bio-comparative-genomics-positive-selection

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---
name: bio-comparative-genomics-positive-selection
description: Detect positive selection using dN/dS (omega) tests with PAML codeml and HyPhy. Identify sites and branches under adaptive evolution through codon models and branch-site tests. Use when testing for adaptive evolution in gene families or identifying positively selected sites.
tool_type: mixed
primary_tool: PAML
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
  - read_file
  - run_shell_command
---

# Positive Selection Analysis

## dN/dS Overview

```python
'''
dN/dS (omega, ω) interpretation:
- ω < 1: Purifying (negative) selection - deleterious mutations removed
- ω = 1: Neutral evolution - no selective pressure
- ω > 1: Positive (diversifying) selection - advantageous mutations favored

Most genes: ω << 1 (strong purifying selection)
Immune genes, reproduction: Often show ω > 1 at specific sites
'''
```

## PAML Codeml Analysis

```python
'''Run PAML codeml for selection analysis'''

import subprocess
import os
from Bio import SeqIO
from Bio.Seq import Seq


def prepare_codon_alignment(cds_fasta, output_phy):
    '''Prepare codon alignment in PHYLIP format

    Requirements:
    - CDS sequences (in-frame, no stop codons except terminal)
    - Multiple sequence alignment already performed
    - Sequence length divisible by 3
    '''
    records = list(SeqIO.parse(cds_fasta, 'fasta'))

    # Validate codon alignment
    for rec in records:
        if len(rec.seq) % 3 != 0:
            print(f'Warning: {rec.id} length not divisible by 3')

    # Write PHYLIP format
    n_seq = len(records)
    seq_len = len(records[0].seq)

    with open(output_phy, 'w') as f:
        f.write(f' {n_seq} {seq_len}\n')
        for rec in records:
            # PHYLIP names: 10 characters, padded
            name = rec.id[:10].ljust(10)
            f.write(f'{name}{str(rec.seq)}\n')

    return output_phy


def create_codeml_control(alignment_file, tree_file, output_dir, model='M8'):
    '''Create codeml control file

    Site models for detecting positive selection:
    - M0: One ratio (single ω for all sites)
    - M1a: Nearly neutral (ω0 < 1, ω1 = 1)
    - M2a: Positive selection (ω0 < 1, ω1 = 1, ω2 > 1)
    - M7: Beta (ω from beta distribution, 0 < ω < 1)
    - M8: Beta + ω > 1 (allows positive selection)
    - M8a: Beta + ω = 1 (null for M8 comparison)

    Standard comparison: M8 vs M7 or M8 vs M8a
    '''
    model_params = {
        'M0': {'NSsites': 0, 'model': 0},
        'M1a': {'NSsites': 1, 'model': 0},
        'M2a': {'NSsites': 2, 'model': 0},
        'M7': {'NSsites': 7, 'model': 0},
        'M8': {'NSsites': 8, 'model': 0},
        'M8a': {'NSsites': 8, 'model': 0, 'fix_omega': 1, 'omega': 1},
    }

    params = model_params.get(model, model_params['M8'])

    ctl_content = f'''
      seqfile = {alignment_file}
     treefile = {tree_file}
      outfile = {output_dir}/mlc

        noisy = 9
      verbose = 1
      runmode = 0
      seqtype = 1
    CodonFreq = 2
        model = {params.get('model', 0)}
      NSsites = {params.get('NSsites', 8)}
        icode = 0
    fix_kappa = 0
        kappa = 2
    fix_omega = {params.get('fix_omega', 0)}
        omega = {params.get('omega', 1)}
    '''

    ctl_file = f'{output_dir}/codeml_{model}.ctl'
    with open(ctl_file, 'w') as f:
        f.write(ctl_content)

    return ctl_file


def run_codeml(ctl_file):
    '''Run PAML codeml'''
    cmd = f'codeml {ctl_file}'
    result = subprocess.run(cmd, shell=True, capture_output=True, text=True)

    if result.returncode != 0:
        print(f'Codeml error: {result.stderr}')

    return result


def parse_codeml_output(mlc_file):
    '''Parse codeml output for likelihood and parameters'''
    results = {'lnL': None, 'omega': None, 'kappa': None, 'sites': []}

    with open(mlc_file) as f:
        content = f.read()

    # Extract log-likelihood
    for line in content.split('\n'):
        if 'lnL' in line and 'np' in line:
            parts = line.split()
            for i, p in enumerate(parts):
                if p == 'lnL':
                    results['lnL'] = float(parts[i + 2])
                    break

        # Extract omega values
        if 'omega' in line.lower() and '=' in line:
            parts = line.split('=')
            if len(parts) >= 2:
                try:
                    results['omega'] = float(parts[-1].strip().split()[0])
                except ValueError:
                    pass

    # Extract positively selected sites (BEB analysis)
    if 'Bayes Empirical Bayes' in content:
        beb_section = content.split('Bayes Empirical Bayes')[1]
        for line in beb_section.split('\n'):
            parts = line.split()
            if len(parts) >= 5:
                try:
                    site = int(parts[0])
                    aa = parts[1]
                    prob = float(parts[2])
                    # Sites with P > 0.95 considered significant
                    # Sites with P > 0.99 highly significant
                    if prob > 0.95:
                        results['sites'].append({
                            'position': site,
                            'amino_acid': aa,
                            'probability': prob,
                            'significance': '**' if prob > 0.99 else '*'
                        })
                except (ValueError, IndexError):
                    continue

    return results


def likelihood_ratio_test(lnL_null, lnL_alt, df=2):
    '''Perform likelihood ratio test

    For M8 vs M7: df = 2
    For M2a vs M1a: df = 2
    For branch-site test: df = 1

    Signific