Automatic Assignment of Prokaryotic Genes to Functional Categories Using Literature Profiling

In the last years, there was an exponential increase in the number of publicly available genomes. Once finished, most genome projects lack financial support to review annotations. A few of these gene annotations are based on a combination of bioinformatics evidence, however, in most cases, annotations are based solely on sequence similarity to a previously known gene, which was most probably annotated in the same way. As a result, a large number of predicted genes remain unassigned to any functional category despite the fact that there is enough evidence in the literature to predict their function. We developed a classifier trained with term-frequency vectors automatically disclosed from text corpora of an ensemble of genes representative of each functional category of the J. Craig Venter Institute Comprehensive Microbial Resource (JCVI-CMR) ontology. The classifier achieved up to 84% precision with 68% recall (for confidence≥0.4), F-measure 0.76 (recall and precision equally weighted) in an independent set of 2,220 genes, from 13 bacterial species, previously classified by JCVI-CMR into unambiguous categories of its ontology. Finally, the classifier assigned (confidence≥0.7) to functional categories a total of 5,235 out of the ∼24 thousand genes previously in categories “Unknown function” or “Unclassified” for which there is literature in MEDLINE. Two biologists reviewed the literature of 100 of these genes, randomly picket, and assigned them to the same functional categories predicted by the automatic classifier. Our results confirmed the hypothesis that it is possible to confidently assign genes of a real world repository to functional categories, based exclusively on the automatic profiling of its associated literature. The LitProf - Gene Classifier web server is accessible at: www.cebio.org/litprofGC.近年来，公开可用的基因组数量呈指数级增长。多数基因组项目在完成后，缺乏用于注释审核的资金支持。其中仅有少数基因注释结合了生物信息学证据，而绝大多数注释仅基于与已知基因的序列相似性——而该已知基因的注释大概率也采用了相同的方式。尽管已有足够文献证据可预测其功能，但仍有大量预测基因未被归入任何功能类别。我们基于克雷格·文特尔研究所综合微生物资源（J. Craig Venter Institute Comprehensive Microbial Resource, JCVI-CMR）本体中各功能类别代表性基因集合的文本语料库，自动提取词频向量，以此训练得到一款分类器。在取自13种细菌的2220个独立基因集中（这些基因此前已被JCVI-CMR归入其本体明确界定的功能类别），该分类器在置信度≥0.4时的精确率（precision）可达84%，召回率（recall）为68%，F值（F-measure，精确率与召回率加权相等时）为0.76。最终，针对MEDLINE数据库中存有文献的约2.4万个此前被归入"功能未知"或"未分类"类别的基因，该分类器为其中5235个（置信度≥0.7）分配了功能类别。两名生物学家对随机选取的100个此类基因的相关文献进行了审核，最终将这些基因归入的功能类别与自动分类器的预测结果完全一致。本研究结果证实了这一假说：仅通过对基因关联文献的自动分析，即可可靠地将真实数据库中的基因归入相应功能类别。LitProf-基因分类器（LitProf - Gene Classifier）的网页服务器可通过以下网址访问：www.cebio.org/litprofGC。