BGCPhyloSignal

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Phylogenomic framework for analysing biosynthetic gene cluster evolution across fungal lineages.

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- config.yaml
- - bgc_presence_absence.tsv
  - metadata.tsv
  - species_tree.nwk
- github_zenodo_guide.md
- bgcphylosignal.yaml
- bgc_phylo_heatmap.pdf
- bgc_phylo_heatmap.png
- metadata_enrichment.tsv
- phylo_signal_results.tsv
- - plot_phylo_heatmap.py
  - run_phylo_signal.py
  - summarize_metadata_enrichment.py
- Snakefile
CITATION.cff
LICENSE
README.md

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**Phylogenetic signal and evolutionary pattern screening for biosynthetic gene clusters (BGCs).**

BGCPhyloSignal is a small, reproducible workflow to test whether biosynthetic gene clusters, gene cluster families (GCFs), or metabolite-associated BGC traits follow the species phylogeny or show dynamic gain/loss patterns.

It is designed for lichen-forming fungi, but it can be used with any fungi or microbes where you have:

1. a species tree in Newick format;
2. a BGC/GCF presence-absence matrix;
3. optional taxonomic/ecological metadata.

This repository is conceptually inspired by comparative genome-mining studies of lichen biosynthetic diversity, but it is **not a reproduction of any single article**. Its goal is to provide a reusable method for prioritizing BGCs from an evolutionary perspective.

---
## Scientific Background

BGCPhyloSignal was developed from research on the evolution of secondary metabolism in lichen-forming fungi and ongoing efforts to understand the phylogenetic distribution of biosynthetic gene clusters (BGCs).

The workflow is conceptually inspired by comparative genome-mining studies, including:

> Pizarro D. et al. (2020). *Genome-wide analysis of biosynthetic gene clusters reveals correlated gene loss with absence of usnic acid in lichen-forming fungi*. Genome Biology and Evolution 12:1858–1868.

It is also being explored in ongoing research focused on linking biosynthetic potential, phylogeny, ecology and metabolite diversity in lichen symbioses.

This repository is not a reproduction of any single publication but provides a reusable framework for evolutionary screening of BGCs.

---
## What does it do?

For each BGC or GCF, the workflow estimates:

- prevalence across taxa;
- phylogenetic clustering of presence states;
- Moran's I on the species tree;
- permutation-based significance;
- parsimony-based minimum number of gain/loss transitions;
- taxonomic enrichment if metadata are provided;
- a combined evolutionary priority score.

The main question is:

> Is this BGC conserved in a phylogenetic lineage, scattered across unrelated taxa, or taxonomically restricted and potentially interesting?

---

## Repository structure

```text
BGCPhyloSignal/
├── README.md
├── LICENSE
├── CITATION.cff
├── config/
│   └── config.yaml
├── data/
│   └── example/
│       ├── species_tree.nwk
│       ├── bgc_presence_absence.tsv
│       └── metadata.tsv
├── envs/
│   └── bgcphylosignal.yaml
├── workflow/
│   ├── Snakefile
│   └── scripts/
│       ├── run_phylo_signal.py
│       ├── plot_phylo_heatmap.py
│       └── summarize_metadata_enrichment.py
├── results/
│   └── .gitkeep
└── docs/
    └── github_zenodo_guide.md
```

---

## Quick start with the example dataset

### 1. Create the conda environment

```bash
conda env create -f envs/bgcphylosignal.yaml
conda activate bgcphylosignal
```

### 2. Run the complete workflow

```bash
snakemake -s workflow/Snakefile --cores 1
```

### 3. Main outputs

```text
results/phylo_signal_results.tsv
results/metadata_enrichment.tsv
results/bgc_phylo_heatmap.pdf
results/bgc_phylo_heatmap.png
```

---

## Input files

### 1. Species tree

File: `data/example/species_tree.nwk`

A rooted or unrooted Newick tree. Tip labels must match the taxon names in the presence-absence matrix.

Example:

```text
((Evernia_prunastri:0.1,Pseudevernia_furfuracea:0.1):0.2,Usnea_sp:0.3);
```

### 2. Presence-absence matrix

File: `data/example/bgc_presence_absence.tsv`

Rows are taxa. Columns are BGCs, GCFs, metabolites, or any binary genomic trait.

```text
taxon	GCF_001	GCF_002	GCF_003
Evernia_prunastri	1	0	1
Pseudevernia_furfuracea	1	0	0
Usnea_sp	0	1	1
```

### 3. Metadata table

File: `data/example/metadata.tsv`

Optional but recommended.

```text
taxon	family	growth_form	substrate
Evernia_prunastri	Parmeliaceae	fruticose	bark
Pseudevernia_furfuracea	Parmeliaceae	fruticose	bark
Usnea_sp	Parmeliaceae	fruticose	bark
```

---

## Interpretation of output metrics

### `prevalence`

Fraction of taxa containing the BGC/GCF.

### `moran_i`

Positive values indicate that related taxa tend to share the same presence/absence state.

### `moran_p_perm`

Permutation p-value for Moran's I.

### `mean_present_distance_ratio`

Mean phylogenetic distance among taxa containing the BGC divided by the overall mean distance among all taxa.

- `< 1`: BGC is phylogenetically clustered.
- `~ 1`: BGC is broadly scattered.
- `> 1`: BGC occurs in distantly related taxa.

### `parsimony_steps`

Minimum number of changes needed to explain the presence/absence pattern on the tree.

Low values suggest clade conservation. High values suggest repeated gain/loss or noisy distribution.

### `priority_score`

A combined heuristic score prioritizing traits that are:

- rare or moderately rare;
- phylogenetically structured;
- taxonomically enriched;
- not trivially present in all taxa.

This is not a biological proof, but a screening metric for selecting candidate BGCs for deeper analysis.

---

## Suggested use cases

- Prioritize BGCs after antiSMASH + BiG-SCAPE.
- Detect lineage-specific biosynthetic innovations.
- Compare PKS, NRPS, terpene and RiPP GCF distributions.
- Link BGC distribution to ecology or chemistry.
- Select candidates for manual inspection, synteny analysis or metabolomics.

---

## Citation

If you use this repository, please cite it using Zenodo DOI: Pizarro, D. (2026). BGCPhyloSignal: phylogenetic signal screening for biosynthetic gene clusters (version_1.0). Zenodo. https://doi.org/10.5281/zenodo.20700585

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