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Phytohormones in Fungi: Inter-Kingdom Modulators or Fungal Self-Controlling Elements?

This repository describes a reproducible bioinformatics workflow designed to identify, curate, and analyze homologs of phytohormone-associated genes in filamentous fungi. The strategy integrates sequence similarity searches, phylogenetic reconstruction, and structural modeling to support evolutionary and functional inference.

License: MIT Bioinformatics Phylogenetics

Table of Contents

Overview

The pipeline was conceived to:

  • Select reliable seed sequences based on curated functional evidence
  • Identify homologs using BLASTp searches against custom fungal databases
  • Filter and curate BLAST results to select best hits per organism
  • Extract sequences systematically from multifasta databases
  • Generate high-quality multiple sequence alignments
  • Infer robust phylogenetic relationships
  • Visualize phylogenies with integrated taxonomic metadata
  • Support structural interpretation through AlphaFold models

The workflow is platform-agnostic and can be adapted to any gene family of interest.

Installation

Conda Environment

We provide a Conda environment file with all required dependencies for the phylogenetic analysis pipeline.

💡 Tip: If you prefer not to manage dependencies, use our Docker image which has everything pre-installed and tested.

Option 1: Create environment from YAML file

# Clone the repository
git clone https://github.com/yourusername/phytohormone-phylogenomics.git
cd phytohormone-phylogenomics

# Create the conda environment
conda env create -f environment.yml

# Activate the environment
conda activate hormone-phylo

Option 2: Manual environment creation

Create the environment with the exact versions we tested:

# Create a new conda environment
conda create -n hormone-phylo python=3.12

# Activate the environment
conda activate hormone-phylo

# Install bioinformatics tools with tested versions
conda install -c bioconda -c conda-forge \
  blast=2.17.0 \
  mafft=7.526 \
  fasttree=2.1.11 \
  trimal=1.5.0 \
  iqtree=3.0.1 \
  seqkit=2.10.1 \
  cd-hit=4.8.1 \
  hmmer=3.4 \
  prank \
  biopython \
  numpy

Verify installation:

# Check versions of key tools
blastp -version          # Should show BLAST 2.17.0+
mafft --version          # Should show v7.526
iqtree3 --version        # Should show IQ-TREE multicore version 3.0.1
trimal --version         # Should show trimAl v1.5.0

Docker Image

We provide a Docker image with all dependencies pre-installed for maximum reproducibility and portability across different systems. The image contains all software versions tested and validated for this pipeline.

Pull the Docker image:

docker pull davidalbertoge/hormone-analysis:latest

Run the container:

# Run interactively with current directory mounted
docker run -v $(pwd):/home/ -it davidalbertoge/hormone-analysis:latest

What's included in the Docker image:

  • All bioinformatics tools with tested versions
  • Pre-configured environment ready to run

Build from Dockerfile (optional):

# Build the image locally from provided Dockerfile
docker build -t hormone-analysis:local .

# Run your local build
docker run -v $(pwd):/home/ -it hormone-analysis:local

Data Requirements

Input Data

1. Proteome Databases

The pipeline requires protein sequence databases from target organisms. We recommend the following sources:

Database Source Format URL
UniProt Reviewed proteins (Swiss-Prot) FASTA https://www.uniprot.org/downloads
NCBI RefSeq Reference protein sequences FASTA https://www.ncbi.nlm.nih.gov/refseq/
JGI MycoCosm Fungal genomes FASTA https://mycocosm.jgi.doe.gov/
FungiDB Fungal genomics resource FASTA https://fungidb.org/

2. Seed Sequences

Experimentally characterized protein sequences used as BLAST queries:

  • Should be in FASTA format
  • One file per gene family or multiple families in separate files
  • Headers should contain gene names and organism information

Example seed sequence format:

>sp|P12345|GENE_ORGANISM Gene description OS=Organism name OX=12345 GN=geneName
MAKTPVQIWSFLKDHGFSDKHGFKJHGFKJDHGFKJDHGF...

3. Taxonomic Metadata

For phylogenetic tree visualization, prepare a metadata file with:

Column Description Example
TaxID NCBI Taxonomy ID 5476
Organism Scientific name Candida albicans
Kingdom Taxonomic kingdom Fungi
Phylum Taxonomic phylum Ascomycota
EarlyDivergent Basal lineage flag TRUE/FALSE

Metadata file format (TSV):

TaxID	Organism	Kingdom	Phylum	EarlyDivergent
5476	Candida albicans	Fungi	Ascomycota	FALSE
367110	Neurospora crassa	Fungi	Ascomycota	FALSE
284811	Aspergillus fumigatus	Fungi	Ascomycota	FALSE

AlphaFold Requirements

If using AlphaFold for structural modeling:

AlphaFold web server: https://alphafold.ebi.ac.uk/

ChimeraX Requirements

For structural visualization:

Pipeline Steps

1. Construction of a Custom Protein Database

Proteomes from selected organisms were integrated into a non-redundant multifasta database representing the taxonomic diversity relevant to the study. This database constituted the search space for downstream homology detection.

Database construction:

# Concatenate individual proteome files into a single multifasta
cat organism1.fasta organism2.fasta organism3.fasta > fungal_proteomes.fasta

# Create BLAST database
makeblastdb -in fungal_proteomes.fasta -dbtype prot -out fungal_db

Key considerations:

  • Headers should follow a consistent format (e.g., TaxID|source|UniProtID|...)
  • Document the taxonomic composition of the database

2. Selection of Seed Sequences

Initial queries were chosen based on:

  • Sequence homology to experimentally characterized proteins
  • Presence of expected functional features described in the literature
  • Structural conservation of key catalytic or regulatory domains

This step ensured that only biologically meaningful references were used to initiate similarity searches, minimizing propagation of misannotated sequences.

Criteria for seed selection:

  • Proteins with experimental validation (in literature)
  • Complete sequences with well-defined domain architecture
  • Representatives from diverse taxonomic groups when applicable

3. Homology Detection via BLASTp

Candidate homologs were identified using BLASTp against the custom database with parameters optimized for divergent fungal proteins.

BLASTp execution:

blastp \
  -query seeds.fasta \
  -db fungal_db \
  -out blastp_results.tsv \
  -evalue 0.00001 \
  -word_size 3 \
  -matrix BLOSUM45 \
  -seg yes \
  -outfmt "6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qcovs qlen slen"

Parameter rationale:

  • E-value threshold (0.00001): Stringent cutoff for statistical significance
  • Word size (3): Increased sensitivity for detecting divergent homologs
  • Matrix (BLOSUM45): Optimized for evolutionarily distant sequences
  • SEG filter (yes): Masks low-complexity regions to reduce spurious hits

Output format:

  • Extended tabular format (outfmt 6) with 15 columns
  • Includes query coverage (qcovs) and sequence lengths (qlen, slen)

4. Filtering and Curation of BLAST Results

Raw BLAST results were systematically filtered to retain only high-quality hits and eliminate redundancy. The filtering process addressed a critical challenge: selecting the best hit per organism when multiple paralogs or isoforms were detected.

Filtering strategy:

# Filtering criteria applied:
# 1. Minimum identity: ≥30%
# 2. Minimum query coverage: ≥50%
# 3. Organism extraction from subject IDs
# 4. Selection of best hit per Query-Organism pair

# Prioritization for best hit selection:
# 1. Bit score (highest) - alignment quality
# 2. Query coverage (highest) - % of query aligned
# 3. Percent identity (highest) - sequence similarity
# 4. E-value (lowest) - statistical significance

Key innovation:

  • Organism IDs were extracted from subject sequence headers (TaxID or species code)
  • Multiple hits from the same organism were consolidated to a single best representative
  • This approach ensures one sequence per organism per query, facilitating downstream phylogenetic analysis

Quality control metrics:

  • Distribution of identity percentages
  • Query coverage statistics
  • E-value ranges
  • Number of organisms per gene family

Outputs:

  • Consolidated table with best hits per Query-Organism combination
  • Individual tables for each query gene
  • Statistical summary of filtering results
  • Visualization of quality metrics (identity vs. coverage)

5. Sequence Retrieval

Selected hits were systematically retrieved from the multifasta database based on their sequence IDs.

Extraction process:

# Extract subject IDs from filtered BLAST results
awk '{print $2}' filtered_results.tsv > sequence_ids.txt

# Remove duplicate IDs
sort -u sequence_ids.txt > unique_ids.txt

# Extract sequences from database
# Using BioPython or similar tools
seqkit grep -f unique_ids.txt fungal_proteomes.fasta > extracted_sequences.fasta

Quality checks:

  • Verify all IDs were successfully retrieved
  • Check for truncated or incomplete sequences
  • Confirm sequence lengths match expected protein sizes
  • Remove sequences with extensive gaps or ambiguous residues

6. Multiple Sequence Alignment

High-quality MSAs were generated for each protein family using progressive alignment strategies.

Initial alignment:

mafft --auto --reorder curated_sequences.fasta > alignment.fasta

MAFFT parameters:

  • --auto: Automatically selects alignment strategy based on dataset size
  • --reorder: Orders sequences by similarity for improved visualization

Alignment considerations:

  • Conservation of catalytic and regulatory motifs
  • Proper alignment of domain boundaries
  • Detection of divergent or potentially mispredicted regions
  • Identification of regions requiring manual curation

7. Phylogeny-Aware Alignment Refinement

To improve alignment quality, a guide tree was used to inform positional homology inference.

Guide tree generation:

FastTree -lg -gamma -nosupport alignment.fasta > guide.nwk

Refinement with PRANK:

prank -d=alignment.fasta \
      -t=guide.nwk \
      -protein +F -termgap -iterate=3 \
      -showtree -showanc -uselogs -shortnames

PRANK advantages:

  • Distinguishes insertions from deletions based on phylogeny
  • Preserves insertion events as positional homologs
  • Iteratively refines alignment (3 iterations recommended)

Trimming poorly aligned regions:

trimal -in prank.best.fas \
       -out alignment_trimmed.fasta \
       -automated1 \
       -htmlout report.html

trimAl parameters:

  • -automated1: Heuristic method balancing alignment quality and information retention
  • HTML report provides visual assessment of trimmed positions

8. Phylogenetic Inference

Gene trees were reconstructed using maximum-likelihood with extended model exploration and statistical support estimation.

IQ-TREE execution:

iqtree3 --prefix analysis \
        -s alignment_trimmed.fasta \
        -m MFP \
        -madd LG+C60,LG+F+C60 \
        -B 1000 \
        -alrt 1000 \
        -bnni \
        -T AUTO \
        --symtest

IQ-TREE parameters:

  • -m MFP: ModelFinder Plus for automatic model selection
  • -madd LG+C60,LG+F+C60: Include profile mixture models for heterogeneous data
  • -B 1000: Ultrafast bootstrap with 1000 replicates
  • -alrt 1000: SH-aLRT test for additional support assessment
  • -bnni: Optimize bootstrap trees with NNI
  • --symtest: Test for symmetry in substitution patterns

Statistical support:

  • UFBoot ≥95%: Strong support
  • SH-aLRT ≥80%: Additional confidence measure
  • Both metrics reported at internal nodes

Tree interpretation: The resulting topologies were used to:

  • Distinguish orthologous clades
  • Detect lineage-specific expansions or losses
  • Propose functional diversification events
  • Identify potential horizontal gene transfer candidates

9. Phylogenetic Tree Visualization

Phylogenetic trees were visualized with integrated taxonomic metadata to enhance biological interpretation.

Visualization approach:

# Tree visualization with ggtree in R
# Features:
# - Cladogram or phylogram layouts
# - Bootstrap support values displayed at nodes
# - Branch lengths optionally shown
# - Tips colored by taxonomic Kingdom
# - Tips shaped by Phylum
# - Highlighting of specific clades

# Metadata integration:
# - TaxID-based matching with tip labels
# - Multiple matching strategies for robustness
# - Color schemes optimized for clarity
# - Phylum abbreviations for space efficiency

Key visualization elements:

  1. Layout options:

    • Cladogram (topology-focused)
    • Phylogram (branch length-based)

  2. Statistical support:

    • UFBoot/SH-aLRT values at internal nodes
    • Configurable size and positioning
    • Color-coded thresholds (optional)

  3. Taxonomic metadata:

    • Kingdom: Color-coded labels (e.g., Fungi, Viridiplantae, Metazoa)
    • Phylum: Shape-coded tip points
    • Early divergence: Special notation for basal lineages

  4. Clade highlighting:

    • Visual emphasis on taxonomic groups of interest
    • Configurable transparency and colors

10. Structural Modeling

Representative sequences from major clades were modeled with AlphaFold to:

  • Evaluate conservation of active sites
  • Compare structural features among paralogs
  • Support functional hypotheses derived from phylogeny
  • Identify potential cryptic paralogs based on structural divergence

Structural analysis in ChimeraX:

  • Conservation of catalytic triads or active site residues
  • Domain organization and relative positioning
  • Structural basis for functional diversification

Example Usage

This section provides a complete walkthrough of the pipeline using example data.

Quick Start

# 1. Activate the conda environment
conda activate hormone-phylo

# 2. Navigate to your project directory
cd phytohormone-phylogenomics

# 3. Create directory structure
mkdir -p data/{proteomes,seeds,blast,filtered,sequences,alignments,trees}

# 4. Build BLAST database
makeblastdb -in data/proteomes/fungal_db.fasta -dbtype prot -out data/proteomes/fungal_db

# 5. Run BLAST search
blastp \
  -query data/seeds/IPT_seeds.fasta \
  -db data/proteomes/fungal_db \
  -out data/blast/IPT_results.tsv \
  -evalue 0.00001 \
  -word_size 3 \
  -matrix BLOSUM45 \
  -seg yes \
  -outfmt "6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qcovs qlen slen"

# 6. Filter BLAST results (using R)
Rscript scripts/filter_blast_results.R \
  --input data/blast/IPT_results.tsv \
  --output data/filtered/IPT_filtered.tsv \
  --min-identity 30 \
  --min-coverage 50

# 7. Extract sequences
seqkit grep -f data/filtered/IPT_ids.txt \
  data/proteomes/fungal_db.fasta \
  > data/sequences/IPT_sequences.fasta

# 8. Multiple sequence alignment
mafft --auto --reorder \
  data/sequences/IPT_sequences.fasta \
  > data/alignments/IPT_mafft.fasta

# 9. Generate guide tree
FastTree -lg -gamma -nosupport \
  data/alignments/IPT_mafft.fasta \
  > data/alignments/IPT_guide.nwk

# 10. Refine alignment with PRANK
prank -d=data/alignments/IPT_mafft.fasta \
      -t=data/alignments/IPT_guide.nwk \
      -o=data/alignments/IPT_prank \
      -protein +F -termgap -iterate=3

# 11. Trim alignment
trimal -in data/alignments/IPT_prank.best.fas \
       -out data/alignments/IPT_trimmed.fasta \
       -automated1

# 12. Phylogenetic inference
iqtree3 --prefix data/trees/IPT \
        -s data/alignments/IPT_trimmed.fasta \
        -m MFP \
        -madd LG+C60,LG+F+C60 \
        -B 1000 \
        -alrt 1000 \
        -bnni \
        -T AUTO

# 13. Visualize tree (using R)
Rscript scripts/visualize_tree.R \
  --tree data/trees/IPT.treefile \
  --metadata data/metadata.tsv \
  --output figures/IPT_tree.svg

Example Output Structure

results/
├── IPT/
│   ├── 01_blast/
│   │   └── IPT_results.tsv
│   ├── 02_filtered/
│   │   ├── IPT_filtered.tsv
│   │   └── IPT_stats.txt
│   ├── 03_sequences/
│   │   └── IPT_sequences.fasta
│   ├── 04_alignments/
│   │   ├── IPT_mafft.fasta
│   │   ├── IPT_prank.best.fas
│   │   └── IPT_trimmed.fasta
│   ├── 05_trees/
│   │   ├── IPT.treefile
│   │   ├── IPT.log
│   │   └── IPT.iqtree
│   └── 06_figures/
│       ├── IPT_tree.svg
│       ├── IPT_tree.png
│       └── IPT_legend.svg
└── CKX/
    └── ... (similar structure)

Directory Structure

Recommended project organization for reproducibility:

phytohormone-phylogenomics/
│
├── README.md
├── LICENSE
├── environment.yml          # Conda environment specification
├── Dockerfile               # Docker container specification
│
├── data/                    # Input data (not tracked in git)
│   ├── proteomes/           # Protein sequence databases
│   │   ├── organism1.fasta
│   │   ├── organism2.fasta
│   │   └── fungal_db.fasta  # Combined database
│   ├── seeds/               # Query sequences
│   │   ├── IPT_seeds.fasta
│   │   ├── CKX_seeds.fasta
│   │   └── ...
│   └── metadata.tsv         # Taxonomic metadata
│
├── scripts/                 # Analysis scripts
│   ├── filter_blast_results.R
│   ├── visualize_tree.R
│   ├── run_pipeline.sh
│   └── fetch_taxonomy.py
│
├── results/                 # Analysis outputs (not tracked in git)
│   ├── IPT/
│   ├── CKX/
│   └── ...
│
├── figures/                 # Publication-ready figures
│   ├── IPT_tree.svg
│   ├── IPT_tree.png
│   └── ...
│
└── docs/                    # Additional documentation
    ├── methods.md
    └── supplementary.md

Git configuration (.gitignore):

# Large data files
data/proteomes/
results/
*.fasta
*.tsv
*.nwk
*.treefile

# Temporary files
*.log
*.tmp
*.bak

# R files
.Rhistory
.RData
.Rproj.user/

# Python cache
__pycache__/
*.pyc

Software Requirements

Core Tools

The following table lists the tested and validated versions included in our Docker image and Conda environment:

Tool Version Purpose Installation
BLAST+ 2.17.0 Homology searches conda install -c bioconda blast=2.17.0
MAFFT 7.526 Multiple sequence alignment conda install -c bioconda mafft=7.526
FastTree 2.1.11 Guide tree generation conda install -c bioconda fasttree
PRANK 170427 Phylogeny-aware alignment refinement conda install -c bioconda prank
trimAl 1.5.0 Alignment trimming conda install -c bioconda trimal=1.5.0
IQ-TREE 3.0.1 Phylogenetic inference conda install -c bioconda iqtree=3.0.1
CD-HIT 4.8.1 Sequence clustering conda install -c bioconda cd-hit
SeqKit 2.10.1 Sequence manipulation conda install -c bioconda seqkit=2.10.1
HMMER 3.4 Profile HMM searches (optional) conda install -c bioconda hmmer=3.4

Optional tools:

Tool Version Purpose
AlphaFold ≥2.3.0 Protein structure prediction
ChimeraX ≥1.5 Structural visualization

R Packages

Recommended R version: ≥4.0.0

Data manipulation and filtering:

  • tidyverse (≥1.3.0): Data wrangling and visualization
  • dplyr (≥1.0.0): Data frame manipulation
  • stringr (≥1.4.0): String operations

Phylogenetic visualization:

  • ggtree (≥3.0.0): Tree visualization (Bioconductor)
  • treeio (≥1.16.0): Tree I/O operations (Bioconductor)
  • ape (≥5.5): Phylogenetic analysis
  • phangorn (≥2.8.0): Additional phylogenetic tools

Graphics and export:

  • ggplot2 (≥3.3.0): Grammar of graphics
  • svglite (≥2.1.0): SVG export
  • ggnewscale (≥0.4.0): Multiple scales in ggplot2
  • here (≥1.0.0): Project-relative paths

Installation:

# CRAN packages
install.packages(c("tidyverse", "svglite", "here", "ape", "phangorn"))

# Bioconductor packages
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c("ggtree", "treeio"))

System Requirements

Recommended:

  • RAM: 16 GB (32 GB for large datasets)
  • Disk space: 100 GB (databases can be large)
  • CPU: 8+ cores (for parallel processing)
  • SSD storage (significantly improves I/O performance)

Reproducibility Considerations

The workflow relies exclusively on widely adopted tools in computational biology. We have tested and validated specific versions to ensure reproducibility across different systems.

Version Control and Compatibility

  • Tested versions: All tools have been validated with the versions listed in Software Requirements
  • Docker container: Pre-configured environment available at davidalbertoge/hormone-analysis:latest
  • Conda environment: Reproducible environment specification provided in environment.yml
  • Parameter transparency: All critical parameters explicitly stated in each pipeline step
  • Platform independence: Runs on Linux, macOS, and Windows (with WSL2)

Ensuring Reproducibility

  1. Use version-controlled software:

    # Recommended: Use our Docker container
docker pull davidalbertoge/hormone-analysis:latest

# Or create Conda environment with pinned versions
conda env create -f environment.yml

  2. Document your analysis:

    • Record exact command lines used
    • Note any parameter changes from defaults
    • Save log files from each step
    • Version control your scripts with Git

  3. Random seed handling:

    • Bootstrap analyses use random sampling
    • IQ-TREE uses -seed parameter for reproducibility
    • FastTree results may vary slightly between runs
    • Document random seeds when applicable

  4. Hardware considerations:

    • Number of CPU threads can affect results (especially with stochastic methods)
    • Use -T parameter to control threads in IQ-TREE
    • Memory allocation can impact performance but not results

Applications

This strategy is suitable for:

  • Evolutionary reconstruction of phytohormone-related pathways in fungi
  • Identification of candidate functional homologs for experimental validation
  • Comparative analysis of fungal signaling networks across taxonomic scales
  • Detection of horizontal gene transfer events
  • Functional prediction based on phylogenetic context
  • Selection of targets for structural or biochemical characterization

Best Practices

General Recommendations

  1. Seed sequence quality: Use experimentally validated proteins when available
  2. Database curation: Remove redundant sequences and verify annotations
  3. BLAST parameters: Adjust word size and matrix based on evolutionary distance
  4. Alignment inspection: Manually verify critical motifs and domains
  5. Model selection: Allow IQ-TREE to explore multiple models
  6. Bootstrap support: Report both UFBoot and SH-aLRT values
  7. Metadata integration: Ensure taxonomic IDs are current and accurate

Contributing

We welcome contributions to improve this pipeline! Here's how you can help:

Reporting Issues

If you encounter bugs or have suggestions:

  1. Check if the issue already exists in Issues
  2. Create a new issue with:
    • Clear title describing the problem
    • Steps to reproduce the issue
    • Expected behavior vs actual behavior
    • Environment details (OS, software versions)
    • Error messages or log files

Submitting Changes

  1. Fork the repository

    git clone https://github.com/yourusername/phytohormone-phylogenomics.git
cd phytohormone-phylogenomics

  2. Create a feature branch

    git checkout -b feature/your-feature-name

  3. Make your changes

    • Follow existing code style
    • Add comments explaining complex steps
    • Update documentation if needed

  4. Test your changes

    # Run on test dataset
bash scripts/test_pipeline.sh

  5. Commit and push

    git add .
git commit -m "Add feature: description"
git push origin feature/your-feature-name

  6. Create a Pull Request

    • Describe what your changes do
    • Reference any related issues
    • Include example output if applicable

Code of Conduct

  • Be respectful and inclusive
  • Provide constructive feedback
  • Focus on the science and methodology
  • Acknowledge contributions from others

Citation

If you use this workflow in your research, please cite:

Primary Citation

Manuscript:

García-Estrada, D.A., et al. (2026). When Fungi Speak the Language of Plants: Shared Phytohormones with Divergent Meanings...

BibTeX:

@article{garcia2026,
  title={When Fungi Speak the Language of Plants: Shared Phytohormones with Divergent Meanings},
  author={García-Estrada, David Alberto and ...},
  journal={ASM Microbiology Society},
  year={2026},
  note={Manuscript submitted}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

This work was supported by:

  • Funding:
  • Databases: UniProt, NCBI, JGI MycoCosm teams
  • Open-Source Community: Developers of all bioinformatics tools used

We thank:

  • The Bioconductor and CRAN communities for R package development
  • All contributors who provided feedback and suggestions

Special acknowledgments:

  • Reviewers for their constructive feedback
  • The open science community for data sharing

Contact

David Alberto García Estrada
Researcher
Center for Research in Advanced Materials (CIMAV)
Chihuahua, México

📧 Email: david.garcia@cimav.edu.mx
🔬 ORCID: 0009-0007-1169-5329 💼 ResearchGate: David-Garcia-Estrada

For technical questions:

  • Open an issue on GitHub Issues
  • Check existing issues before creating new ones

For collaboration inquiries:

  • Contact via email with subject: "Collaboration - Phytohormone Phylogenomics"

Repository:

FAQ

Q: Can I use this pipeline for other organisms besides fungi?
A: Yes! The pipeline is designed to be taxonomically agnostic. Just replace the fungal protein database with your organisms of interest and adjust metadata accordingly.

Q: How long does the pipeline take to run?
A: For a typical gene family with 100-200 sequences: BLAST (5-30 min), alignment (30 min - 4 hours), IQ-TREE (1-12 hours depending on model complexity). Total: 2-16 hours.

Q: Do I need a GPU for this pipeline?
A: Not for the phylogenetic analysis. GPU is only recommended for AlphaFold structural modeling, which is optional.

Q: Can I run this on Windows?
A: Yes, using Windows Subsystem for Linux (WSL2) or Docker. We recommend Docker for Windows users.

Q: What if my gene family has >1000 sequences?
A: Consider using approximate methods (FastTree instead of IQ-TREE) or representative sampling. IQ-TREE can handle large datasets but may require substantial time.

Q: Is there a maximum number of sequences I can analyze?
A: Practical limits depend on available RAM and time. We've successfully analyzed datasets with 200+ sequences. For >500 sequences, consider filtering or sampling strategies.

Last Updated: February 2026
Maintainer: David Alberto García Estrada
Status: Active Development

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Complementary pipeline for the review “Phytohormones in Filamentous Fungi: Biosynthesis, Perception, and Growth Regulation Response” — from homology search to 3D structure prediction.

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