Title |
MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome
|
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Published in |
Genome Biology, March 2015
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DOI | 10.1186/s13059-015-0610-8 |
Pubmed ID | |
Authors |
Ohad Manor, Elhanan Borenstein |
Abstract |
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.MUSiCC is available at http://elbo.gs.washington.edu/software.html . |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | 26% |
United Kingdom | 4 | 12% |
France | 2 | 6% |
Sweden | 1 | 3% |
Denmark | 1 | 3% |
Finland | 1 | 3% |
Canada | 1 | 3% |
Spain | 1 | 3% |
China | 1 | 3% |
Other | 0 | 0% |
Unknown | 13 | 38% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 17 | 50% |
Scientists | 16 | 47% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 2% |
Canada | 3 | 1% |
Belgium | 2 | <1% |
India | 2 | <1% |
France | 1 | <1% |
Taiwan | 1 | <1% |
Germany | 1 | <1% |
Australia | 1 | <1% |
Mexico | 1 | <1% |
Other | 2 | <1% |
Unknown | 229 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 68 | 27% |
Student > Ph. D. Student | 63 | 25% |
Student > Master | 30 | 12% |
Student > Bachelor | 18 | 7% |
Student > Doctoral Student | 12 | 5% |
Other | 36 | 14% |
Unknown | 22 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 104 | 42% |
Biochemistry, Genetics and Molecular Biology | 38 | 15% |
Computer Science | 17 | 7% |
Medicine and Dentistry | 12 | 5% |
Immunology and Microbiology | 11 | 4% |
Other | 38 | 15% |
Unknown | 29 | 12% |