Lynda B. M. Ellis

Using claims data for epidemiologic research. The concordance of claims-based criteria with the medical record and patient survey for identifying a hypertensive population.

In this study, a method was developed to identify health plan members with hypertension from insurance claims, using medical records and a patient survey for validation. A sample of 2,079 patients from two study sites with medical service or pharmacy claims indicating a diagnosis of essential hypertension were surveyed, and the medical records of 182 of the 1,275 survey respondents were reviewed. Where the criteria to identify hypertensive patients used both the medical and pharmacy claims, there was 96% agreement with either the medical record or the patient survey. Where the criteria relied on medical claims alone, the agreement rate decreased to 74% with the medical record and 64% with the patient survey. Where the criteria relied on the pharmacy claims alone, the agreement rate was 67% with the medical record and 75% with the patient survey. Combined evidence from medical service and pharmacy claims yielded a high level of agreement with alternative, more costly sources of data in identifying patients with essential hypertension. As it is more thoroughly investigated, claims data should become a more widely accepted resource for epidemiologic research.

The University of Minnesota Biocatalysis/ Biodegradation Database: the first decade

As the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, ) starts its second decade, it includes information on over 900 compounds, over 600 enzymes, nearly 1000 reactions and about 350 microorganism entries. Its Biochemical Periodic Tables have grown to include biological information for almost all stable, non-noble-gas elements (). Its Pathway Prediction System (PPS) () is now an internationally recognized, open system for predicting microbial catabolism of organic compounds. Graphical display of PPS rules, a stand-alone version of the PPS and guidance for PPS users are being developed. The next decade should see the PPS, and the UM-BBD on which it is based, find increasing use by national and international government agencies, commercial organizations and educational institutions.

The University of Minnesota Biocatalysis/Biodegradation Database: improving public access

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.msi.umn.edu/) began in 1995 and now contains information on almost 1200 compounds, over 800 enzymes, almost 1300 reactions and almost 500 microorganism entries. Besides these data, it includes a Biochemical Periodic Table (UM-BPT) and a rule-based Pathway Prediction System (UM-PPS) (http://umbbd.msi.umn.edu/predict/) that predicts plausible pathways for microbial degradation of organic compounds. Currently, the UM-PPS contains 260 biotransformation rules derived from reactions found in the UM-BBD and scientific literature. Public access to UM-BBD data is increasing. UM-BBD compound data are now contributed to PubChem and ChemSpider, the public chemical databases. A new mirror website of the UM-BBD, UM-BPT and UM-PPS is being developed at ETH Zürich to improve speed and reliability of online access from anywhere in the world.

Microbial genomics and the periodic table

Extensive knowledge of microbial metabolism has been earned through more than a century of reductionist study. There is now basic understanding of how cultivated bacteria transduce the chemical energy of a growth substrate into the work and biosynthetic processes that underlie both survival and

The University of Minnesota pathway prediction system: predicting metabolic logic

The University of Minnesota pathway prediction system (UM-PPS, http://umbbd.msi.umn.edu/predict/) recognizes functional groups in organic compounds that are potential targets of microbial catabolic reactions, and predicts transformations of these groups based on biotransformation rules. Rules are based on the University of Minnesota biocatalysis/biodegradation database (http://umbbd.msi.umn.edu/) and the scientific literature. As rules were added to the UM-PPS, more of them were triggered at each prediction step. The resulting combinatorial explosion is being addressed in four ways. Biodegradation experts give each rule an aerobic likelihood value of Very Likely, Likely, Neutral, Unlikely or Very Unlikely. Users now can choose whether they view all, or only the more aerobically likely, predicted transformations. Relative reasoning, allowing triggering of some rules to inhibit triggering of others, was implemented. Rules were initially assigned to individual chemical reactions. In selected cases, these have been replaced by super rules, which include two or more contiguous reactions that form a small pathway of their own. Rules are continually modified to improve the prediction accuracy; increasing rule stringency can improve predictions and reduce extraneous choices. The UM-PPS is freely available to all without registration. Its value to the scientific community, for academic, industrial and government use, is good and will only increase.

Evaluating eukaryotic secreted protein prediction

BackgroundImprovements in protein sequence annotation and an increase in the number of annotated protein databases has fueled development of an increasing number of software tools to predict secreted proteins. Six software programs capable of high throughput and employing a wide range of prediction methods, SignalP 3.0, SignalP 2.0, TargetP 1.01, PrediSi, Phobius, and ProtComp 6.0, are evaluated.ResultsPrediction accuracies were evaluated using 372 unbiased, eukaryotic, SwissProt protein sequences. TargetP, SignalP 3.0 maximum S-score and SignalP 3.0 D-score were the most accurate single scores (90–91% accurate). The combination of a positive TargetP prediction, SignalP 2.0 maximum Y-score, and SignalP 3.0 maximum S-score increased accuracy by six percent.ConclusionSingle predictive scores could be highly accurate, but almost all accuracies were slightly less than those reported by program authors. Predictive accuracy could be substantially improved by combining scores from multiple methods into a single composite prediction.

Genome-Wide Reverse Genetics Framework to Identify Novel Functions of the Vertebrate Secretome

Background Understanding the functional role(s) of the more than 20,000 proteins of the vertebrate genome is a major next step in the post-genome era. The approximately 4,000 co-translationally translocated (CTT) proteins – representing the vertebrate secretome – are important for such vertebrate-critical processes as organogenesis. However, the role(s) for most of these genes is currently unknown. Results We identified 585 putative full-length zebrafish CTT proteins using cross-species genomic and EST-based comparative sequence analyses. We further investigated 150 of these genes (Figure 1) for unique function using morpholino-based analysis in zebrafish embryos. 12% of the CTT protein-deficient embryos resulted in specific developmental defects, a notably higher rate of gene function annotation than the 2%–3% estimate from random gene mutagenesis studies. Conclusion(s) This initial collection includes novel genes required for the development of vascular, hematopoietic, pigmentation, and craniofacial tissues, as well as lipid metabolism, and organogenesis. This study provides a framework utilizing zebrafish for the systematic assignment of biological function in a vertebrate genome.

Encoding microbial metabolic logic: predicting biodegradation.

Prediction of microbial metabolism is important for annotating genome sequences and for understanding the fate of chemicals in the environment. A metabolic pathway prediction system (PPS) has been developed that is freely available on the world wide web (http://umbbd.ahc.umn.edu/predict/), recognizes the organic functional groups found in a compound, and predicts transformations based on metabolic rules. These rules are designed largely by examining reactions catalogued in the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) and are generalized based on metabolic logic. The predictive accuracy of the PPS was tested: (1) using a 113-member set of compounds found in the database, (2) against a set of compounds whose metabolism was predicted by human experts, and (3) for consistency with experimental microbial growth studies. First, the system correctly predicted known metabolism for 111 of the 113 compounds containing C and H, O, N, S, P and/or halides that initiate existing pathways in the database, and also correctly predicted 410 of the 569 known pathway branches for these compounds. Second, computer predictions were compared to predictions by human experts for biodegradation of six compounds whose metabolism was not described in the literature. Third, the system predicted reactions liberating ammonia from three organonitrogen compounds, consistent with laboratory experiments showing that each compound served as the sole nitrogen source supporting microbial growth. The rule-based nature of the PPS makes it transparent, expandable, and adaptable.

Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection

Meta-predictors make predictions by organizing and processing the predictions produced by several other predictors in a defined problem domain. A proficient meta-predictor not only offers better predicting performance than the individual predictors from which it is constructed, but it also relieves experimentally researchers from making difficult judgments when faced with conflicting results made by multiple prediction programs. As increasing numbers of predicting programs are being developed in a large number of fields of life sciences, there is an urgent need for effective meta-prediction strategies to be investigated. We compiled four unbiased phosphorylation site datasets, each for one of the four major serine/threonine (S/T) protein kinase families—CDK, CK2, PKA and PKC. Using these datasets, we examined several meta-predicting strategies with 15 phosphorylation site predictors from six predicting programs: GPS, KinasePhos, NetPhosK, PPSP, PredPhospho and Scansite. Meta-predictors constructed with a generalized weighted voting meta-predicting strategy with parameters determined by restricted grid search possess the best performance, exceeding that of all individual predictors in predicting phosphorylation sites of all four kinase families. Our results demonstrate a useful decision-making tool for analysing the predictions of the various S/T phosphorylation site predictors. An implementation of these meta-predictors is available on the web at: http://MetaPred.umn.edu/MetaPredPS/.

The University of Minnesota Biocatalysis/Biodegradation Database: emphasizing enzymes

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.ahc.umn.edu/) provides curated information on microbial catabolic enzymes and their organization into metabolic pathways. Currently, it contains information on over 400 enzymes. In the last year the enzyme page was enhanced to contain more internal and external links; it also displays the different metabolic pathways in which each enzyme participates. In collaboration with the Nomenclature Commission of the International Union of Biochemistry and Molecular Biology, 35 UM-BBD enzymes were assigned complete EC codes during 2000. Bacterial oxygenases are heavily represented in the UM-BBD; they are known to have broad substrate specificity. A compilation of known reactions of naphthalene and toluene dioxygenases were recently added to the UM-BBD; 73 and 108 were listed respectively. In 2000 the UM-BBD is mirrored by two prestigious groups: the European Bioinformatics Institute and KEGG (the Kyoto Encyclopedia of Genes and Genomes). Collaborations with other groups are being developed. The increased emphasis on UM-BBD enzymes is important for predicting novel metabolic pathways that might exist in nature or could be engineered. It also is important for current efforts in microbial genome annotation.