Data-independent acquisition (DIA) is a high-throughput, unbiased mass spectrometry data acquisition method which has good quantitative reproducibility and is friendly to low-abundance proteins. It becomes the preferred choice for clinical proteomic studies especially for large cohort studies in recent years. The mass-spectrometry (MS)/MS spectra generated by DIA is usually heavily mixed with fragment ion information of multiple peptides, which makes the protein identification and quantification more difficult. Currently, DIA data analysis methods fall into two main categories, namely peptide-centric and spectrum-centric. The peptide-centric strategy is more sensitive for identification and more accurate for quantification. Thus, it has become the mainstream strategy for DIA data analysis, which includes four key steps: building a spectral library, extracting ion chromatogram, feature scoring and statistical quality control. This work reviews the peptide-centric DIA data analysis procedure, introduces the corresponding algorithms and software tools, and summarizes the improvements for the existing algorithms. Finally, the future development directions are discussed.
Keywords: computational proteomics; data-independent acquisition; peptide-centric; quantitative proteomics.