A biological pathway database is a database that describes biochemical pathways, reactions, enzymes that catalyze the reactions, and the substrates that participate in these reactions. A pathway genome database (PGDB) integrates pathway information with information about the complete genome of various sequenced organisms. Two of the popular PGDBs available today are the Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaCyc. The proliferation of biological databases in general raises several questions for the life scientist. Which of these databases is most accurate, most current, or most comprehensive? Do they have a standard format? Do they complement each other? Overall, which database should be used for what purpose? If more than one database is deemed relevant, it is desirable to have a unified database containing information from all the shortlisted databases. There is no standard methodology yet for integrating biological pathway databases and, to the best of our knowledge, no commercial software that can perform such integration tasks. While XML based pathway data exchange standards such as BioPAX and SBML are emerging, these do not address the basic problems such as inconsistent nomenclature and substrate matching between databases in the unification of pathway databases. Here, we present the PathMeld methodology to unify KEGG and MetaCyc databases starting from their flat files. Individual PGDBs are transformed into a unified schema that we design. With individual PGDBs in the common unified schema, the key to the PathMeld methodology is to find the entity correspondences between the KEGG and MetaCyc substrates. We present a heuristic driven approach for one-to-one mapping of the substrates between KEGG and MetaCyc. Using the exact name and chemical formula match criteria, 82.6% of the substrates in MetaCyc were matched accurately to corresponding substrates in KEGG. The substrate names in the MetaCyc database contain html tags and non-characters such as <sub>, <sup>, <i>, <l>, &, and $. The MetaCyc chemical formula are stored in lisp format in the database while KEGG stores them as continuous strings. Hence, we subject MetaCyc chemical formulae to transformation into KEGG format to make them directly comparable. Applying pre-processing to transform MetaCyc substrate names and formulae improved substrate matching by 2%. To investigate how many of the remaining 17:4% substrates are indeed absent from KEGG, we employ a standard UNIX based approximate string matching tool called agrep. The resulting matches are curated into four mutually exlusive groups: 3:83% are correct matches, 3:17% are close matches, and 7:45% are incorrect matches. 3:68% of MetaCyc substrate names are not matched at all. This shows that 11:13% of MetaCyc substrate names are absent in KEGG. We note some of the implementation issues we solved. First, parsing only one flat file to populate one database table is not sufficient. Second, intermediate database tables are needed. Third, transformation of substrate names and chemical formula from one of the component databases is required for comparison. Fourth, a biochemist's intervention is needed in evaluating the approximate substrate matches from agrep. In conclusion, the PathMeld methodology successfully uni¯es KEGG and MetaCyc °at ¯le databases into a uni¯ed PostgreSQL database. Matching substrates between databases is the key issue in the uni¯cation process. About 83% of the substrate correspondences can be computationally achieved, while the remaining 17% substrates require approximate matching and manual curation by a biochemist. We presented several di®erent techniques for substrate matching and showed that about 10% of the MetaCyc substrates do not match and hence are absent from KEGG. / Master of Science

This project compared the performance of the correlation coefficient to show similarities in annotations between a predictive automated bacterial annotation database and the curated EcoCyc database. EcoCyc is a conservative multidimensional annotation system that is exclusively based on experimentally validated findings by over 15,000 publications. The automated annotation system, used in the comparison was BASys. It is often used as a first pass annotation tool that tries to add as many annotations as possible by drawing upon over 30 information sources. Gene ontology served as one basis of comparison between these databases because of the limited common terms in the ontology annotations. Translation libraries were used to extend the number of BASys terms that could be compared to the gene ontology terms in EcoCyc. Additional, non-ontology terms and metadata in BASys were compared to EcoCyc terms after parsing them into root words. The different term sources were quantitatively compared by using the correlation coefficient as the evaluation metric. The direct gene ontology comparison gave the lowest correlation coefficient. The addition of gene ontology terms to BASys by using translation tables of metadata greatly increased the correlation coefficient, which was comparable to the parsed word comparison. The combination of enhanced gene ontology and parsed word methods gave the highest correlation coefficient of 0.16. The controlled vocabulary system of gene ontology was not sufficient to compare two annotated databases. The addition of gene ontology terms from translation libraries greatly increased the performance of these comparisons. In general, as the number of comparison terms increased the correlation coefficient increased. Future comparisons should include the enhanced gene ontology dataset in order to monitor the organization pertaining to formal nomenclature and the datasets generated from Word parsing can be used to monitor the degree of additional terms might be incorporated with translation libraries.