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MicroPheno: Predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples
View ORCID ProfileEhsaneddin Asgari, Kiavash Garakani, View ORCID ProfileAlice C. McHardy, View ORCID ProfileMohammad R.K. Mofrad
doi: https://doi.org/10.1101/255018
Ehsaneddin Asgari
1 Department of Bioengineering, University of California, Berkeley, CA 94720, USA
3 Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Brunswick 38124, Germany
Kiavash Garakani
2 Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA
Alice C. McHardy
3 Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Brunswick 38124, Germany
Mohammad R.K. Mofrad
1 Department of Bioengineering, University of California, Berkeley, CA 94720, USA
2 Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA
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Posted January 28, 2018.
MicroPheno: Predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples
Ehsaneddin Asgari, Kiavash Garakani, Alice C. McHardy, Mohammad R.K. Mofrad
bioRxiv 255018; doi: https://doi.org/10.1101/255018
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