Journal
GENOME BIOLOGY
Volume 16, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13059-015-0610-8
Keywords
-
Funding
- NIH [P30 DK089507]
- [DP2 AT007802-01]
Ask authors/readers for more resources
Functional metagenomic analyses commonly involve a normalization step, where measured levels of genes or pathways are converted into relative abundances. Here, we demonstrate that this normalization scheme introduces marked biases both across and within human microbiome samples, and identify sample- and gene-specific properties that contribute to these biases. We introduce an alternative normalization paradigm, MUSiCC, which combines universal single-copy genes with machine learning methods to correct these biases and to obtain an accurate and biologically meaningful measure of gene abundances. Finally, we demonstrate that MUSiCC significantly improves downstream discovery of functional shifts in the microbiome.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available