I installed the latest metaphlan 4.0.6. I want to install Humann3 using conda. It might automatically install the latest version of Humann3.
In the forum, I saw Humann3 is comparable with metaphlan 4.0.3. Do I have to install specific 4.0.3 metaphlan for humann3 or is there a way to make them (4.0.6 and humann3) comparable?
I have installed metaphlan4.06 and humann3.7. I have succeffully ran metaphlan and humann on my metagenomics data with metaphlan database of mpa_vOct22_CHOCOPhlAnSGB_202212.
However, it goes wrong while I was running humann on my metatranscriptomics data with the maximum taxonomic profile generated by metaphlan4. The error looks like this.
ERROR: The MetaPhlAn taxonomic profile provided was not generated with the expected database version. Please update your version of MetaPhlAn to at least v3.0.
From HUMAnN 3.5 onward, if you’re working with MetaPhlAn 4 taxonomic data, you have to be using a very specific version of MetaPhlAn 4 for each HUMAnN version. This is because the HUMAnN versions contain compatibility files to help associate SGBs from the different MetaPhlAn 4 releases with the HUMAnN 3 pangenomes. You would need to refer to my HUMAnN 3.X release notes to see which version of MetaPhlAn 4 (and more specifically it’s markers / SGBs) to pair with which version of HUMAnN. The latest HUMAnN (3.7) works with the Oct22 marker set from MetaPhlAn 4.
Hi Eric,
Thanks for your explanation.
It seems that the max_taxonomic_profile by MetaPhlAn 4.06 does not work for Humann 3.7. Instead, I directly used the taxonomic profile of individual metagenome as the --taxonomic-profile flag for running Humann 3.7 on the corresponding metatranscriptomics data. This method has successfully ran Humann3.7 on mtx data with the provided taxonomic profile by MetaPhlAn 4.06.
Hope this try will help some users to analyze metatranscriptomics data by Humann with taxonomic profile by Metaphlan.
max_taxonomic_profile isn’t widely used, so it’s possible that it hasn’t been updated to work with the newer MetaPhlAn output formats. In any case I’d recommend doing what you did - i.e. using the per-sample MGX for MTX normalization.