A semi-automated genome annotation comparison and integration scheme
1 Computational Systems Biology and Bioinformatics, School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton Street, Edinburgh, EH8 9AB, UK
2 Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
3 Biological Systems Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa, Japan
BMC Bioinformatics 2013, 14:172 doi:10.1186/1471-2105-14-172Published: 1 June 2013
Different genome annotation services have been developed in recent years and widely used. However, the functional annotation results from different services are often not the same and a scheme to obtain consensus functional annotations by integrating different results is in demand.
This article presents a semi-automated scheme that is capable of comparing functional annotations from different sources and consequently obtaining a consensus genome functional annotation result. In this study, we used four automated annotation services to annotate a newly sequenced genome--Arcobacter butzleri ED-1. Our scheme is divided into annotation comparison and annotation determination sections. In the functional annotation comparison section, we employed gene synonym lists to tackle term difference problems. Multiple techniques from information retrieval were used to preprocess the functional annotations. Based on the functional annotation comparison results, we designed a decision tree to obtain a consensus functional annotation result. Experimental results show that our approach can greatly reduce the workload of manual comparison by automatically comparing 87% of the functional annotations. In addition, it automatically determined 87% of the functional annotations, leaving only 13% of the genes for manual curation. We applied this approach across six phylogenetically different genomes in order to assess the performance consistency. The results showed that our scheme is able to automatically perform, on average, 73% and 86% of the annotation comparison and determination tasks, respectively.
We propose a semi-automatic and effective scheme to compare and determine genome functional annotations. It greatly reduces the manual work required in genome functional annotation. As this scheme does not require any specific biological knowledge, it is readily applicable for genome annotation comparison and genome re-annotation projects.