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Deep sequencing has become one of the most popular tools for transcriptomeprofiling in biomedical studies. While an abundance of computational methodsexists for "normalizing" sequencing data to remove unwanted between-samplevariations due to experimental handling, there is no consensus on whichnormalization is the most suitable for a given data set. To address thisproblem, we developed "DANA" - an approach for assessing the performance ofnormalization methods for microRNA sequencing data based on biology-motivatedand data-driven metrics. Our approach takes advantage of well-known biologicalfeatures of microRNAs for their expression pattern and chromosomal clusteringto simultaneously assess (1) how effectively normalization removes handlingartifacts, and (2) how aptly normalization preserves biological signals. WithDANA, we confirm that the performance of eight commonly used normalizationmethods vary widely across different data sets and provide guidance forselecting a suitable method for the data at hand. Hence, it should be adoptedas a routine preprocessing step (preceding normalization) for microRNAsequencing data analysis. DANA is implemented in R and publicly available athttps://github.com/LXQin/DANA.

Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method