Ancestral Sequence Reconstruction
PAML Ancestral Reconstruction
'''Ancestral sequence reconstruction with PAML codeml/baseml'''
import subprocess
import re
from Bio import SeqIO
from Bio.Seq import Seq
def create_asr_control(alignment, tree, output_dir, seq_type='protein'):
'''Create control file for ancestral reconstruction
RateAncestor = 1: Enable ancestral reconstruction
Generates RST file with ancestral sequences
For codons: Use codeml with seqtype = 1
For amino acids: Use codeml with seqtype = 2
For nucleotides: Use baseml
'''
if seq_type == 'protein':
ctl = f'''
seqfile = {alignment}
treefile = {tree}
outfile = {output_dir}/asr.mlc
seqtype = 2
model = 3
aaRatefile = wag.dat
RateAncestor = 1
cleandata = 0
'''
else: # codon
ctl = f'''
seqfile = {alignment}
treefile = {tree}
outfile = {output_dir}/asr.mlc
seqtype = 1
CodonFreq = 2
model = 0
NSsites = 0
RateAncestor = 1
cleandata = 0
'''
ctl_file = f'{output_dir}/asr.ctl'
with open(ctl_file, 'w') as f:
f.write(ctl)
return ctl_file
def parse_rst_file(rst_file):
'''Parse PAML RST file for ancestral sequences
RST contains:
- Tree with node numbers
- Ancestral sequences at each node
- Posterior probabilities for each site
Node numbering: Extant sequences first, then internal nodes
'''
ancestors = {}
current_node = None
current_seq = []
with open(rst_file) as f:
content = f.read()
# Find ancestral sequence section
if 'Ancestral reconstruction by' in content:
sections = content.split('Ancestral reconstruction by')
for section in sections[1:]:
lines = section.strip().split('\n')
for line in lines:
if line.startswith('node #'):
if current_node and current_seq:
ancestors[current_node] = ''.join(current_seq)
match = re.search(r'node #(\d+)', line)
if match:
current_node = f'Node_{match.group(1)}'
current_seq = []
elif current_node and line.strip() and not line.startswith(' '):
# Sequence line
seq_part = ''.join(line.split()[1:]) if len(line.split()) > 1 else ''
current_seq.append(seq_part)
if current_node and current_seq:
ancestors[current_node] = ''.join(current_seq)
return ancestors
def extract_marginal_probabilities(rst_file):
'''Extract site-wise posterior probabilities
High confidence: P > 0.95 (commonly used threshold)
Moderate confidence: P > 0.80
Low confidence: P < 0.80 (consider alternatives)
Report ambiguous sites for experimental validation
'''
site_probs = []
with open(rst_file) as f:
in_probs = False
for line in f:
if 'Prob of best state' in line:
in_probs = True
continue
if in_probs and line.strip():
parts = line.split()
if len(parts) >= 3:
try:
site = int(parts[0])
state = parts[1]
prob = float(parts[2])
site_probs.append({
'site': site,
'state': state,
'probability': prob,
'confidence': 'high' if prob > 0.95 else 'moderate' if prob > 0.8 else 'low'
})
except ValueError:
in_probs = False
return site_probs
IQ-TREE Ancestral Reconstruction
def run_iqtree_asr(alignment, tree=None, model='LG+G4', output_prefix='asr'):
'''Run IQ-TREE for ancestral sequence reconstruction
IQ-TREE provides:
- Marginal reconstruction (default)
- Joint reconstruction (-asr-joint)
- State file (.state) with probabilities
Advantages over PAML:
- Automatic model selection
- Better handling of gaps
- Faster for large datasets
'''
cmd = f'iqtree2 -s {alignment} -m {model} --ancestral -pre {output_prefix}'
if tree:
cmd += f' -te {tree}'
subprocess.run(cmd, shell=True)
return f'{output_prefix}.state'
def parse_iqtree_state(state_file):
'''Parse IQ-TREE .state file
Format: Node Site State Probability [other states and probs]
'''
ancestors = {}
with open(state_file) as f:
next(f) # Skip header
for line in f:
parts = line.strip().split('\t')
if len(parts) >= 4:
node = parts[0]
site = int(parts[1])
state = parts[2]
prob = float(parts[3])
if node not in ancestors:
ancestors[node] = {'sequence': [], 'probabilities': []}
ancestors[node]['sequence'].append(state)
ancestors[node]['probabilities'].append(prob)
# Convert to sequences
for node in ancestors:
ancestors[node]['sequence'] = ''.join(ancestors[node]['sequence'])
return ancestors
Alternative State Analysis
def get_alternative_states(site_probs, threshold=0.1):
'''Identify sites with plausible alternative ancestral states
Alternative states with P > 0.1 should be considered
for experimental validation (ancestral protein resurrection)
These sites may:
- Affect function differently
- Represent true ancestral ambiguity
- Be targets for directed evolution
'''
ambiguous_sites = []
for site_data in site_probs:
if 'alternatives' in site_data:
significant_alts = [
alt for alt in site_data['alternatives']
if alt['probability'] > threshold
]
if significant_alts:
ambiguous_sites.append({
'site': site_data['site'],
'best_state': site_data['state'],
'best_prob': site_data['probability'],
'alternatives': significant_alts
})
return ambiguous_sites
def calculate_sequence_confidence(site_probs):
'''Calculate overall confidence in ancestral sequence
Metrics:
- Mean posterior probability
- Fraction of high-confidence sites (P > 0.95)
- Number of ambiguous positions
'''
if not site_probs:
return None
probs = [s['probability'] for s in site_probs]
high_conf = sum(1 for p in probs if p > 0.95) / len(probs)
low_conf = sum(1 for p in probs if p < 0.8)
return {
'mean_probability': sum(probs) / len(probs),
'high_confidence_fraction': high_conf,
'low_confidence_sites': low_conf,
'total_sites': len(probs),
'overall_quality': 'high' if high_conf > 0.9 else 'moderate' if high_conf > 0.7 else 'low'
}
Ancestral Protein Resurrection
def design_asr_construct(ancestral_seq, extant_reference, ambiguous_sites):
'''Design constructs for ancestral protein resurrection
Strategy:
1. Use most probable state at each position
2. Create alternative constructs at ambiguous sites
3. Consider codon optimization for expression host
Validation:
- Test activity of resurrected proteins
- Compare to extant proteins
- Test alternative constructs at ambiguous positions
'''
constructs = [{'name': 'ASR_ML', 'sequence': ancestral_seq, 'description': 'Maximum likelihood ancestral'}]
# Create alternative constructs for ambiguous sites
for site in ambiguous_sites[:5]: # Limit to top 5 ambiguous
alt_seq = list(ancestral_seq)
best_alt = site['alternatives'][0]
alt_seq[site['site'] - 1] = best_alt['state']
constructs.append({
'name': f"ASR_alt_{site['site']}",
'sequence': ''.join(alt_seq),
'description': f"Alternative at position {site['site']}: {best_alt['state']}"
})
return constructs
Related Skills
- comparative-genomics/positive-selection - Selection analysis on ancestral branches
- comparative-genomics/ortholog-inference - Identify orthologs for reconstruction
- phylogenetics/modern-tree-inference - Generate trees for ASR
- alignment/pairwise-alignment - Prepare MSA for reconstruction
FridrichMethod