Estimating probabilities of peptide database identifications to LC-FTICR-MS observations

BACKGROUND: The field of proteomics involves the characterization of the peptides and proteins expressed in a cell under specific conditions. Proteomics has made rapid advances in recent years following the sequencing of the genomes of an increasing number of organisms. A prominent technology for high throughput proteomics analysis is the use of liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS). Meaningful biological conclusions can best be made when the peptide identities returned by this technique are accompanied by measures of accuracy and confidence. METHODS: After a tryptically digested protein mixture is analyzed by LC-FTICR-MS, the observed masses and normalized elution times of the detected features are statistically matched to the theoretical masses and elution times of known peptides listed in a large database. The probability of matching is estimated for each peptide in the reference database using statistical classification methods assuming bivariate Gaussian probability distributions on the uncertainties in the masses and the normalized elution times. RESULTS: A database of 69,220 features from 32 LC-FTICR-MS analyses of a tryptically digested bovine serum albumin (BSA) sample was matched to a database populated with 97% false positive peptides. The percentage of high confidence identifications was found to be consistent with other database search procedures. BSA database peptides were identified with high confidence on average in 14.1 of the 32 analyses. False positives were identified on average in just 2.7 analyses. CONCLUSION: Using a priori probabilities that contrast peptides from expected and unexpected proteins was shown to perform better in identifying target peptides than using equally likely a priori probabilities. This is because a large percentage of the target peptides were similar to unexpected peptides which were included to be false positives. The use of triplicate analyses with a "2 out of 3" reporting rule was shown to have excellent rejection of false positives

IT Data Mining Tool Uses in Aerospace

Data mining has a broad spectrum of uses throughout the realms of aerospace and information technology. Each of these areas has useful methods for processing, distributing, and storing its corresponding data. This paper focuses on ways to leverage the data mining tools and resources used in NASA's information technology area to meet the similar data mining needs of aviation and aerospace domains. This paper details the searching, alerting, reporting, and application functionalities of the Splunk system, used by NASA's Security Operations Center (SOC), and their potential shared solutions to address aircraft and spacecraft flight and ground systems data mining requirements. This paper also touches on capacity and security requirements when addressing sizeable amounts of data across a large data infrastructure

Protean and Boundaryless Career Attitudes: Do Teacher Candidates Have These?

Since the late 20th century, the Protean (Hall, 1996) and Boundaryless (Arthur, 1994) career concepts have been posited as explanations for employment transformations in corporate structures. While previous research (Briscoe, Hall, & Fratschy DeMuth, 2006) provides evidence of these constucts with business students, research has lacked in evaluating the Protean and Boundaryless Career Attitudes Scale (PBCAS) with other professions. The purpose of this study was to investigate the factor structure of the PBCAS with 350 undergraduate teacher candidates and to test the new model with a second sample (n = 194). The results showed moderate support for the validity of the PBCAS with teacher candidates. The data produced a five-factor model similar to the factor structure reported by de Bruin and Buchner (2010). These results support previous findings and indicate the need for further research with the instrument