The quality control of cancer somatic mutations has a great significance in the research of cancer genomics. It helps reduce the noise, systematic bias, as well as the false positive rate in dataset, and to pick out candidate mutations related to tumorigenesis. However, existing tools were not designed especially for cancer somatic mutations, and parameters and filtering standards used are different among them. Besides, there is no such a platform, database or tool stores or summarizes the standards for filtration of cancer somatic mutations applied in previous studies, which increases the time and energy spent on related research.
Therefore, we present this R package, CaMutQC, for the comprehensive filtration and selection of cancer somatic mutations for tumor-normal paired samples. CaMutQC is able to filter false positive mutations generated due to technical issues, as well as to select candidate cancer mutations through a series of well-structured functions by labeling mutations with various flags.
Also, a detailed and vivid filter report will be offered after completing a whole filtration or selection section.
- Through Bioconductor (recommended) to get the latest & most stable version:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("CaMutQC")- Through Github to get the latest version:
# install the latest version from GitHub
if(!require(devtools)) install.packages("devtools")
devtools::install_github("likelet/CaMutQC")This is a simple workflow of CaMutQC.
A detailed manual can be found here.
This software was mainly developed by:
- Xin Wang, [email protected]
- Jian Ren from School of Life Sciences in Sun Yat-sen University
- Qi Zhao from Bioinformatic Center of Sun Yat-sen University Cancer Center
Xin Wang, Jian Ren, Qi Zhao. Integrative quality control of cancer somatic mutations with CaMutQC [abstract]. In: Proceedings of the 113th Annual Meeting of the American Association for Cancer Research; 2022 April 8-13; New Orleans LA. Philadelphia (PA): AACR; 2022. Abstract nr 5004