Improving the success rate of proteome analysis by modeling protein-abundance distributions and experimental designs.
Truly comprehensive proteome analysis is highly desirable in systems biology and biomarker discovery efforts. But complete proteome characterization has been hindered by the dynamic range and detection sensitivity of experimental designs, which are not adequate to the very wide range of protein abundances. Experimental designs for comprehensive analytical efforts involve separation followed by mass spectrometry-based identification of digested proteins. Because results are generally reported as a collection of identifications with no information on the fraction of the proteome that was missed, they are difficult to evaluate and potentially misleading. Here we address this problem by taking a holistic view of the experimental design and using computer simulations to estimate the success rate for any given experiment. Our approach demonstrates that simple changes in typical experimental designs can enhance the success rate of proteome analysis by five- to tenfold.
Eriksson
Jan
J
Department of Chemistry, Swedish University of Agricultural Sciences, Box 7015, SE-750 07, Uppsala, Sweden. Jan.eriksson@kemi.slu.se
Fenyö
David
D
D003198:Computer Simulation; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D010449:Peptide Mapping / Q000379:methods*; D020543:Proteome / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012107:Research Design; D012680:Sensitivity and Specificity; D016008:Statistical Distributions
D020543:Proteome