The Fc receptor-cytoskeleton complex from human neutrophils

Creation of a federated database of blood proteins: a powerful new tool for finding and characterizing biomarkers in serum

“…The data analysis strategies generally advocate the storage of raw data in xml or text files with proteomics-specific software routines to manage, analyze and summarize the data [45-47]. In contrast, we proposed that the generic data analysis systems such as SQL, BLAST, and a generic statistical analysis system (SAS) may be used to organize and analyze proteomic data, using this broadly available and well tested software [10,12,20,23,29,48]. When considering the choice of a data analysis system, it is important to note the differences in proteomic versus genomic data.…”

“…Moreover the so called FDR used in proteomics, based on the empirical model, has been shown to disagree with classical statistical analyses by many orders of magnitude and to incorrectly reject well known blood proteins, including albumin, resulting is a large type II error (false negative) of protein identification [10,27]. Comparing the goodness-of-fit scores of authentic MS/MS spectra versus random or noise spectra [27,28] shows that the score distributions of real spectra correlations can be easily separated from false positive results [27,28] so long as the data are collected with a high signal to noise ratio [10-12]. …”

“…Confidence is known to increase with the number of peptides identified from each protein [25,26] and so the peptide to protein distribution of a data set is a simple means to infer the statistical reliability of the data [10-12]. Previously, control experiments using random libraries and random or noise spectra showed that roughly 88% of false positive proteins were identified by 1 peptide, about 11% of false positive hits show 2 peptides, and about 1% of false positive hits show 3 or more peptides: Experiments with random libraries of amino acid sequences, decoy libraries and random spectra all agree that proteins observed with at least 3 peptides show a false positive rate of less than 1% [27,28] and a low false positive rate (type I error) [25,27].…”

“…The proteins and endogenous peptides of human plasma may be randomly and independently sampled, identified by liquid chromatography and tandem mass spectrometry, followed by log transformation and comparative statistical analysis by ANOVA [10-12]; with the current generation of mass spectrometers, these processes are relatively simple and accessible to many laboratories. However, to facilitate routine use in biomarker studies, we have created a federated database of all proteins and peptides reported in blood.…”

“…Here we provide for the first time the specific identity of the set of cellular regulatory proteins detected with high confidence and good agreement internationally from human serum/plasma by liquid chromatography and tandem mass spectrometry that show great promise as potential biomarkers and therapeutic targets. The good agreement on peptides from the same cellular proteins by different research groups, together with the capacity of mass spectrometry to compare blood samples at the level of the parent peptide and fragment ion intensity using ANOVA, indicates that conditions for the detailed analysis of blood fluids between diseases and physiological states may now exist [10-12]. …”