title: GPAC: benchmarking the sensitivity of genome informatics analysis to genome annotation completeness.
abstract: In view of the recent explosion in genome sequence data, and the 200 or more complete genome sequences currently available, the importance of genome-scale bioinformatics analysis is increasing rapidly. However, computational genome informatics analyses often lack a statistical assessment of their sensitivity to the completeness of the functional annotation. Therefore, a pre-analysis method to automatically validate the sensitivity of computational genome analyses with regard to genome annotation completeness is useful for this purpose. In this report we developed the Gene Prediction Accuracy Classification (GPAC) test, which provides statistical evidence of sensitivity by repeating the same analysis for five different gene groups (classified according to annotation accuracy level), and for randomly sampled gene groups, with the same number of genes as each of the five classified groups. Variability in these results is then assessed, and if the results vary significantly with different data subsets, the analysis is considered "sensitive" to annotation completeness, and careful selection of data is advised prior to the actual in silico analysis. The GPAC test has been applied to the analyses of Sakai et al., 2001, and Ohno et al., 2001, and it revealed that the analysis of Ohno et al. was more sensitive to annotation completeness. It showed that GPAC could be employed to ascertain the sensitivity of an analysis. The GPAC bendhmarking software is freely available in the latest G-language Genome Analysis Environment package, at http://www.g-language.org/.
authors.affiliation: Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520 Kanagawa, Japan.
authors.forename: Kazuharu
authors.initials: K
authors.lastname: Arakawa
authors.forename: Yoichi
authors.initials: Y
authors.lastname: Nakayama
authors.forename: Masaru
authors.initials: M
authors.lastname: Tomita
mesh_terms: D019985:Benchmarking; D019295:Computational Biology / Q000592:standards / Q000706:statistics & numerical data; D030541:Databases, Genetic; D016678:Genome; D023281:Genomics / Q000592:standards / Q000706:statistics & numerical data; D015203:Reproducibility of Results