Biomedical subjects

Russ Greiner

Publications and source records attributed to Russ Greiner.

3 recordsLinked to original sources

PubMed2007

HMDB: the Human Metabolome Database.

The Human Metabolome Database (HMDB) is currently the most complete and comprehensive curated collection of human metabolite and human metabolism data in the world. It contains records for more than 2180 endogenous metabolites with information gathered from thousands of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the HMDB also contains an extensive collection of experimental metabolite concentration data compiled from hundreds of mass spectra (MS) and Nuclear Magnetic resonance (NMR) metabolomic analyses performed on urine, blood and cerebrospinal fluid samples. This is further supplemented with thousands of NMR and MS spectra collected on purified, reference metabolites. Each metabolite entry in the HMDB contains an average of 90 separate data fields including a comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, biofluid concentrations, disease associations, pathway information, enzyme data, gene sequence data, SNP and mutation data as well as extensive links to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided. The HMDB is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. The HMDB is available at: www.hmdb.ca.

Databases, Factual↗

PubMed2005

BASys: a web server for automated bacterial genome annotation.

BASys (Bacterial Annotation System) is a web server that supports automated, in-depth annotation of bacterial genomic (chromosomal and plasmid) sequences. It accepts raw DNA sequence data and an optional list of gene identification information and provides extensive textual annotation and hyperlinked image output. BASys uses >30 programs to determine approximately 60 annotation subfields for each gene, including gene/protein name, GO function, COG function, possible paralogues and orthologues, molecular weight, isoelectric point, operon structure, subcellular localization, signal peptides, transmembrane regions, secondary structure, 3D structure, reactions and pathways. The depth and detail of a BASys annotation matches or exceeds that found in a standard SwissProt entry. BASys also generates colorful, clickable and fully zoomable maps of each query chromosome to permit rapid navigation and detailed visual analysis of all resulting gene annotations. The textual annotations and images that are provided by BASys can be generated in approximately 24 h for an average bacterial chromosome (5 Mb). BASys annotations may be viewed and downloaded anonymously or through a password protected access system. The BASys server and databases can also be downloaded and run locally. BASys is accessible at http://wishart.biology.ualberta.ca/basys.

Chromosomes, Bacterial↗

PubMed2004

Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms.

Hereditary predisposition and causative environmental exposures have long been recognized in human malignancies. In most instances, cancer cases occur sporadically, suggesting that environmental influences are critical in determining cancer risk. To test the influence of genetic polymorphisms on breast cancer risk, we have measured 98 single nucleotide polymorphisms (SNPs) distributed over 45 genes of potential relevance to breast cancer etiology in 174 patients and have compared these with matched normal controls. Using machine learning techniques such as support vector machines (SVMs), decision trees, and naïve Bayes, we identified a subset of three SNPs as key discriminators between breast cancer and controls. The SVMs performed maximally among predictive models, achieving 69% predictive power in distinguishing between the two groups, compared with a 50% baseline predictive power obtained from the data after repeated random permutation of class labels (individuals with cancer or controls). However, the simpler naïve Bayes model as well as the decision tree model performed quite similarly to the SVM. The three SNP sites most useful in this model were (a) the +4536T/C site of the aldosterone synthase gene CYP11B2 at amino acid residue 386 Val/Ala (T/C) (rs4541); (b) the +4328C/G site of the aryl hydrocarbon hydroxylase CYP1B1 at amino acid residue 293 Leu/Val (C/G) (rs5292); and (c) the +4449C/T site of the transcription factor BCL6 at amino acid 387 Asp/Asp (rs1056932). No single SNP site on its own could achieve more than 60% in predictive accuracy. We have shown that multiple SNP sites from different genes over distant parts of the genome are better at identifying breast cancer patients than any one SNP alone. As high-throughput technology for SNPs improves and as more SNPs are identified, it is likely that much higher predictive accuracy will be achieved and a useful clinical tool developed.

Algorithms↗