BytesAgain is a curated directory of 60,000+ AI agent skills from ClawHub, GitHub, LobeHub, and Dify. Search skills by keyword in 7 languages, browse by role (developer, creator, trader, marketer) or by use case.

How do I find AI skills on BytesAgain?

Use the search bar on BytesAgain.com to search by keyword in 7 languages. You can also browse by role (developer, creator, trader, marketer) or by use case. Each skill shows install instructions for Claude, Cursor, OpenClaw, Continue, and more.

Yes, BytesAgain is completely free. No registration required for searching skills. The MCP API is also free with rate limits.

Does BytesAgain have an API for AI agents?

Yes! BytesAgain provides a free MCP SSE endpoint at /api/mcp/sse for AI agents, plus a REST API at /api/mcp?action=search&q= . No authentication needed.

Can I request a new AI skill on BytesAgain?

Yes! Visit the Requests page on BytesAgain.com to submit a skill request. Your request will be visible to the community and notified to the site admin.

🦀 ClawHub

Protein Phylogeny

by @billwanttobetop

Comprehensive protein family phylogenetic analysis workflow with quality control, conservation analysis, coevolution network analysis, and publication-ready...

Versionv1.5.0

Downloads608

#crypto-defi #legal #automation

View on ClawHub →

TERMINAL

clawhub install protein-phylogeny

📖 About This Skill

name: protein-phylogeny description: "Comprehensive protein family phylogenetic analysis workflow with quality control, conservation analysis, coevolution network analysis, and publication-ready visualization. Use when: (1) analyzing protein family evolution, (2) building phylogenetic trees from sequences, (3) identifying conserved/coevolved residues, (4) generating publication-quality figures and reports, (5) quality-controlling sequence datasets, or (6) performing systematic evolutionary analysis of enzyme families, protein superfamilies, or any homologous protein groups."

Protein Family Phylogenetic Analysis

Complete workflow for protein family evolutionary analysis: quality control → conservation → coevolution → phylogeny → publication report.

Quick Start

Input: FASTA file with protein sequences (any family, any size) Output: Publication-ready report with phylogenetic tree, conservation analysis, coevolution networks, and high-quality figures

Typical workflow:

# 1. Quality control (removes low-quality sequences)
bash scripts/01_quality_control.sh input.fasta output_dir/
2. Conservation analysis
bash scripts/02_conservation.sh output_dir/qc/final.fasta output_dir/
3. Coevolution analysis
bash scripts/03_coevolution.sh output_dir/qc/final.fasta output_dir/
4. Phylogenetic tree
bash scripts/04_phylogeny.sh output_dir/qc/final.fasta output_dir/
5. Generate figures
bash scripts/05_visualize.sh output_dir/
6. Create report
bash scripts/06_report.sh output_dir/ "Family Name"

Workflow Overview

Stage 1: Quality Control (references/01-quality-control.md)

Purpose: Filter raw sequences to high-quality, non-redundant dataset

Steps: 1. Literature validation (remove predicted sequences) 2. Length filtering (remove fragments/fusions) 3. CD-HIT redundancy removal (90% identity) 4. Complexity check (remove low-complexity regions) 5. Motif validation (confirm family membership) 6. MAFFT alignment (high accuracy mode) 7. trimAl trimming (automatic strategy) 8. Final validation (gap ratio, coverage)

Key parameters:

CD-HIT threshold: 90% (adjustable 70-95%)

Length range: mean ± 2 SD

Gap threshold: < 30% per position

Motif coverage: > 50%

Output: qc/final.fasta (high-quality aligned sequences)

Stage 2: Conservation Analysis (references/02-conservation.md)

Purpose: Identify functionally important conserved residues

Method: Shannon entropy

H_norm < 0.3: Highly conserved

H_norm 0.3-0.6: Moderately conserved

H_norm > 0.6: Variable

Output:

Conserved positions list

Conservation landscape plot

Gap vs conservation scatter plot

Stage 3: Coevolution Analysis (references/03-coevolution.md)

Purpose: Identify residue pairs that evolve together

Method: Normalized Mutual Information (NMI)

Corrects for phylogenetic bias

Identifies structural/functional coupling

Builds coevolution network

Output:

Coevolved position pairs (MI scores)

Network graph (hub identification)

Hub residue heatmap

Stage 4: Phylogenetic Analysis (references/04-phylogeny.md)

Purpose: Reconstruct evolutionary relationships

Method: IQ-TREE maximum likelihood

Automatic model selection (ModelFinder)

UFBoot2 ultrafast bootstrap (1000 replicates)

Convergence check (> 0.99 required)

Output:

Phylogenetic tree (.treefile)

Bootstrap consensus tree (.contree)

Model parameters (.iqtree)

Stage 5: Visualization (references/05-visualization.md)

Purpose: Generate publication-quality figures (300 DPI)

Figures: 1. Workflow diagram 2. Conservation heatmap 3. Coevolution network 4. Hub analysis 5. Quality metrics 6. Phylogenetic tree 7. Bootstrap distribution 8. Supplementary plots

Style: Clean, colorblind-friendly, Nature/Science standards

Stage 6: Report Generation (references/06-report.md)

Purpose: Create comprehensive analysis report

Sections: 1. Overview (dataset summary) 2. Quality control (methods + results) 3. Conservation analysis (algorithms + findings) 4. Coevolution analysis (networks + hubs) 5. Phylogenetic analysis (tree + support) 6. Quality assessment (standards comparison) 7. Conclusions (biological insights)

Format: Markdown → Feishu/Word/PDF

Key Features

AI-Friendly Design

Modular scripts: Each stage is independent

Clear parameters: All thresholds documented

Error handling: Automatic validation at each step

Progress tracking: JSON state files

Resume capability: Skip completed stages

Token Efficiency

Progressive disclosure: Load only needed references

Compact instructions: Essential info only

Script execution: No need to read code

Cached results: Reuse intermediate files

Professional Quality

Publication standards: All methods peer-reviewed

Reproducible: Fixed random seeds, versioned tools

Validated: Tested on 10+ protein families

Documented: Complete algorithm explanations

Dependencies

Required tools:

CD-HIT v4.8.1+

MAFFT v7.490+

trimAl v1.4+

IQ-TREE v2.0+

Python 3.8+ (BioPython, NumPy, Matplotlib, NetworkX)

R 4.0+ (ape, phytools)

Installation:

bash scripts/install_dependencies.sh

Common Pitfalls

1. Low Sequence Similarity (< 25%)

Problem: Alignment unreliable, phylogeny uncertain Solution:

Use profile HMM (HMMER) instead of MAFFT

Consider domain-based analysis

Increase CD-HIT threshold to 95%

2. High Gap Ratio (> 30%)

Problem: Many unreliable positions Solution:

Stricter trimAl settings (-gt 0.8)

Manual inspection of alignment

Remove problematic sequences

3. Bootstrap Convergence Failure (< 0.99)

Problem: Tree topology unstable Solution:

Increase bootstrap replicates (2000+)

Try different substitution models

Check for long-branch attraction

4. No Conserved Motifs

Problem: Family definition unclear Solution:

Verify sequences are truly homologous

Use structural alignment (DALI, TM-align)

Consider broader superfamily analysis

Advanced Usage

Custom Quality Control

Edit scripts/01_quality_control.sh parameters:

CDHIT_THRESHOLD=0.85  # More stringent
MIN_LENGTH=200        # Shorter proteins
MAX_LENGTH=600        # Longer proteins
GAP_THRESHOLD=0.25    # Stricter gap cutoff

Alternative Phylogeny Methods

See references/04-phylogeny.md for:

Bayesian inference (MrBayes)

Distance methods (FastTree)

Parsimony (PAUP*)

Custom Visualization

Edit scripts/05_visualize.sh for:

Color schemes

Figure dimensions

Font sizes

Layout styles

Troubleshooting

Issue: CD-HIT crashes with large datasets Fix: Split input, process in batches, merge results

Issue: IQ-TREE runs forever Fix: Use -fast mode or reduce bootstrap replicates

Issue: Figures look pixelated Fix: Increase DPI in scripts/05_visualize.sh (default 300)

Issue: Report generation fails Fix: Check all intermediate files exist, rerun failed stages

References

For detailed methodology, see:

Quality Control

Conservation Analysis

Coevolution Analysis

Phylogenetic Analysis

Visualization

Report Generation

Citation

If you use this workflow, please cite:

CD-HIT: Li & Godzik (2006) Bioinformatics

MAFFT: Katoh & Standley (2013) Mol Biol Evol

trimAl: Capella-Gutiérrez et al. (2009) Bioinformatics

IQ-TREE: Nguyen et al. (2015) Mol Biol Evol

This workflow: [Your publication]

Example Usage

# Download your sequences
(from UniProt, NCBI, or your own database)
Run full workflow
bash scripts/run_full_workflow.sh sequences.fasta analysis_output/ "Your Family Name"
Results in analysis_output/:
- qc/final.fasta (high-quality sequences)
- conservation/ (conserved positions)
- coevolution/ (coevolved pairs)
- phylogeny/ (phylogenetic tree)
- figures/ (publication-quality plots)
- report.md (complete analysis)

💡 Examples

Input: FASTA file with protein sequences (any family, any size) Output: Publication-ready report with phylogenetic tree, conservation analysis, coevolution networks, and high-quality figures

Typical workflow:

# 1. Quality control (removes low-quality sequences)
bash scripts/01_quality_control.sh input.fasta output_dir/
2. Conservation analysis
bash scripts/02_conservation.sh output_dir/qc/final.fasta output_dir/
3. Coevolution analysis
bash scripts/03_coevolution.sh output_dir/qc/final.fasta output_dir/
4. Phylogenetic tree
bash scripts/04_phylogeny.sh output_dir/qc/final.fasta output_dir/
5. Generate figures
bash scripts/05_visualize.sh output_dir/
6. Create report
bash scripts/06_report.sh output_dir/ "Family Name"

📋 Tips & Best Practices

Issue: CD-HIT crashes with large datasets Fix: Split input, process in batches, merge results

Issue: IQ-TREE runs forever Fix: Use -fast mode or reduce bootstrap replicates

Issue: Figures look pixelated Fix: Increase DPI in scripts/05_visualize.sh (default 300)

Issue: Report generation fails Fix: Check all intermediate files exist, rerun failed stages