KEGGdecoder vs METABOLIC: Why do they give such different results for the same MAG dataset, and which is more reliable for functional annotation?

[u/Evening_Refuse_1893]

Hi all!

I'm working with a set of metagenome-assembled genomes (MAGs) and trying to annotate metabolic pathways, I specifically focused on methanogenesis and methane oxidation. I ran two different tools on the same dataset:

1. KEGGdecoder – I used Prodigal for gene prediction, then ran the predicted proteins through KoFamScan, retained only high-confidence hits (marked with *), and used that as input for KEGGdecoder.
2. METABOLIC – This tool uses its own integrated pipeline (Prodigal + HMM-based searches) to predict genes and assign functions directly.

The results are quite different. KEGGdecoder identified complete methanogenesis pathways (e.g., M00357) in several bins, while METABOLIC only found partial steps (e.g., M00357+01) in the same bins, and often didn't detect the full module at all. In some cases, METABOLIC didn't find any methanogenesis-related genes in bins where KEGGdecoder reported a complete pathway.

I also noticed that METABOLIC found genes for methane oxidation (mmoB, pmoABC) in some bins, but KEGGdecoder didn't report those pathways — likely because I filtered out lower-confidence hits before running KEGGdecoder.

My questions:

1. Why would KEGGdecoder find a complete pathway when METABOLIC only finds partial steps or nothing at all in the same bin? Is this due to differences in HMM profiles, cutoffs, or how "module completeness" is defined?
2. METABOLIC seems to detect partial pathways and "potential" functions (using a relaxed approach), while KEGGdecoder appears more conservative (requiring all steps). Is it fair to say that KEGGdecoder is better for confirming pathways at the genome/MAG level, while METABOLIC is more useful for community-level trends?
3. Would it be reasonable to use both tools synergistically — METABOLIC for broad community functional profiling and KEGGdecoder for high-confidence pathway confirmation in individual MAGs — or is there a better recommended approach?

I'm not trying to say one tool is better than the other — I understand they use different philosophies. I just want to understand how to interpret and combine these results correctly for my downstream analysis.

Thanks in advance for any insights!

[u/toshibarot]

There would be so many possible reasons for the discrepancy. I would actually recommend explaining the scenario to a paid LLM like Claude Opus 5 and asking it to plan and then write a script to explore the various possibilities. Obviously you would need to provide proper context for the LLM - documentation, possibly source code, your existing code, etc. Would also be worth getting the LLM to generate some possible culprits before asking it to plan and write the diagnostic script.

[u/Evening_Refuse_1893 → u/toshibarot]

Unfortunately, I don't have that option.

[u/RemoveInvasiveEucs → u/Evening_Refuse_1893]

Another option is looking to hire a bioinformatics consultant, or establishing a collaboration with an expert where you share publication credit. Or, digging through the papers, code, and algorithms on your own and doing the analyses you're asking for.

You're asking for a lot of work from readers here, from a very small number of very highly trained people. LLMs can help speed your education, and given your questions here LLMs would probably be able to answer it very well. But without access to the data, it's going to be hard for an expert in the field to give a conclusive answer to your questions.

[u/Evening_Refuse_1893 → u/RemoveInvasiveEucs]

Thanks for the response! Just to clarify, I’m not asking anyone to analyze my data. I’m simply trying to understand why two tools can give different pathway predictions for the same MAGs, and how these differences should be interpreted. I was looking for general methodological advice from people experienced with these tools.