title: 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 (R(2) = 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.
authors.affiliation: Institutes of Information Science, Academia Sinica, Taipei 11529, Taiwan.
authors.forename: Chih-Chiang
authors.initials: CC
authors.lastname: Tsou
authors.forename: Chia-Feng
authors.initials: CF
authors.lastname: Tsai
authors.forename: Ying-Hao
authors.initials: YH
authors.lastname: Tsui
authors.forename: Putty-Reddy
authors.initials: PR
authors.lastname: Sudhir
authors.forename: Yi-Ting
authors.initials: YT
authors.lastname: Wang
authors.forename: Yu-Ju
authors.initials: YJ
authors.lastname: Chen
authors.forename: Jeou-Yuan
authors.initials: JY
authors.lastname: Chen
authors.forename: Ting-Yi
authors.initials: TY
authors.lastname: Sung
authors.forename: Wen-Lian
authors.initials: WL
authors.lastname: Hsu
mesh_terms: D000465:Algorithms; D045744:Cell Line, Tumor; D029968:Escherichia coli Proteins / Q000032:analysis / Q000145:classification; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D010455:Peptides / Q000032:analysis*; D020543:Proteome / Q000032:analysis / Q000145:classification; D040901:Proteomics / Q000379:methods; D015203:Reproducibility of Results; D012984:Software
chemical_list: D029968:Escherichia coli Proteins; D010455:Peptides; D020543:Proteome