miRNA-Seq normalization comparisons need improvement
- Xiaobei Zhou1,2,
- Alicia Oshlack3 and
- Mark D. Robinson1,2,4
- 1Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland
- 2SIB Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland
- 3Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, Victoria 3052, Australia
This extract was created in the absence of an abstract.
BACKGROUND
Currently there is no method of best practice for the normalization of microRNA sequencing data (miRNA-Seq). Therefore, we read with interest a recent article in RNA by Garmire and Subramaniam that set out to compare various normalization strategies specifically for this application (Garmire and Subramaniam 2012). They compared methods currently in use for normalization of messenger RNA sequencing (mRNA-Seq) data, such as total-depth normalization (“raw”) and Trimmed Mean of M-values (“TMM”). Additionally, they compared many methods not used previously with sequencing data, such as global scaling, and borrowed from strategies applied to microarray studies, such as quantile normalization (QN). The article attracted our attention for many reasons, but notably for the claimed poor performance and “abnormal results” of our TMM method (Robinson and Oshlack 2010). After investigating, we discovered that TMM’s claimed poor performance was the result of an error that shifted log-ratios in the wrong direction. Furthermore, we felt …
