Using functional and organizational information to improve genome-wide computational prediction of transcription units on pathway-genome databases
Using functional and organizational information to improve genome-wide computational prediction of transcription units on pathway-genome databases
复制标题
DOI：
10.1093/bioinformatics/btg471
复制
发表时间：
2004-03-22
期刊：
影响因子：
5.8
通讯作者：
Karp, PD
中科院分区：
文献类型：
--
作者：
Romero, PR;Karp, PD
动机：转录单位(TU，类似于操纵子)的预测是一个重要的问题，已经使用许多不同的方法解决了这个问题。获得完整的微生物基因组使全基因组的TU预测成为可能。路径基因组数据库(PGDB)将代谢和其他组织(即蛋白质复合体)信息添加到已注释的基因组中，并能够捕获TU组织信息。结果：我们实现了一种只利用基因间距离和基因功能分类来预测TU边界的TU预测器，并将其应用于我们的大肠杆菌PGDB EcoCyc。为了生成增强的预测值，我们在原始预测值的基础上添加了有关代谢途径、蛋白质复合体和转运蛋白的信息，所有这些都可以在EcoCyc中找到。增强的预测器正确预测了80%的已知E.Coli TU(69%的已知操纵子)，比原始预测器的性能(75%的TU和65%的操纵子正确预测)有了适度的改善，表明PGDB中可用的额外信息确实提高了预测性能。这个基于大肠杆菌的预测器在大肠杆菌以外的基因组上的性能是在BsubCyc上测试的，BsubCyc是我们为枯草杆菌计算生成的PGDB，它有一组100个已知的操纵子。预测精度大幅下降(46%的已知操作子被正确预测)。这在一定程度上是由于BsubCyc中缺少信息，这阻碍了预测器功能的充分使用。增强预测器已作为我们的路径工具软件套件的一部分实现，并可用于使用预测的TU填充PGDB。
Motivation: The prediction of transcription units (TUs, which are similar to operons) is an important problem that has been tackled using many different approaches. The availability of complete microbial genomes has made genome-wide TU predictions possible. Pathway-genome databases (PGDBs) add metabolic and other organizational (i.e. protein complexes) information to the annotated genome, and are able to capture TU organization information. These characteristics of PGDBs make them a suitable framework for the development and implementation of TU predictors.Results: We implemented a TU predictor that uses only intergenic distance and functional classification of genes to predict TU boundaries, and applied it to EcoCyc, our PGDB of Escherichia coli. To this original predictor, we added information on metabolic pathways, protein complexes and transporters, all readily available in EcoCyc, in order to generate an enhanced predictor. The enhanced predictor correctly predicted 80% of the known E.coli TUs (69% of the known operons), a moderate improvement over the original predictor's performance (75% of TUs and 65% of operons correctly predicted), demonstrating that the extra information available in the PGDB does indeed improve prediction performance. Performance of this E.coli-based predictor on a genome other than that of E.coli was tested on BsubCyc, our computationally generated PGDB for Bacillus subtilis, for which a set of 100 known operons is available. Prediction accuracy decreased substantially (46% of the known operons correctly predicted). This was due in part to missing information in BsubCyc, which prevented full use of the predictor's features. The augmented predictor has been implemented as part of our Pathway Tools software suite, and can be used to populate a PGDB with predicted TUs.