Rate Of False Positive Identification Research Articles

A Comprehensive Analysis of the 14-3-3 Interactome in Barley Leaves Using a Complementary Proteomics and Two-Hybrid Approach

This study describes the identification of over 150 target proteins of the five 14-3-3 isoforms in 7-d-old barley (Hordeum vulgare) cv Himalaya seedlings using yeast two-hybrid screens complemented with 14-3-3 protein affinity purification and tandem mass spectrometry. Independent experiments for a subset of genes confirmed the yeast two-hybrid interactions, demonstrating a low false positive identification rate. These combined approaches resulted in the identification of more than 150 putative targets; 15% were previously reported to be 14-3-3 interactors, including, for example, Serpin, RF2A, WPK4 kinase, P-type proton-translocating adenosine triphosphatase, EF1A, glutamine synthetase, and invertases. The affinity purification resulted in 30 interactors, of which 44% function in metabolism, while the yeast two-hybrid screens identified 132 different proteins, with 35% of the proteins involved in signal transduction. A number of proteins have a well-described function in hormonal signaling, such as the auxin transport protein PIN1 and NPH3 and components of the brassinosteroid pathway, such as the receptor kinase BAK1 (OsPERK1) and BRI1-kinase domain-interacting protein 129. However, 14-3-3 interactions with these signal mediators have not been confirmed in the affinity purification. Confirmations of the 14-3-3 interaction with the three ABF-like transcription factors are shown using far western analysis. Also, a REPRESSION OF SHOOT GROWTH ortholog named RF2A was identified; these transcription factors play important roles in the abscisic acid and gibberellin pathways, respectively. We speculate that 14-3-3 proteins have a role in cross talk between these hormonal pathways. The specificity and complementary nature of both the affinity purification and the yeast two-hybrid approaches is discussed.

MAPU: Max-Planck Unified database of organellar, cellular, tissue and body fluid proteomes

Mass spectrometry (MS)-based proteomics has become a powerful technology to map the protein composition of organelles, cell types and tissues. In our department, a large-scale effort to map these proteomes is complemented by the Max-Planck Unified (MAPU) proteome database. MAPU contains several body fluid proteomes; including plasma, urine, and cerebrospinal fluid. Cell lines have been mapped to a depth of several thousand proteins and the red blood cell proteome has also been analyzed in depth. The liver proteome is represented with 3200 proteins. By employing high resolution MS and stringent validation criteria, false positive identification rates in MAPU are lower than 1:1000. Thus MAPU datasets can serve as reference proteomes in biomarker discovery. MAPU contains the peptides identifying each protein, measured masses, scores and intensities and is freely available at using a clickable interface of cell or body parts. Proteome data can be queried across proteomes by protein name, accession number, sequence similarity, peptide sequence and annotation information. More than 4500 mouse and 2500 human proteins have already been identified in at least one proteome. Basic annotation information and links to other public databases are provided in MAPU and we plan to add further analysis tools.

Randomized Sequence Databases for Tandem Mass Spectrometry Peptide and Protein Identification

Tandem mass spectrometry (MS/MS) combined with database searching is currently the most widely used method for high-throughput peptide and protein identification. Many different algorithms, scoring criteria, and statistical models have been used to identify peptides and proteins in complex biological samples, and many studies, including our own, describe the accuracy of these identifications, using at best generic terms such as "high confidence." False positive identification rates for these criteria can vary substantially with changing organisms under study, growth conditions, sequence databases, experimental protocols, and instrumentation; therefore, study-specific methods are needed to estimate the accuracy (false positive rates) of these peptide and protein identifications. We present and evaluate methods for estimating false positive identification rates based on searches of randomized databases (reversed and reshuffled). We examine the use of separate searches of a forward then a randomized database and combined searches of a randomized database appended to a forward sequence database. Estimated error rates from randomized database searches are first compared against actual error rates from MS/MS runs of known protein standards. These methods are then applied to biological samples of the model microorganism Shewanella oneidensis strain MR-1. Based on the results obtained in this study, we recommend the use of use of combined searches of a reshuffled database appended to a forward sequence database as a means providing quantitative estimates of false positive identification rates of peptides and proteins. This will allow researchers to set criteria and thresholds to achieve a desired error rate and provide the scientific community with direct and quantifiable measures of peptide and protein identification accuracy as opposed to vague assessments such as "high confidence."

Mass spectrometry of the M. smegmatis proteome: Protein expression levels correlate with function, operons, and codon bias

The fast-growing bacterium Mycobacterium smegmatis is a model mycobacterial system, a nonpathogenic soil bacterium that nonetheless shares many features with the pathogenic Mycobacterium tuberculosis, the causative agent of tuberculosis. The study of M. smegmatis is expected to shed light on mechanisms of mycobacterial growth and complex lipid metabolism, and provides a tractable system for antimycobacterial drug development. Although the M. smegmatis genome sequence is not yet completed, we used multidimensional chromatography and tandem mass spectrometry, in combination with the partially completed genome sequence, to detect and identify a total of 901 distinct proteins from M. smegmatis over the course of 25 growth conditions, providing experimental annotation for many predicted genes with an approximately 5% false-positive identification rate. We observed numerous proteins involved in energy production (9.8% of expressed proteins), protein translation (8.7%), and lipid biosynthesis (5.4%); 33% of the 901 proteins are of unknown function. Protein expression levels were estimated from the number of observations of each protein, allowing measurement of differential expression of complete operons, and the comparison of the stationary and exponential phase proteomes. Expression levels are correlated with proteins' codon biases and mRNA expression levels, as measured by comparison with codon adaptation indices, principle component analysis of codon frequencies, and DNA microarray data. This observation is consistent with notions that either (1) prokaryotic protein expression levels are largely preset by codon choice, or (2) codon choice is optimized for consistency with average expression levels regardless of the mechanism of regulating expression.

Antecedents of seizure disorders in early childhood.

One or more nonfebrile seizures occurred between the ages of 1 month and 7 years in eight per 1000 white and in nine per 1000 black children enrolled in a large prospective study. We examined hundreds of prenatal and perinatal factors as predictors of childhood seizure disorders. Congenital malformations of the fetus (cerebral and noncerebral), family history of certain neurologic disorders, and neonatal seizures were the major predictors identified. Forty percent of children with postneonatal seizures and 68% of children with minor motor seizures had one or more of these risk factors compared with 21% of the seizure-free population. More than half of the children with minor motor seizures and a third of the infants with neonatal seizures had congenital malformations. Based on the prenatal and perinatal factors examined, prediction of postneonatal seizures carried a high rate of false-positive identification, indicating that our knowledge of the etiology of childhood seizure disorders is still very limited.

Computer detection of premature ventricular complexes: A modified approach

The accuracy of a data reduction system for arrhythmia detection in identifying premature ventricular complexes was evaluated in continuous tape records of 30 patients in a coronary care unit. Computer analysis was performed with a Honeywell 316 digital computer. Threshold values for dominant complexes were automatically determined and recognition of premature ventricular complexes was based on differences in QRS configuration, timing and T wave configuration from the dominant complexes. Verification of the computer accuracy in detecting premature ventricular complexes was made with visual beat by beat inspection using a two channel strip chart recorder with simultaneous recording of the electrocardiogram and computer signal. This procedure allowed for exact beat to beat correlation and, thus, absolute determination of false positive and false negative identifications. From 0.5 to 6 continuous hours of monitoring per patient (average 3.5 hours) were analyzed for a total of 105 monitoring hours. The basic cardiac rhythms noted were normal sinus rhythm, sinus arrhythmia, sinus tachycardia, demand pacemaker rhythm, atrial fibrillation and atrioventricular (A-V) dissociation with junctional rhythm. Premature ventricular complexes were evident in 28 tapes (93 percent) including 12 (43 percent) with multifocal premature ventricular complexes and 3 (11 percent) with ventricular tachycardia. The visual count of premature ventricular complexes totaled 7,921. Of these, 7,542 (95 percent) were properly classified by the computer. The total computer count was 8,717, representing a 13 percent false positive and 5 percent false negative identification rate. The false positive identifications of premature ventricular complexes occurred during periods of 10 seconds or more of continuous noise artifact and in the presence of atrial premature complexes conducted aberrantly. When these sections of tape were excluded, the computer had a less than 2 percent false negative and 3 percent false positive rate of identification of premature ventricular complexes.