MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples

Ehsaneddin Asgari^{1

2}, Kiavash Garakani¹, Alice C McHardy², Mohammad R K Mofrad^{1

3}

Affiliations

¹ Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA.
² Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany and.
³ Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Lab, Berkeley, CA, USA.

Published Erratum

Ehsaneddin Asgari et al. Bioinformatics. 2019.

. 2019 Mar 15;35(6):1082.

doi: 10.1093/bioinformatics/bty652.

Ehsaneddin Asgari^{1

2}, Kiavash Garakani¹, Alice C McHardy², Mohammad R K Mofrad^{1

3}

¹ Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA.
² Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany and.
³ Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Lab, Berkeley, CA, USA.

No abstract available

Erratum for

MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples.
Asgari E, Garakani K, McHardy AC, Mofrad MRK. Asgari E, et al. Bioinformatics. 2018 Jul 1;34(13):i32-i42. doi: 10.1093/bioinformatics/bty296. Bioinformatics. 2018. PMID: 29950008 Free PMC article.