CiteULike: nedwards's Moore

Hierarchical structure and the prediction of missing links in networks

Aaron Clauset — 2008-04-30T19:31:59-00:00

Nature, Vol. 453, No. 7191., pp. 98-101.

A rapid molecular assay for the detection of antibiotic resistance determinants in causal agents of infective endocarditis

JE Moore — 2007-12-11T15:11:30-00:00

Journal of Applied Microbiology, Vol. 90, No. 5. (2001), pp. 719-726.

Aims: To develop and employ a PCR amplification system, directly from clinical specimens, for the rapid molecular detection of common antimicrobial resistance genes for streptococci, staphylococci and enterococci organisms causing infective endocarditis (IE). Methods and Results: Eleven antibiotic resistance genes were targeted by PCR along with four identification-related loci. Blood culture and heart valve material from staphylococcal endocarditis patients were directly examined for methicillin resistance. PCR conditions were optimized for the following antibiotic resistance loci: staphylococci (mecA, aacA-aphD), streptococci (PBP 1A, PBP 2B, gyrB, parE) and enterococci (vanA, vanB, vanC-1, vanC-2, aacA-aphD, aphA3). The presence of methicillin resistance was confirmed in one of the eight IE patients examined. Conclusions: This study presents a PCR amplification system for the detection of antibiotic resistance genes. Detection of such genes may indicate susceptibility of the causal agents of IE to commonly prescribed antimicrobial agents. Significance and Impact of the Study: Rapid detection of antibiotic resistant organisms may reduce the use of inappropriate antibiotic agents or enable the use of the most appropriate combinations of antibiotics, other than those that would normally be prescribed empirically for IE. Such a method may be particularly valuable in cases of culture-negative endocarditis. Detection of antibiotic resistance genes by molecular-based techniques, namely PCR, will allow more directed antibiotic therapy and may also provide opportunities for earlier identification of resistant organisms.

Qscore: an algorithm for evaluating SEQUEST database search results.

RE Moore — 2007-08-30T15:27:40-00:00

J Am Soc Mass Spectrom, Vol. 13, No. 4. (April 2002), pp. 378-386.

A scoring procedure is described for measuring the quality of the results for protein identifications obtained from spectral matching of MS/MS data using the Sequest database search program. The scoring system is essentially probabilistic and operates by estimating the probability that a protein identification has come about by chance. The probability is based on the number of identified peptides from the protein, the total number of identified peptides, and the fraction of distinct tryptic peptides from the database that are present in the identified protein. The score is not strictly a probability, as it also incorporates information about the quality of the individual peptide matches. The result of using Qscore on a large test set of data was similar to that achieved using approaches that validate individual spectral matches, with only a narrow overlap in scores between identified proteins and false positive matches. In direct comparison with a published method of evaluating Sequest results, Qscore was able to identify an equivalent number of proteins without any identifiable false positive assignments. Qscore greatly reduces the number of Sequest protein identifications that have to be validated manually.