Hi,
I have a question. I obtained the tratified output (i.e. _contrib.tsv) for EC, KO and path_abundance tables. Since I have very different microbiomes (gut, oral, milk and vaginal) at different timepoints, I want to explore the data as “classes of pathways” and get a sort of bird eye view, rather than analysing one single pathway at a time.
In stratified output there are columns function and taxon. The taxon is feature ID of ASV which I believe I can link to taxonomic nomenclature using the original .biom table that I provided as input (which I created via QIIME2)
Specifically I am interested in the column function of the pathway abundance file (path_abun_contrib.tsv). As I understand, the ID/acronyms are MetaCyc database ID. I would like to do two things:
In this way I believe I can merge pathways (row entry in path_abun_contrib.tsv file) to their common “Superclass” and analyse the relative contributions of organisms to that “Superclass of pathways”.
It is relatively simple if I had a “table” to link the acronyms to the pathway name or superclass. However, I never worked with MetaCyc before, and I wonder if there is a “table” or (more likely) a way to do that accessing the MetaCyc database.
I thank you in advance
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Dear Gavin,
Thank you. Yes, for the point 1 in my previous post, the table you provide is what I was looking for. But is there something similar for point 2? Basically a table that matches the pathway ID to “Superclasses” hierarchical order in which they are organized in MetaCyc.
The information is visible and accessible on a click-based format in the MetaCyc webpage, but I do not know how to retrieve, if there is already a way to download that information from somewhere. Here as example (https://metacyc.org/META/NEW-IMAGE?type=PATHWAY&object=PWY-5143) I click on “Fatty acids and Lipid Biosynthesis” (https://metacyc.org/META/NEW-IMAGE?type=ECOCYC-CLASS&object=Lipid-Biosynthesis).
The hierarchical order, I think, would help to isolate pathways as higher order groups. In my case I plan to combine my data with metabolomics as well. Then it would be much easier to select and compare, for example, “Fatty acids and Lipids metabolism” related data for the two omic datasets.
Best
Matteo
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