Mathé C, Déhais P, Pavy N, Rombauts S, Van Montagu M, Rouzé P
Laboratorium voor Genetica, Department of Genetics, Flanders Interuniversity Institute for Biotechnology (VIB), Universiteit Gent, B-9000, Gent, Belgium.
J Biotechnol. 2000 Mar 31;78(3):293-9. doi: 10.1016/s0168-1656(00)00196-6.
Gene prediction methods for eukaryotic genomes still are not fully satisfying. One way to improve gene prediction accuracy, proven to be relevant for prokaryotes, is to consider more than one model of genes. Thus, we used our classification of Arabidopsis thaliana genes in two classes (CU(1) and CU(2)), previously delineated according to statistical features, in the GeneMark gene identification program. For each gene class, as well as for the two classes combined, a Markov model was developed (respectively, GM-CU(1), GM-CU(2) and GM-all) and then used on a test set of 168 genes to compare their respective efficiency. We concluded from this analysis that GM-CU(1) is more sensitive than GM-CU(2) which seems to be more specific to a gene type. Besides, GM-all does not give better results than GM-CU(1) and combining results from GM-CU(1) and GM-CU(2) greatly improve prediction efficiency in comparison with predictions made with GM-all only. Thus, this work confirms the necessity to consider more than one gene model for gene prediction in eukaryotic genomes, and to look for gene classes in order to build these models.
真核生物基因组的基因预测方法仍不尽人意。一种提高基因预测准确性的方法（已证明对原核生物有效）是考虑多种基因模型。因此，我们在GeneMark基因识别程序中，使用了先前根据统计特征划分的拟南芥基因的两类分类（CU(1)和CU(2)）。针对每个基因类别以及两类组合，开发了马尔可夫模型（分别为GM-CU(1)、GM-CU(2)和GM-all），然后将其应用于168个基因的测试集，以比较它们各自的效率。我们从该分析中得出结论，GM-CU(1)比GM-CU(2)更敏感，GM-CU(2)似乎对某一基因类型更具特异性。此外，GM-all的结果并不比GM-CU(1)好，与仅使用GM-all进行的预测相比，将GM-CU(1)和GM-CU(2)的结果相结合可大大提高预测效率。因此，这项工作证实了在真核生物基因组的基因预测中考虑多种基因模型以及寻找基因类别以构建这些模型的必要性。
