Vinayagam Arunachalam, del Val Coral, Schubert Falk, Eils Roland, Glatting Karl-Heinz, Suhai Sándor, König Rainer
Department of Molecular Biophysics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69121 Heidelberg, Germany.
BMC Bioinformatics. 2006 Mar 20;7:161. doi: 10.1186/1471-2105-7-161.
Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions.
We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology (GO). Now, this method has been made available to the public via our web-service GOPET (Gene Ontology term Prediction and Evaluation Tool). It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar
Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user.
测序项目取得的巨大进展要求进行大规模注释。已经开发了许多方法来应对这一具有挑战性的任务。然而,这些方法要么适用于特定子集,要么其预测未形式化,要么它们没有为其预测提供精确的置信度值。
我们最近建立了一个用于自动注释的学习系统,使用多种不同的生物体进行训练,以预测来自基因本体论(GO)的标准化注释术语。现在,这种方法已通过我们的网络服务GOPET(基因本体论术语预测与评估工具)向公众开放。它为任何生物体的序列提供注释。对于每个预测的术语,都会提供一个适当的置信度值。基本方法是为预测分子功能GO术语而开发的。现在它已扩展到预测生物过程术语。该网络服务可通过http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar获得。
我们的网络服务为实验研究人员以及生物信息学社区提供了一个有价值的序列注释工具。此外,GOPET还提供不太重要的注释数据,可为用户提供一个扩展的发现平台。