Han Heonjong, Lehner Ben, Lee Insuk
Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, South Korea.
EMBL-CRG Systems Biology Unit, Centre for Genomic Regulation (CRG), Barcelona, Spain.
Methods Mol Biol. 2019;1907:37-50. doi: 10.1007/978-1-4939-8967-6_3.
Identifying genes that are capable of inducing tumorigenesis has been a major challenge in cancer research. In many cases, such genes frequently show somatic mutations in tumor samples; thus various computational methods for predicting cancer genes have been developed based on "significantly mutated genes." However, this approach is intrinsically limited by the fact that there are many cancer genes infrequently mutated in cancer genomes. Therefore, we recently developed MUFFINN (Mutations For Functional Impact on Network Neighbors), a method for cancer gene prediction based not only on mutation occurrences in each gene but also those of neighbors in functional networks. This enables the identification of cancer genes with infrequent mutation occurrence. We demonstrated that MUFFINN could retrieve known cancer genes more efficiently than gene-based methods and predicted cancer genes with low mutation occurrences in tumor samples. Users can freely access a web server ( http://www.inetbio.org/muffinn ) and run predictions with either public or private data of cancer somatic mutations. For given information of mutation occurrence profiles, the MUFFINN server returns lists of candidate cancer genes by four distinct predictions with different combinations between gene networks and scoring algorithms. Stand-alone software is also available, which allows MUFFINN to be run on local machines with a custom gene network. Here, we present an overall guideline for using the MUFFINN web server and stand-alone software for the discovery of novel cancer genes.
识别能够诱导肿瘤发生的基因一直是癌症研究中的一项重大挑战。在许多情况下，此类基因在肿瘤样本中经常出现体细胞突变；因此，基于“显著突变基因”开发了各种预测癌症基因的计算方法。然而，这种方法本质上受到以下事实的限制：在癌症基因组中，有许多癌症基因很少发生突变。因此，我们最近开发了MUFFINN（对网络邻居功能有影响的突变），这是一种不仅基于每个基因的突变发生情况，还基于功能网络中邻居的突变发生情况来预测癌症基因的方法。这使得能够识别那些很少发生突变的癌症基因。我们证明，与基于基因的方法相比，MUFFINN能够更有效地检索已知的癌症基因，并预测肿瘤样本中低突变发生率的癌症基因。用户可以免费访问一个网络服务器（http://www.inetbio.org/muffinn），并使用癌症体细胞突变的公共或私人数据进行预测。对于给定的突变发生概况信息，MUFFINN服务器通过基因网络和评分算法之间不同组合的四种不同预测返回候选癌症基因列表。也有独立软件可用，它允许MUFFINN在带有自定义基因网络的本地机器上运行。在这里，我们提供了一份使用MUFFINN网络服务器和独立软件发现新型癌症基因的总体指南。
