Abstract
Background: Next generation sequencing (NGS) is a widely used technology in both basic research and clinical settings and it will continue to have a major impact on biomedical sciences. However, the use of incorrect normalization methods can lead to systematic biases and spurious results, making the selection of an appropriate normalization strategy a crucial and often overlooked part of NGS analysis.
Results: We present a basic introduction to the currently available normalization methods for differential expression and ChIP-seq applications, along with best use recommendations for different experimental techniques and datasets.
We demonstrate that the choice of normalization technique can have a significant impact on the number of genes called as differentially expressed in an RNA-seq experiment or peaks called in a ChIP-seq experiment.
Conclusions: The choice of the most adequate normalization method depends on both the distribution of signal in the dataset and the intended downstream applications. Depending on the design and purpose of the study, appropriate bias correction should also be considered.
