BACKGROUND: Biological databases and pathway knowledge-bases are proliferating rapidly. We are developing software tools for computer-aided hypothesis design and evaluation, and we would like our tools to take advantage of the information stored in these repositories. But before we can reliably use a pathway knowledge-base as a data source, we need to proofread it to ensure that it can fully support computer-aided information integration and inference. RESULTS: We design a series of logical tests to detect potential problems we might encounter using a particular knowledge-base, the Reactome database, with a particular computer-aided hypothesis evaluation tool, HyBrow. We develop an explicit formal language from the language implicit in the Reactome data format and specify a logic to evaluate models expressed using this language. We use the formalism of finite model theory in this work. We then use this logic to formulate tests for desirable properties (such as completeness, consistency, and well-formedness) for pathways stored in Reactome. We apply these tests to the publicly available Reactome releases (releases 10 through 14) and compare the results, which highlight Reactome's steady improvement in terms of decreasing inconsistencies. We also investigate and discuss Reactome's potential for supporting computer-aided inference tools. CONCLUSION: The case study described in this work demonstrates that it is possible to use our model theory based approach to identify problems one might encounter using a knowledge-base to support hypothesis evaluation tools. The methodology we use is general and is in no way restricted to the specific knowledge-base employed in this case study. Future application of this methodology will enable us to compare pathway resources with respect to the generic properties such resources will need to possess if they are to support automated reasoning.

SUMMARY: Analysis of microarray data most often produces lists of genes with similar expression patterns, which are then subdivided into functional categories for biological interpretation. Such functional categorization is most commonly accomplished using Gene Ontology (GO) categories. Although there are several programs that identify and analyze functional categories for human, mouse and yeast genes, none of them accept Arabidopsis thaliana data. In order to address this need for A.thaliana community, we have developed a program that retrieves GO annotations for A.thaliana genes and performs functional category analysis for lists of genes selected by the user. AVAILABILITY: http://www.personal.psu.edu/nhs109/Clench

We describe two sets of programs for expediting routine tasks in analysis of cDNA microarray data and promoter sequences. The first set permits bad data points to be flagged with respect to a number of parameters and performs normalization in three different ways. It allows combining of result files into comprehensive data sets, evaluation of the quality of both technical and biological replicates and row and/or column standardization of data matrices. The second set supports mapping ESTs in the genome, identifying the corresponding genes and recovering their promoters, analyzing promoters for transcription factor binding sites, and visual representation of the results. The programs are designed primarily for Arabidopsis thaliana researchers, but can be adapted readily for other model systems. Availability and Supplementary information: http://www.personal.psu.edu/nhs109/Programs/