Hello,
My colleagues and I would like to use MelonnPan to analyze our 16S rRNA data, but we are unclear as to how to process our data in order to get the type of input file that is shown in the MelonnPan tutorial.
From browsing other posts in this forum, it seems as though you might need to run Picrust2 before using MelonnPan, is this correct? We are already working on running Picrust2 on our samples.
Thanks in advance,
Carli Jones
Hi @carli-jones - this topic has been discussed here: MelonnPan Input - Picrust2 output.
In short, currently, it is not possible to use Picrust/Picrust2 KO output with the default MelonnPan-Predict module. However, the users can still use the MelonnPan-Train module to train a new model with KOs derived from Picrust/Picrust2.
Hi, thanks for your reply!
We do not necessarily want to use the outputs from Picrust2 - we just don’t understand what the proper input for MelonnPan is.
If we are starting with 16S rRNA sequencing data, what data would go into MelonnPan?
Thanks,
Carli
Hi @carli-jones - MelonnPan has two workflows: MelonnPan-Train and MelonnPan-Predict.
MelonnPan-Predict by default uses a pre-trained model that was internally and externally validated in UniRef90 profiles as described in the manuscript. In order to use this default model, the users need to provide functionally profiled metagenomes in the form of UniRef90’s (as described in the tutorial). Currently, we do not support non-UniRef90-based input for the default workflow.
On the other hand, the MelonnPan-Train workflow allows users to re-train a new model from paired metabolite-microbiome profiles, and the input features can be any microbial sequence features which can be passed to the MelonnPan-Predict workflow to generate new predictions.
In summary, if you currently do not have paired metabolites with the 16S data, there is not much we can do. But if you do, then you can re-train a new model (different from the default one) and use that model to predict new samples.
Hope that makes sense!