Experience report: issues in comparing gene function annotation in text

Annotating function of genes accurately is one of the most important tasks in molecular biology and medical sciences. The new sequencing technology, called the next generation sequencing technology, made sequencing the whole genomes possible with a fraction of cost of sequencing by using the traditional sequencing technology. As a result, the amount of sequence data has been growing very rapidly, but the computational method for gene function annotation is yet to be fully developed. Thus annotation of gene function is a serious bottleneck to achieving the high-throughput genome projects. The most commonly used gene annotation technique is to transfer annotation of genes based on the sequence similarity; annotation of top-ranked genes in terms of sequence similarity is simply transferred to the function of a target gene. However, this sequence-similarity based gene function annotation is often incorrect. As a result, genome projects still rely on expensive, error-prone, labor-intensive, manual process. Combining annotation and sequence similarity can improve the accuracy of gene function annotation significantly. We have been developing a computational method for comparing gene annotation in text. In this paper, we will discuss issues in comparing genome annotation in a text format. To compute textual similarity, we used cosine similarity. Since cosine similarity is effective only after preprocessing with textual variations, we used commonly used text preprocessing techniques such as removing stop words and stemming as well as gene annotation specific preprocessing such as handling synonyms and gene symbols using databases of biology terminologies such as BioThesaurus and MeSH. In experiments with annotations of a number of bacterial genomes, our method was able to handle many difficult cases (syntactically different but semantically equivalent gene function annotations) correctly.