MetaPhlAn

MetaPhlAn is a computational tool for profiling the composition of microbial communities (Bacteria, Archaea, Eukaryotes and Viruses) from metagenomic shotgun sequencing data with species level resolution.
With StrainPhlAn, it is possible to identify specific strains (in the not-so-frequent cases in which the sample contains a previously sequenced strains) and to track strains across samples for all species.


 

MetaPhlAn 3 is now available!

MetaPhlAn 3 is available in Bioconda and can be installed with Conda by running:
conda install -c bioconda metaphlan


 

MetaPhlAn 3.0 useful links

Bug reports should be submitted via the repository issues tracking system.
For user support, consult the user manual or ask a question in our user support group, or contact directly the Segata lab.

MetaPhlAn 3.0 citation

If used MetaPhlAn 3.0 for your research, please consider citing the folliwing paper:

Francesco Beghini1 Lauren J McIver2 Aitor Blanco-Mìguez1 Leonard Dubois1 Francesco Asnicar1 Sagun Maharjan2,3 Ana Mailyan2,3 Andrew Maltez Thomas1 Paolo Manghi1 Matthias Scholz2,3 Mireia Valles-Colomer1 George Weingart2,3 Yancong Zhang2,3 Moreno Zolfo1 Curtis Huttenhower2,3 Eric A Franzosa2,3 Nicola Segata1,5

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

eLife(2021) https://doi.org/10.7554/eLife.65088

1 Department CIBIO, University of Trento, Italy

2 Harvard T. H. Chan School of Public Health, Boston, MA, USA

3 The Broad Institute of MIT and Harvard, Cambridge, MA, USA

4 Department of Food Quality and Nutrition, Research and Innovation Center, Edmund Mach Foundation, Italy

5 IEO, European Institute of Oncology IRCCS, Milan, Italy


 

MetaPhlAn2

If you are interested in using the previous version, MetaPhlAn2, the source code is available on GitHub.
We advise to install it using Conda by running:
conda install -c bioconda metaphlan2=2.8

MetaPhlAn2 for enhanced metagenomic taxonomic profiling

Duy Tin Truong Eric Franzosa Timothy L Tickle Matthias Scholz George Weingart Edoardo Pasolli Adrian Tett Curtis Huttenhower Nicola Segata

Nature Methods https://doi.org/10.1038/nmeth.3589