Motivation: The massively parallel sequencing technology can be used by small research labs to generate genome sequences of their research interest. However, annotation of genomes still relies on the manual process, which becomes a serious bottleneck to the high-throughput genome projects. Recently, automatic annotation methods are increasingly more accurate, but there are several issues. One important challenge in using automatic annotation methods is to distinguish annotation quality of ORFs or genes. The availability of such annotation quality of genes can reduce the human labor cost dramatically since manual inspection can focus only on genes with low-annotation quality scores.
Results: In this article, we propose a novel annotation quality or confidence scoring scheme, called Annotation Confidence Score (ACS), using a genome comparison approach. The scoring scheme is computed by combining sequence and textual annotation similarity using a modified version of a logistic curve. The most important feature of the proposed scoring scheme is to generate a score that reflects the excellence in annotation quality of genes by automatically adjusting the number of genomes used to compute the score and their phylogenetic distance. Extensive experiments with bacterial genomes showed that the proposed scoring scheme generated scores for annotation quality according to the quality of annotation regardless of the number of reference genomes and their phylogenetic distance.
Availability: http://microbial.informatics.indiana.edu/acs