# Pfam Domain Pattern Recognition > Use when when you have a set of Biosynthetic Gene Clusters (BGCs) in GenBank format and need to identify statistically significant or topic-modeled co-occurrence patterns of Pfam domains and subPfams across multiple genes. - Skill: `holobiomicslab/pfam-domain-pattern-recognition` (Agent Skill) - Install (CLI): `npx skillmds@latest add holobiomicslab/pfam-domain-pattern-recognition` - Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/pfam-domain-pattern-recognition/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - 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/pfam-domain-pattern-recognition --- # Pfam Domain Pattern Recognition ## Summary Recognition and extraction of Pfam domain and subPfam patterns from genes within Biosynthetic Gene Clusters to create tokenized sequence representations. This skill enables systematic identification of co-occurring domain motifs that characterize functional gene sub-clusters. ## When to use When you have a set of Biosynthetic Gene Clusters (BGCs) in GenBank format and need to identify statistically significant or topic-modeled co-occurrence patterns of Pfam domains and subPfams across multiple genes. Apply this skill as the initial tokenization step before redundancy filtering and sub-cluster detection, or when you seek to link detected sub-clusters back to conserved domain architectures. ## When NOT to use - Input BGCs have no Pfam domain annotations or lack functional gene annotation — domain pattern recognition requires curated domain assignments as input. - Goal is to predict biosynthetic product structure directly from gene sequence without intermediate sub-cluster identification — this skill focuses on domain tokenization, not end-to-end product prediction. - BGC dataset is already redundancy-filtered and you only need to apply statistical or topic-based sub-cluster detection — skip to PRESTO-STAT or PRESTO-TOP instead. ## Inputs - Biosynthetic Gene Clusters in GenBank format - Gene annotations with Pfam domain assignments - Pfam domain database with subPfam hierarchy ## Outputs - Tokenized BGC representations (Pfam and subPfam token sequences per gene) - Domain composition profiles per gene - Adjacency matrices or co-occurrence frequency tables of domain patterns ## How to apply First, represent each gene in the BGC as a combination of its annotated Pfam domains, using subPfams to increase resolution and granularity of the token representation. This creates a token string per gene that captures the domain architecture. The rationale is that Pfam domains encode functional modules of biosynthetic enzymes; subPfams further discriminate between functional subtypes (e.g., different condensation domain variants). Once tokenized, these representations become amenable to statistical co-occurrence analysis (PRESTO-STAT) or topic modeling (PRESTO-TOP) to detect patterns that recur across many BGCs. The choice of subPfam resolution affects sensitivity: finer tokenization reveals more specific domain combinations but risks over-segmentation; coarser Pfam-only tokenization is more robust but may mask functional distinction. ## Related tools - **iPRESTO** (Command-line platform that implements Pfam/subPfam tokenization, redundancy filtering, and sub-cluster detection (PRESTO-STAT and PRESTO-TOP) on BGCs) ## Evaluation signals - All genes in input BGCs have been assigned at least one Pfam or subPfam token; no genes are left untokenized. - Token composition reflects known biosynthetic enzyme architectures (e.g., polyketide synthase genes contain expected condensation, ketoreductase, and ACP domain tokens). - Adjacency Index or domain co-occurrence frequency matrix is symmetric and non-negative; no NaN or infinite values present. - When redundancy filtering is subsequently applied, the Adjacency Index correctly identifies highly similar tokenized BGCs as neighbors in the similarity network. - Detected sub-clusters contain genes whose tokens show statistically significant or topic-weighted co-occurrence patterns that are rare or absent in random BGC samples. ## Limitations - Tokenization accuracy depends on completeness and quality of upstream Pfam domain annotation; unannotated genes or missing domains will degrade pattern detection. - SubPfam resolution introduces trade-off: finer granularity increases sensitivity to domain variants but risks false fragmentation and reduces statistical power if individual sub-cluster patterns are rare. - Pfam domains are ortholog-based and may not capture species-specific or novel biosynthetic adaptations that lie outside Pfam HMM definitions. - The Adjacency Index distance metric assumes co-occurrence patterns are uniform across genomic contexts; gene order and synteny are not explicitly modeled in token representation. ## 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] For the detection of sub-clusters two methods are used: PRESTO-STAT, which is based on the statistical algorithm from Del Carratore et al. (2019): "For the detection of sub-clusters two methods are used: PRESTO-STAT, which is based on the statistical algorithm from Del Carratore et al. (2019)" - [intro] Tokenised BGCs are filtered for redundancy using similarity network with an Adjacency Index of domains as a distance metric: "Tokenised BGCs are filtered for redundancy using similarity network with an Adjacency Index of domains as a distance metric" - [intro] iPRESTO (integrated Prediction and Rigorous Exploration of biosynthetic Sub-clusters Tool) is a command line tool for the detection of gene sub-clusters: "iPRESTO (integrated Prediction and Rigorous Exploration of biosynthetic Sub-clusters Tool) is a command line tool for the detection of gene sub-clusters"