DNA copy number alteration is common in many cancers. Studies have shown that insertion or deletion of DNA sequences can directly alter gene expression, and significant correlation exists between DNA copy number and gene expression. Data normalization is a critical step in the analysis of gene expression generated by RNA-seq technology. Successful normalization reduces/removes unwanted nonbiological variations in the data, while keeping meaningful information intact. However, as far as we know, no attempt has been made to adjust for the variation due to DNA copy number changes in RNA-seq data normalization.
Now, a team led by researchers at Texas Tech University Health Sciences Center and the LSU Health Sciences Center propose an integrated approach for RNA-seq data normalization. Comparisons show that the proposed normalization can improve power for downstream differentially expressed gene detection and generate more biologically meaningful results in gene profiling. In addition, their findings show that due to the effects of copy number changes, some housekeeping genes are not always suitable internal controls for studying gene expression.
A flow chart of the proposed integrated normalization method
Using information from DNA copy number, integrated approach is successful in reducing noises due to both biological and nonbiological causes in RNA-seq data, thus increasing the accuracy of gene profiling.