While recent advances in next-generation sequencing technologies have enabled the creation of a multitude of databases in cancer genomic research, there is no comprehensive database focusing on the annotation of driver indels (insertions and deletions) yet. Therefore, we have developed the database of Cancer driver InDels (dbCID), which is a collection of known coding indels that likely to be engaged in cancer development, progression or therapy. dbCID contains experimentally supported and putative driver indels derived from manual curation of literature and is freely available online at http://bioinfo.ahu.edu.cn:8080/dbCID. Using the data deposited in dbCID, we summarized features of driver indels in four levels (gene, DNA, transcript and protein) through comparing with putative neutral indels. We found that most of the genes containing driver indels in dbCID are known cancer genes playing a role in tumorigenesis. Contrary to the expectation, the sequences affected by driver frameshift indels are not larger than those by neutral ones. In addition, the frameshift and inframe driver indels prefer to disrupt high-conservative regions both in DNA sequences and protein domains. Finally, we developed a computational method for discriminating cancer driver from neutral frameshift indels based on the deposited data in dbCID. The proposed method outperformed other widely used non-cancer-specific predictors on an external test set, which demonstrated the usefulness of the data deposited in dbCID. We hope dbCID will be a benchmark for improving and evaluating prediction algorithms, and the characteristics summarized here may assist with investigating the mechanism of indel-cancer association.

中文翻译：

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dbCID：手动策划的资源，用于探索人类癌症中的驱动子插入/缺失。

尽管下一代测序技术的最新进展已使癌症基因组研究中可以创建大量数据库，但还没有一个全面的数据库专注于对驱动插入缺失（插入和缺失）的注释。因此，我们已经开发了癌症驱动程序InDels（dbCID）的数据库，该数据库是一组可能参与癌症发展，进展或治疗的已知编码indel的集合。dbCID包含实验支持的推定驱动程序indel，这些indel来自人工文献整理，可从http://bioinfo.ahu.edu.cn:8080/dbCID免费获得。使用存储在dbCID中的数据，我们通过与推定的中性插入缺失进行比较，在四个级别（基因，DNA，转录本和蛋白质）中总结了驱动插入缺失的特征。我们发现dbCID中大多数包含驱动插入缺失的基因都是已知的致癌基因。与预期相反，受驱动程序移入插入缺失影响的序列不大于受中性序列影响的序列。此外，移码和框内驱动插入缺失倾向于破坏DNA序列和蛋白质结构域中的高保守区。最后，我们开发了一种基于dbCID中存储的数据的将癌症驱动程序与中性移码插入/插入区分开的计算方法。所提出的方法在外部测试集上胜过其他广泛使用的非癌特异性预测变量，这证明了dbCID中存储的数据的有用性。我们希望dbCID将成为改进和评估预测算法的基准，