IDEAL-Q, an Automated Tool for Label-free Quantitation Analysis Using an Efficient Peptide Alignment Approach and Spectral Data Validation

Abstract

In this study, we present a fully automated tool, called IDEAL-Q, for label-free quantitation analysis. It accepts raw data in the standard mzXML format as well as search results from major search engines, including Mascot, SEQUEST, and X!Tandem, as input data. To quantify as many identified peptides as possible, IDEAL-Q uses an efficient algorithm to predict the elution time of a peptide unidentified in a specific LC-MS/MS run but identified in other runs. Then, the predicted elution time is used to detect peak clusters of the assigned peptide. Detected peptide peaks are processed by statistical and computational methods and further validated by signal-to-noise ratio, charge state, and isotopic distribution criteria (SCI validation) to filter out noisy data. The performance of IDEAL-Q has been evaluated by several experiments. First, a serially diluted protein mixed with Escherichia coli lysate showed a high correlation with expected ratios and demonstrated good linearity (R2 = 0.996). Second, in a biological replicate experiment on the THP-1 cell lysate, IDEAL-Q quantified 87% (1,672 peptides) of all identified peptides, surpassing the 45.7% (909 peptides) achieved by the conventional identity-based approach, which only quantifies peptides identified in all LC-MS/MS runs. Manual validation on all 11,940 peptide ions in six replicate LC-MS/MS runs revealed that 97.8% of the peptide ions were correctly aligned, and 93.3% were correctly validated by SCI. Thus, the mean of the protein ratio, 1.00 ± 0.05, demonstrates the high accuracy of IDEAL-Q without human intervention. Finally, IDEAL-Q was applied again to the biological replicate experiment but with an additional SDS-PAGE step to show its compatibility for label-free experiments with fractionation. For flexible workflow design, IDEAL-Q supports different fractionation strategies and various normalization schemes, including multiple spiked internal standards. User-friendly interfaces are provided to facilitate convenient inspection, validation, and modification of quantitation results. In summary, IDEAL-Q is an efficient, user-friendly, and robust quantitation tool. It is available for download.

Alterations of the serum peptidome in renal cell carcinoma discriminating benign and malignant kidney tumors

“…Normalization was performed using the median of peptide abundance and quantile of the peptide ratio level [34]. Protein abundances were determined using the same parameter settings and a peptide score threshold of 37 (score for identity or for extensive homology) as calculated by Mascot algorithm.…”

Research on Efficient Algorithm of Peptide Identification and Quantification in Proteomics

“…In terms of the number of peptides identified only by XICs, the peptides identified by XICFinder are significantly more than peptides, which were identified by MaxQuant [20] and IDEAL-Q [21].The number of peptide identification by three algorithms is shown in Figure 3(left). XICFinder found 3798 XICs, and identified 2744 peptides-1470 peptides were identified by MS/MS spectrum and 974 were identified by AMT matches based on peptide detectability prediction.…”

Recent advances on multidimensional liquid chromatography–mass spectrometry for proteomics: From qualitative to quantitative analysis—A review

“…The core concept of this algorithm is that if an accurate m/z is acquired, plus the pairwise accurate retention time, this ion does not need to be verified by MS/MS. Several software have employed the similar concept, as implemented in IDEAL-Q [142] and Progenesis TM . The introduction of such a concept proposes very high demand for the reproducibility of LC setups [134,142].…”

“…Several software have employed the similar concept, as implemented in IDEAL-Q [142] and Progenesis TM . The introduction of such a concept proposes very high demand for the reproducibility of LC setups [134,142]. In conclusion, one should bear in mind that the choice of algorithms for reporting label-free quantification results is highly correlated with the platform setups.…”