  Deep sequencing has become one of the most popular tools for transcriptome
profiling in biomedical studies. While an abundance of computational methods
exists for "normalizing" sequencing data to remove unwanted between-sample
variations due to experimental handling, there is no consensus on which
normalization is the most suitable for a given data set. To address this
problem, we developed "DANA" - an approach for assessing the performance of
normalization methods for microRNA sequencing data based on biology-motivated
and data-driven metrics. Our approach takes advantage of well-known biological
features of microRNAs for their expression pattern and chromosomal clustering
to simultaneously assess (1) how effectively normalization removes handling
artifacts, and (2) how aptly normalization preserves biological signals. With
DANA, we confirm that the performance of eight commonly used normalization
methods vary widely across different data sets and provide guidance for
selecting a suitable method for the data at hand. Hence, it should be adopted
as a routine preprocessing step (preceding normalization) for microRNA
sequencing data analysis. DANA is implemented in R and publicly available at
https://github.com/LXQin/DANA.
