Depth normalization of small RNA sequencing: using data and biology to select a suitable method.

ABSTRACT: 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 (i) how effectively normalization removes handling artifacts and (ii) how aptly normalization

SUBMITTER: Duren Y 

PROVIDER: S-EPMC9177987 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature