subpfam-functional-resolution-augmentation
Summary
This skill augments Pfam domain annotations with subPfam assignments to increase functional resolution when tokenizing individual genes in Biosynthetic Gene Clusters. It enables finer-grained domain-level representation suitable for detecting functionally coherent sub-clusters within larger BGCs.
When to use
Apply this skill when you have annotated genes with Pfam domains but require higher functional specificity to detect natural product sub-clusters. Use it specifically when Pfam-level annotation alone produces over-broad domain tokens that obscure functionally distinct sub-domains, or when linking detected sub-clusters to specific natural product substructures requires domain-level granularity beyond standard Pfam assignments.
When NOT to use
- Input genes have not yet been scanned for Pfam domains; perform standard Pfam annotation first.
- Downstream analysis does not require sub-cluster detection or natural product substructure linking; standard Pfam tokens may be sufficient for coarser-grained clustering tasks.
- Genes belong to non-biosynthetic pathways where Pfam/subPfam domain architecture is not the primary discriminator.
Inputs
- GenBank-format BGC sequence files
- Gene sequences annotated with Pfam domain assignments
- Pfam domain identifiers and boundaries per gene
Outputs
- Tokenized BGC representation (one token string per gene, combining Pfam domains and subPfams)
- Gene-level annotation objects enriched with subPfam metadata
How to apply
After initial Pfam domain scanning of BGC genes, systematically augment each Pfam annotation with its corresponding subPfam assignment to increase resolution. Represent each gene as a token string combining both Pfam domains and their subPfam refinements. This dual-level representation preserves broad functional categories (via Pfams) while capturing fine-grained functional distinctions (via subPfams) needed for subsequent sub-cluster detection methods like PRESTO-STAT or PRESTO-TOP. The rationale is that subPfams partition Pfam domains into functionally distinct subtypes, allowing the similarity network filtering and sub-cluster detection steps to discriminate between genes with related but non-identical biosynthetic roles.
Related tools
- iPRESTO (Command-line tool that executes BGC tokenization using Pfam and subPfam domains as part of its integrated sub-cluster detection pipeline)
- Pfam domain scanner (Upstream tool for initial Pfam domain assignment; provides the base annotations that are subsequently augmented with subPfams)
Evaluation signals
- Each gene in the tokenized output contains both Pfam and subPfam identifiers in its token string; verify no gene is represented by Pfam domains alone.
- Token strings show increased specificity compared to Pfam-only representation; subPfam tokens refine broad Pfam categories into distinct functional subtypes.
- Downstream similarity network filtering produces non-trivial redundancy reduction, indicating that subPfam augmentation enabled meaningful discrimination among BGCs.
- Sub-clusters detected after augmentation show tighter functional coherence and stronger linkage to specific natural product substructures than pre-augmentation results.
- Audit log or token comparison confirms subPfam assignments are non-empty and systematic across all genes; no missing or spurious subPfam entries.
Limitations
- Augmentation depends on the completeness and accuracy of the underlying Pfam domain database; genes lacking Pfam annotation cannot be augmented.
- SubPfam assignments may not exist for all Pfam domains; augmentation coverage may be partial, requiring fallback to Pfam-only representation for unaugmented domains.
- Increased dimensionality from dual-level annotation may inflate computational cost in large-scale BGC screening; trade-off between resolution and scalability should be considered.
- The quality of sub-cluster detection and natural product linkage depends critically on downstream filtering and statistical methods (PRESTO-STAT, PRESTO-TOP); augmentation alone does not guarantee valid or interpretable results.
Evidence
- [other] Each gene in a BGC is tokenised by representing it as a combination of its Pfam domains, with subPfams employed to increase resolution of the domain annotation.: "Each gene in a BGC is tokenised by representing it as a combination of its Pfam domains, with subPfams employed to increase resolution of the domain annotation."
- [intro] BGCs are tokenised by representing each gene as a combination of its Pfam domains, where subPfams are used to increase resolution: "BGCs are tokenised by representing each gene as a combination of its Pfam domains, where subPfams are used to increase resolution"
- [other] Represent each gene as a token string combining its Pfam domains and subPfams.: "Represent each gene as a token string combining its Pfam domains and subPfams."
- [intro] The sub-clusters found with iPRESTO can then be linked to Natural Product substructures: "The sub-clusters found with iPRESTO can then be linked to Natural Product substructures"
- [intro] Tokenise BGCs using Pfam domains and subPfams: "iPRESTO detects gene sub-clusters in Biosynthetic Gene Clusters by tokenizing BGCs using Pfam domains and subPfams"