利用不同G+C含量细菌基因组评估细菌ncRNA基因预测工具

Assessment of bacterial ncRNA gene prediction tools using bacterial genomes with different G+C content

摘要:

【目的】为识别已完成全测序细菌基因组中的ncRNA基因，对3个常用ncRNA预测工具sRNAPredict、PORTRAIT和sRNAscanner进行评估。【方法】选择了细菌ncRNA数据库BSRD收录的含有已知ncRNA基因数目大于30的9个细菌基因组，并按基因组G+C含量进行分类，比较sRNAPredict和PORTRAIT工具的预测准确性。提取不同G+C含量基因组中ncRNA基因转录起始和终止区的序列特征，对sRNAscanner预测结果进行评估。【结果】sRNAPredict对细菌ncRNA基因的预测特异性和阳性检出率均高于PORTRAIT，而敏感性则较差；两种工具预测效果均随基因组G+C含量不同而产生明显变化。在不同G+C含量的细菌基因组中，ncRNA基因启动子和终止子区域的序列特征有明显差异。利用这些序列特征能提高sRNAscanner预测ncRNA基因的平均水平。【结论】3种ncRNA基因工具预测效果随基因组G+C含量变化而不同。不同G+C含量基因组中ncRNA基因的转录起始和终止区特征可作为ncRNA基因预测的重要参数之一。

Abstract:

[Objective] Bacterial ncRNAs are a versatile class of non-coding RNA which plays an important role in the process of microbial life. In this study, we assess three ncRNA gene-prediction tools used frequently with the different bacterial genomes. [Methods] Prediction tools representing the position weight matrix method (sRNAscanner), comparative genomics method (sRNAPredict) and machine learning method (PORTRAIT) were tested by using 7 BSRD-archived bacterial genomes with low, middle and high G+C contents, each of which contains more than 30 experimentally verified ncRNA genes. A set of genomic G+C content-associated position weight matrixes of transcription initiation and termination regions of ncRNA genes was generated and employed to test sRNAscanner prediction. [Results] The sRNAPredict tool had higher specificity and positive prediction value, but lower sensitivity than PORTRAIT. The performance of both tools varied with the selected strains of different G+C contents. The obtained G+C content-associated matrix slightly improved the average accuracy of sRNAscanner. [Conclusion] The changing accuracy of the bacterial ncRNA gene detection tools under study was attributed to genomic G+C heterogeneity. Conserved sequence features of ncRNA gene promoters and terminators in genomes sharing similar G+C contents may be helpful to enhance bacterial ncRNA genes prediction.