A systematic view of computational methods for identifying driver genes based on somatic mutation data

Abnormal changes of driver genes are serious for human health and biomedical research. Identifying driver genes, exactly from enormous genes with mutations, promotes accurate diagnosis and treatment of cancer. A lot of works about uncovering driver genes have been developed over the past decades. By analyzing previous works, we find that computational methods are more efficient than traditional biological experiments when distinguishing driver genes from massive data. In this study, we summarize eight common computational algorithms only using somatic mutation data. We first group these methods into three categories according to mutation features they apply. Then, we conclude a general process of nominating candidate cancer driver genes. Finally, we evaluate three representative methods on 10 kinds of cancer derived from The Cancer Genome Atlas Program and five Chinese projects from the International Cancer Genome Consortium. In addition, we compare results of methods with various parameters. Evaluation is performed from four perspectives, including CGC, OG/TSG, Q-value and QQQuantile-Quantileplot. To sum up, we present algorithms using somatic mutation data in order to offer a systematic view of various mutation features and lay the foundation of methods based on integration of mutation information and other types of data.

Automated summary

Abnormal changes in driver genes have serious implications for human health and biomedical research. Computational methods prove to be more efficient than traditional biological experiments in distinguishing driver genes from massive data. This study summarizes eight common computational algorithms using only somatic mutation data, categorizes them by mutation features, and presents a general process for nominating candidate cancer driver genes. Evaluation of three representative methods on various cancers from different sources is conducted, comparing results with different parameters for a systematic view of mutation features and a foundation for future integration of mutation data with other types of information.