Zeng Pan, Chen Ji, Meng Yuhong, Zhou Yuan, Yang Jichun, Cui Qinghua
School of Basic Medical Sciences, MOE Key Lab of Cardiovascular Sciences, Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Centre for Noncoding RNA Medicine, Peking University, Beijing, China.
Front Genet. 2018 Oct 9;9:380. doi: 10.3389/fgene.2018.00380. eCollection 2018.
Measuring the essentiality of genes is critically important in biology and medicine. Here we proposed a computational method, GIC (Gene Importance Calculator), which can efficiently predict the essentiality of both protein-coding genes and long noncoding RNAs (lncRNAs) based on only sequence information. For identifying the essentiality of protein-coding genes, GIC outperformed well-established computational scores. In an independent mouse lncRNA dataset, GIC also achieved an exciting performance (AUC = 0.918). In contrast, the traditional computational methods are not applicable to lncRNAs. Moreover, we explored several potential applications of GIC score. Firstly, we revealed a correlation between gene GIC score and research hotspots of genes. Moreover, GIC score can be used to evaluate whether a gene in mouse is representative for its homolog in human by dissecting its cross-species difference. This is critical for basic medicine because many basic medical studies are performed in animal models. Finally, we showed that GIC score can be used to identify candidate genes from a transcriptomics study. GIC is freely available at http://www.cuilab.cn/gic/.
衡量基因的必需性在生物学和医学中至关重要。在此，我们提出了一种计算方法GIC（基因重要性计算器），它仅基于序列信息就能高效预测蛋白质编码基因和长链非编码RNA（lncRNA）的必需性。在识别蛋白质编码基因的必需性方面，GIC优于已确立的计算得分。在一个独立的小鼠lncRNA数据集中，GIC也取得了令人振奋的表现（AUC = 0.918）。相比之下，传统的计算方法不适用于lncRNA。此外，我们探索了GIC得分的几种潜在应用。首先，我们揭示了基因GIC得分与基因研究热点之间的相关性。此外，通过剖析基因的跨物种差异，GIC得分可用于评估小鼠中的某个基因是否能代表其在人类中的同源基因。这对基础医学至关重要，因为许多基础医学研究是在动物模型中进行的。最后，我们表明GIC得分可用于从转录组学研究中识别候选基因。GIC可在http://www.cuilab.cn/gic/免费获取。
