Core Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany.
Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, Berlin, 13092, Germany.
BMC Bioinformatics. 2019 Sep 2;20(1):450. doi: 10.1186/s12859-019-3043-7.
Mutational signatures are specific patterns of somatic mutations introduced into the genome by oncogenic processes. Several mutational signatures have been identified and quantified from multiple cancer studies, and some of them have been linked to known oncogenic processes. Identification of the processes contributing to mutations observed in a sample is potentially informative to understand the cancer etiology.
We present here SigsPack, a Bioconductor package to estimate a sample's exposure to mutational processes described by a set of mutational signatures. The package also provides functions to estimate stability of these exposures, using bootstrapping. The performance of exposure and exposure stability estimations have been validated using synthetic and real data. Finally, the package provides tools to normalize the mutation frequencies with respect to the tri-nucleotide contents of the regions probed in the experiment. The importance of this effect is illustrated in an example.
SigsPack provides a complete set of tools for individual sample exposure estimation, and for mutation catalogue & mutational signatures normalization.
突变特征是致癌过程将体细胞突变引入基因组的特定模式。已经从多个癌症研究中鉴定和量化了几种突变特征，其中一些与已知的致癌过程有关。确定导致样本中观察到的突变的过程对于了解癌症病因具有潜在的信息价值。
我们在这里介绍 SigsPack，这是一个 Bioconductor 包，用于估计样本对一组突变特征所描述的突变过程的暴露程度。该包还提供了使用引导法估计这些暴露稳定性的功能。使用合成数据和真实数据验证了暴露和暴露稳定性估计的性能。最后，该包提供了工具，可根据实验中探测到的区域的三核苷酸含量对突变频率进行归一化。在一个示例中说明了这种效果的重要性。
SigsPack 提供了一套完整的工具，用于单个样本暴露估计以及突变目录和突变特征的归一化。
