Brouwer Rutger W W, Kuipers Oscar P, van Hijum Sacha A F T
Molecular Genetics Department of University of Groningen.
Brief Bioinform. 2008 Sep;9(5):367-75. doi: 10.1093/bib/bbn019. Epub 2008 Apr 17.
For most organisms, computational operon predictions are the only source of genome-wide operon information. Operon prediction methods described in literature are based on (a combination of) the following five criteria: (i) intergenic distance, (ii) conserved gene clusters, (iii) functional relation, (iv) sequence elements and (v) experimental evidence. The performance estimates of operon predictions reported in literature cannot directly be compared due to differences in methods and data used in these studies. Here, we survey the current status of operon prediction methods. Based on a comparison of the performance of operon predictions on Escherichia coli and Bacillus subtilis we conclude that there is still room for improvement. We expect that existing and newly generated genomics and transcriptomics data will further improve accuracy of operon prediction methods.
对于大多数生物体而言,计算操纵子预测是全基因组操纵子信息的唯一来源。文献中描述的操纵子预测方法基于以下五个标准(或其组合):(i)基因间距离,(ii)保守基因簇,(iii)功能关系,(iv)序列元件,以及(v)实验证据。由于这些研究中使用的方法和数据存在差异,文献中报道的操纵子预测性能估计无法直接进行比较。在此,我们调查了操纵子预测方法的当前状况。基于对大肠杆菌和枯草芽孢杆菌操纵子预测性能的比较,我们得出结论,仍有改进空间。我们预计,现有的以及新生成的基因组学和转录组学数据将进一步提高操纵子预测方法的准确性。