Yates, John R.

doi:10.1021/ac034790h

Cited by 265 publications

(260 citation statements)

References 62 publications

Supporting

Mentioning

256

Contrasting

Unclassified

Order By: Relevance

“…In our 1:1 SILAC data, pQuant outputs only 4 NaN ratios and 80 outliers (ratios >2 or <0.5), that is, of the 587 inaccurate Census ratios described above, pQuant successfully corrected 503 (86%). 15 N-labeled cultures were mixed at seven different ratios 10:1, 3:1, 1.5:1, 1:1, 1:1.5, 1:3, and 1:10 by total cell numbers (OD 600 mL).…”

mentioning

confidence: 99%

pQuant Improves Quantitation by Keeping out Interfering Signals and Evaluating the Accuracy of Calculated Ratios

et al. 2014

View full text Add to dashboard Cite

In relative protein abundance determination from peptide intensities recorded in full mass scans, a major complication that affects quantitation accuracy is signal interference from coeluting ions of similar m/z values. Here, we present pQuant, a quantitation software tool that solves this problem. pQuant detects interference signals, identifies for each peptide a pair of least interfered isotopic chromatograms: one for the light and one for the heavy isotope-labeled peptide. On the basis of these isotopic pairs, pQuant calculates the relative heavy/light peptide ratios along with their 99.75% confidence intervals (CIs). From the peptides ratios and their CIs, pQuant estimates the protein ratios and associated CIs by kernel density estimation. We tested pQuant, Census and MaxQuant on data sets obtained from mixtures (at varying mixing ratios from 10:1 to 1:10) of light-and heavy-SILAC labeled HeLa cells or 14 N-and 15 N-labeled Escherichia coli cells. pQuant quantitated more peptides with better accuracy than Census and MaxQuant in all 14 data sets. On the SILAC data sets, the nonquantified "NaN" (not a number) ratios generated by Census, MaxQuant, and pQuant accounted for 2.5−10.7%, 1.8−2.7%, and 0.01−0.5% of all ratios, respectively. On the 14 N/ 15 N data sets, which cannot be quantified by MaxQuant, Census and pQuant produced 0.9−10.0% and 0.3−2.9% NaN ratios, respectively. Excluding these NaN results, the standard deviations of the numerical ratios calculated by Census or MaxQuant are 30−100% larger than those by pQuant. These results show that pQuant outperforms Census and MaxQuant in SILAC and 15 N-based quantitation.M uch progress has been made in mass spectrometry (MS)-based quantitative proteomics in recent years, as evidenced by numerous applications, such as biomarker discovery, 1 study of chromatin assembly and disassembly, 2 identification of insulin signaling targets, 3 and protein posttranslational modification (PTM). 4 Among the most commonly used quantitative strategies are full MS scan-based quantitation methods, such as SILAC (stable isotope labeling with amino acids in cell), 5 15 N-labeling, 6 and 18 O-labeling. 7 In these strategies, proteins are metabolically labeled with stable isotopes, digested into peptides, and then analyzed using liquid chromatography (LC)-MS/MS. Quantitation software tools are designed to extract the intensities of pairs of light (L, unlabeled) and heavy (H, labeled) peptides from full MS scans. The relative abundance ratio of a protein between two conditions is then calculated based on the ratios of its constituent peptides. 8 For high-complexity samples such as whole cell lysates, it is not uncommon that a peptide coelutes with another peptide or a nonpeptide contaminant of a similar m/z value. 9 The interference caused by coeluting ions of similar m/z values can seriously compromise the accuracy of quantitation. 10,11 We examined two leading quantitation software tools Census 12 and MaxQuant, 13 and found that a lot of the peptide quantitation results ...

show abstract

mentioning

confidence: 99%

pQuant Improves Quantitation by Keeping out Interfering Signals and Evaluating the Accuracy of Calculated Ratios

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Finally, 14 N and 15 N labeled peptides never elute perfectly together meaning that during LC-MS/MS analysis, retention time spacing between light and heavy peptides is introduced, further complicating retrieval of peptide couples. RelEx [6] and MSQuant ((http://msquant.sourceforge.net/) and [7]) are two examples of algorithms that were designed to deal with these drawbacks and allow automated quantification. However, from our own experience we learned that such algorithms are often difficult to setup and implement, and always contain little bugs slowing down the whole process and making them often not user-friendly.…”

Section: G a L L E Y P R O O Fmentioning

confidence: 99%

Stable isotopic labeling in proteomics

et al. 2008

View full text Add to dashboard Cite

Labeling of proteins and peptides with stable heavy isotopes (deuterium, carbon-13, nitrogen-15, and oxygen-18) is widely used in quantitative proteomics. These are either incorporated metabolically in cells and small organisms, or postmetabolically in proteins and peptides by chemical or enzymatic reactions. Only upon measurement with mass spectrometers holding sufficient resolution, light, and heavy labeled peptide ions or reporter peptide fragment ions segregate and their intensity values are subsequently used for quantification. Targeted use of these labels or mass tags further leads to specific monitoring of diverse aspects of dynamic proteomes. In this review article, commonly used isotope labeling strategies are described, both for quantitative differential protein profiling and for targeted analysis of protein modifications.

show abstract

“…There are a large variety of excellent tools available, which are targeted in the main towards particular approaches, instruments, vendors, third-party software and/or formats [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. Table 1 summarises existing freely available quantitative proteomic software.…”

Section: Existing Softwarementioning

confidence: 99%

“…In the table, there are five tools relating to SEQUEST-based analyses for single isotope and ICAT studies [32][33][34][35]40], three relating specifically to iTRAQ ™ [24,38,43] and two relating to solely 16 O/ 18 O labelling methods [37,41,42]. They were chosen because sufficient details of the algorithm have been made available in the respective publications.…”

Section: Extracting and Calculating Quantitative Datamentioning

confidence: 99%

“…Background subtraction and outlier removal are then performed prior to the calculation of an abundance ratio for each peptide. Other approaches have simplified some of these steps, calculating the peptide abundance ratio using a least-squares fit to the points representing the two ion chromatograms (corresponding to the labelled and unlabelled peptides) in the case of RelEx [35] whilst ProRata uses a principal components analysis approach [32]. More recent tools adopt similar approaches to obtain peptide ratios from chromatograms, estimating the relative areas under chromatograms for the labelled/unlabelled peptides.…”

Section: Extracting and Calculating Quantitative Datamentioning

confidence: 99%

See 1 more Smart Citation

Capture and analysis of quantitative proteomic data

et al. 2007

View full text Add to dashboard Cite

Whilst the array of techniques available for quantitative proteomics continues to grow, the attendant bioinformatic software tools are similarly expanding in number. The data capture and analysis of such quantitative data is obviously crucial to the experiment and the methods used to process it will critically affect the quality of the data obtained. These tools must deal with a variety of issues, including identification of labelled and unlabelled peptide species, location of the corresponding MS scans in the experiment, construction of representative ion chromatograms, location of the true peptide ion chromatogram start and end, elimination of background signal in the mass spectrum and chromatogram and calculation of both peptide and protein ratios/abundances. A variety of tools and approaches are available, in part restricted by the nature of the experiment to be performed and available instrumentation. Currently, although there is no single consensus on precisely how to calculate protein and peptide abundances, many common themes have emerged which identify and reduce many of the key sources of error. These issues will be discussed, along with those relating to deposition of quantitative data. At present, mature data standards for quantitative proteomics are not yet available, although formats are beginning to emerge.

show abstract

A Correlation Algorithm for the Automated Quantitative Analysis of Shotgun Proteomics Data

Cited by 265 publications

References 62 publications

pQuant Improves Quantitation by Keeping out Interfering Signals and Evaluating the Accuracy of Calculated Ratios

pQuant Improves Quantitation by Keeping out Interfering Signals and Evaluating the Accuracy of Calculated Ratios

Stable isotopic labeling in proteomics

Capture and analysis of quantitative proteomic data

Contact Info

Product

Resources

About