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 benchmarking software is freely available in the latest
G-language Genome Analysis Environment package, at
http://www.g-language.org/.
