Hu Gang-Qing, Zheng Xiaobin, Ju Li-Ning, Zhu Huaiqiu, She Zhen-Su
State Key Lab for Turbulence and Complex System and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China.
BMC Bioinformatics. 2008 Mar 25;9:160. doi: 10.1186/1471-2105-9-160.
Accurate annotation of translation initiation sites (TISs) is essential for understanding the translation initiation mechanism. However, the reliability of TIS annotation in widely used databases such as RefSeq is uncertain due to the lack of experimental benchmarks.
Based on a homogeneity assumption that gene translation-related signals are uniformly distributed across a genome, we have established a computational method for a large-scale quantitative assessment of the reliability of TIS annotations for any prokaryotic genome. The method consists of modeling a positional weight matrix (PWM) of aligned sequences around predicted TISs in terms of a linear combination of three elementary PWMs, one for true TIS and the two others for false TISs. The three elementary PWMs are obtained using a reference set with highly reliable TIS predictions. A generalized least square estimator determines the weighting of the true TIS in the observed PWM, from which the accuracy of the prediction is derived. The validity of the method and the extent of the limitation of the assumptions are explicitly addressed by testing on experimentally verified TISs with variable accuracy of the reference sets. The method is applied to estimate the accuracy of TIS annotations that are provided on public databases such as RefSeq and ProTISA and by programs such as EasyGene, GeneMarkS, Glimmer 3 and TiCo. It is shown that RefSeq's TIS prediction is significantly less accurate than two recent predictors, Tico and ProTISA. With convincing proofs, we show two general preferential biases in the RefSeq annotation, i.e. over-annotating the longest open reading frame (LORF) and under-annotating ATG start codon. Finally, we have established a new TIS database, SupTISA, based on the best prediction of all the predictors; SupTISA has achieved an average accuracy of 92% over all 532 complete genomes.
Large-scale computational evaluation of TIS annotation has been achieved. A new TIS database much better than RefSeq has been constructed, and it provides a valuable resource for further TIS studies.
准确注释翻译起始位点(TIS)对于理解翻译起始机制至关重要。然而,由于缺乏实验基准,诸如RefSeq等广泛使用的数据库中TIS注释的可靠性尚不确定。
基于基因翻译相关信号在基因组中均匀分布的同质性假设,我们建立了一种计算方法,用于大规模定量评估任何原核生物基因组TIS注释的可靠性。该方法包括根据三个基本位置权重矩阵(PWM)的线性组合对预测TIS周围的比对序列的PWM进行建模,其中一个用于真实TIS,另外两个用于错误TIS。这三个基本PWM是使用具有高度可靠TIS预测的参考集获得的。广义最小二乘估计器确定观察到的PWM中真实TIS的权重,由此得出预测的准确性。通过对参考集准确性不同的经实验验证的TIS进行测试,明确解决了该方法的有效性和假设局限性的程度。该方法用于估计公共数据库(如RefSeq和ProTISA)以及EasyGene、GeneMarkS、Glimmer 3和TiCo等程序提供的TIS注释的准确性。结果表明,RefSeq的TIS预测明显不如两个最新的预测器Tico和ProTISA准确。通过令人信服的证据,我们展示了RefSeq注释中的两种普遍的优先偏差,即对最长开放阅读框(LORF)注释过多和对ATG起始密码子注释不足。最后,我们基于所有预测器的最佳预测建立了一个新的TIS数据库SupTISA;SupTISA在所有532个完整基因组上的平均准确率达到了92%。
已实现对TIS注释的大规模计算评估。构建了一个比RefSeq好得多的新TIS数据库,为进一步的TIS研究提供了宝贵资源。