# Domain Composition Analysis > Use when you have a set of Biosynthetic Gene Clusters in GenBank format and need to understand the domain architecture of constituent genes, either to detect statistically significant domain co-occurrence patterns within BGCs, to filter redundant BGCs by domain similarity, or to link detected. - Skill: `holobiomicslab/domain-composition-analysis` (Agent Skill) - Install (CLI): `npx skillmds@latest add holobiomicslab/domain-composition-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/domain-composition-analysis/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: CC-BY-4.0 - Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/holobiomicslab/domain-composition-analysis --- # Domain-Composition Analysis ## Summary Analyze the Pfam domain and subPfam composition of genes within Biosynthetic Gene Clusters to identify and characterize domain patterns that distinguish sub-clusters. This skill enables tokenization and statistical profiling of BGC gene content to support sub-cluster detection and functional annotation. ## When to use Apply this skill when you have a set of Biosynthetic Gene Clusters in GenBank format and need to understand the domain architecture of constituent genes, either to detect statistically significant domain co-occurrence patterns within BGCs, to filter redundant BGCs by domain similarity, or to link detected sub-clusters to specific natural product substructures. ## When NOT to use - Input BGCs are already pre-filtered for domain redundancy or have been extensively curated; re-tokenization and re-filtering may introduce unnecessary computational overhead. - Your goal is general BGC annotation or homology search rather than detection of statistically significant intra-cluster domain patterns or natural product sub-cluster linking. - Pfam or subPfam annotations are unavailable or unreliable for the gene set; domain-based tokenization will not produce valid input for PRESTO-STAT. ## Inputs - Biosynthetic Gene Clusters in GenBank format - Pfam domain annotations per gene - subPfam annotations (optional, for increased resolution) ## Outputs - Tokenised BGC representations (gene × domain/subPfam vectors) - Redundancy-filtered tokenised BGCs (via Adjacency Index similarity network) - Detected sub-clusters with constituent genes and domain compositions - Genomic positions and structural annotations of sub-clusters - Structured sub-cluster output (linked to natural product substructures where available) ## How to apply Tokenize each gene in the BGC by representing it as a vector of its Pfam domains, using subPfams to increase resolution where available. Compute the Adjacency Index of domains as a distance metric to quantify similarity between BGC domain compositions. Use this similarity network to filter the tokenised BGC set for redundancy, removing highly similar BGCs. For remaining non-redundant BGCs, apply the PRESTO-STAT statistical algorithm (based on Del Carratore et al. 2019) to detect statistically significant co-occurring domain patterns across the tokenised sequences. Extract and record detected sub-clusters with their constituent genes, full domain compositions, and genomic positions for downstream annotation and validation. ## Related tools - **iPRESTO** (Command-line tool that orchestrates tokenization of BGCs using Pfam domains and subPfams, redundancy filtering via Adjacency Index similarity networks, and application of PRESTO-STAT and PRESTO-TOP statistical methods for sub-cluster detection and linking to natural product substructures.) ## Evaluation signals - Tokenised BGC representations must contain valid Pfam and subPfam domain identifiers for every gene; check that no gene is represented as an empty token vector. - Adjacency Index similarity values between BGC pairs must fall in the range [0, 1]; BGCs with similarity above a specified threshold (e.g., 0.9) should be marked as redundant and one retained per cluster. - PRESTO-STAT sub-clusters must have statistically significant co-occurrence p-values (typically p < 0.05 or adjusted p < 0.01); verify that p-values are computed and reported for each detected sub-cluster. - Detected sub-clusters must have genomic positions that fall within the span of their parent BGC; validate that all gene positions are contiguous or within acceptable gap thresholds. - Sub-clusters linked to natural product substructures should have interpretable domain compositions consistent with known biosynthetic pathways (e.g., polyketide synthase domains for PKS-derived products). ## Limitations - PRESTO-STAT relies on statistical power from sufficient redundancy-filtered BGC samples; small datasets may yield false negatives or unreliable co-occurrence estimates. - Pfam and subPfam annotations are dependent on the completeness and curation of the reference database; novel or divergent domains may be missed or misclassified. - Adjacency Index similarity is a coarse metric for redundancy filtering and may over-cluster functionally distinct BGCs with similar domain inventories but different arrangements. - Sub-cluster detection is sensitive to tokenization granularity (i.e., choice of subPfams); different resolutions may yield overlapping or conflicting sub-cluster calls. - Linking to natural product substructures requires a separate trained model or knowledge base; this skill produces sub-clusters but does not guarantee valid structure prediction. ## Evidence - [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" - [intro] Tokenised BGCs are filtered for redundancy using similarity network with Adjacency Index of domains as distance metric: "Tokenised BGCs are filtered for redundancy using similarity network with an Adjacency Index of domains as a distance metric" - [intro] PRESTO-STAT is based on statistical algorithm from Del Carratore et al. 2019 for detecting gene sub-clusters: "PRESTO-STAT, which is based on the statistical algorithm from Del Carratore et al. (2019)" - [other] Sub-clusters are extracted with constituent genes, domain compositions, and genomic positions: "Extract and record detected sub-clusters with their constituent genes, domain compositions, and genomic positions" - [intro] Sub-clusters found with iPRESTO can be linked to Natural Product substructures: "The sub-clusters found with iPRESTO can then be linked to Natural Product substructures"