Functional Prediction - PICRUSt (Greengenes) - Tax4Fun (SILVA) using the QIIME outputs

Hi everyone,

Can you advise on how to obtain the data from QIIME outputs in order to run the Functional Prediction - PICRUSt (Greengenes) - Tax4Fun (SILVA)?
I am trying to run using the Microbiome Analyst, but they cannot recognize the QIIME outputs. From Visual Exploration to Comparison and Classification I would read everything, but not the Functional Prediction.

Hello,
Generally,if you choose the correct database (Greengenes for PICRUST or SILVA for Tax4Fun) for the taxonomy assignment,MicrobiomeAnalyst can deal with the output.

If this is not the case, please follow our post guideline for specific trouble shooting.

Hope this helps.

Hi,
I chose the Silva output to run the Tax4fun with the QIIME against SILVA database as Annotation Pipeline and this error came on the screen.
“Error Please check the taxonomy levels”

Also, I chose Greengenes OTU ID to run the Picrust. The following screen came out, the graph became empty with the attached output.
functionalprof_picrust(1).csv (692.7 KB)
picrust_ko_0-1.pdf (6.5 KB)

I would greatly appreciate it if you could advise me on how to run the PicRust. The input files are available upon request.
Thanks

Please read and follow our post guide.

Dear All,

Please find attached the screenshot of my procedures. In fact I could make the PicRust and found the log - KO counts (Slide 6). From now, can anyone show me the tutorial on how to access the metabolic functions using this graph? or table?
Also, I tried to run the Tax4fun but I found some error. Can anyone guide me on how to solve this issue?
Regards,
Presentation1.pdf (565.5 KB)

Hello,

You can us our SDP module to further analysis the result KO abundance table from PiCRUST following * Performing shotgun metagenomics data analysis (SDP).

Tax4fun works for the silva-based pipeline. I noticed that you upload the same dataset to Tax4Fun as to PiCRUST but only change the selection for the taxonomy labels. This is not correct. If your data was annotated using GreenGenes, it not fit for Tax4Fun.

Hope this helps!