measurement errors systematically and quantitatively using the natural properties of isotopic distributions.Then,we propose an algorithm for label-free absolute protein quantification,LFAQ,which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides.When validated on datasets produced by different MS instruments and data acquisition modes,LFAQ presented accuracy and precision superior to those of existing methods.In particular,it reduced the quantification error by an average of 46%for low-abundance proteins.The advantages of LFAQ were further confirmed using the data from published papers.