Build evolutionary trees with a time axis from genetic sequences and sample dates using TreeTime. Estimate outbreak origins, mutation rates, ancestral sequences, and geographic spread, with guidance for BEAST2 analyses that include uncertainty estimates.
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Construct time-scaled phylogenies and infer evolutionary dynamics using TreeTime and BEAST2 for outbreak analysis. Estimate divergence times, molecular clock rates, and ancestral states. Use when dating outbreak origins, estimating transmission rates, or building time-calibrated trees.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Reference examples tested with: BioPython 1.83+, TreeTime 0.11+, scanpy 1.10+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signatures<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Build a time-scaled tree for my outbreak" → Estimate divergence times and molecular clock rates from dated sequences to reconstruct outbreak timing and evolutionary dynamics.
treetime.TreeTime() for maximum likelihood time-scaled treestreetime --tree tree.nwk --aln aln.fasta --dates dates.tsvfrom treetime import TreeTime
from Bio import Phylo
# Load tree and alignment
tree = Phylo.read('tree.nwk', 'newick')
# Create TreeTime object with dates
# dates_file: tab-separated with columns 'name' and 'date'
# Date formats: 2020.5, 2020-06-15, numeric (decimal year)
tt = TreeTime(
tree=tree,
aln='alignment.fasta',
dates='dates.tsv',
gtr='JC69' # Nucleotide model: JC69, HKY85, GTR
)
# Run molecular clock analysis
tt.run(
root='best', # Root optimization: 'best', 'least-squares', or clade name
Tc='skyline', # Coalescent prior: None, 'skyline', 'opt', or numeric
time_marginal='assign_ml' # Date estimation method
)
# Access results
print(f'Root date: {tt.tree.root.numdate:.2f}')
print(f'Clock rate: {tt.clock_rate:.2e} subs/site/year')
# Install treetime
pip install phylo-treetime
# Basic time tree
treetime --tree tree.nwk --aln alignment.fasta --dates dates.tsv --outdir results/
# With coalescent prior (for population dynamics)
treetime --tree tree.nwk --aln alignment.fasta --dates dates.tsv \
--coalescent skyline --outdir results/
# Ancestral sequence reconstruction
treetime ancestral --tree tree.nwk --aln alignment.fasta --outdir results/
# Mugration (discrete trait analysis, e.g., geographic spread)
treetime mugration --tree tree.nwk --states locations.tsv \
--attribute location --outdir results/
Goal: Prepare a correctly formatted date file mapping sample names to decimal-year collection dates for TreeTime input.
Approach: Extract name and date columns from metadata, convert dates to decimal year format, and write as a tab-separated file with 'name' and 'date' headers.
def prepare_date_file(metadata, name_col, date_col, output_path):
'''Prepare dates file for TreeTime
Date formats accepted:
- Decimal year: 2020.5 (July 2020)
- ISO format: 2020-06-15
- Year only: 2020 (treated as midpoint)
For incomplete dates, use ranges:
- [2020.0:2020.5] for first half of 2020
'''
dates = metadata[[name_col, date_col]].copy()
dates.columns = ['name', 'date']
# Convert to decimal year if needed
dates['date'] = dates['date'].apply(convert_to_decimal_year)
dates.to_csv(output_path, sep='\t', index=False)
return output_path
def convert_to_decimal_year(date_str):
'''Convert date string to decimal year'''
from datetime import datetime
if isinstance(date_str, (int, float)):
return float(date_str)
try:
dt = datetime.strptime(str(date_str), '%Y-%m-%d')
year = dt.year
day_of_year = dt.timetuple().tm_yday
days_in_year = 366 if (year % 4 == 0) else 365
return year + (day_of_year - 1) / days_in_year
except:
return float(date_str) # Assume already decimal
def interpret_clock_rate(rate, genome_length):
'''Interpret molecular clock rate
Args:
rate: Substitutions per site per year
genome_length: Genome size in bp
Returns:
Estimated substitutions per year (genome-wide)
Typical rates:
- RNA viruses: 10^-3 to 10^-4 subs/site/year
- SARS-CoV-2: ~8×10^-4 (24 subs/year for 30kb genome)
- Bacteria: 10^-6 to 10^-7 subs/site/year
- E. coli: ~10^-7 (5 SNPs/genome/year)
'''
subs_per_year = rate * genome_length
print(f'Clock rate: {rate:.2e} subs/site/year')
print(f'Genome-wide: ~{subs_per_year:.1f} substitutions/year')
# Estimate time to MRCA from SNP distance
example_snps = 10
time_estimate = example_snps / (2 * subs_per_year)
print(f'{example_snps} SNPs = ~{time_estimate:.1f} years since MRCA')
Goal: Extract effective population size trajectory over time from a TreeTime coalescent skyline analysis.
Approach: Parse the skyline JSON output from TreeTime to retrieve time points and corresponding Ne estimates for plotting population dynamics.
def extract_skyline(treetime_results_dir):
'''Extract coalescent skyline from TreeTime output
Skyline shows effective population size over time.
Useful for:
- Detecting population expansions (outbreak growth)
- Identifying bottlenecks
- Estimating R0 from growth rate
'''
import json
with open(f'{treetime_results_dir}/skyline.json') as f:
skyline = json.load(f)
times = skyline['times']
Ne = skyline['Ne'] # Effective population size
return times, Ne
def prepare_beast_xml(alignment, dates, template='strict_clock'):
'''Prepare BEAST2 XML configuration
BEAST2 templates:
- strict_clock: Single clock rate (simple outbreaks)
- relaxed_clock: Rate variation (diverse sampling)
- bdsky: Birth-death skyline (epidemic dynamics)
Note: BEAST2 requires more setup than TreeTime but provides
full Bayesian uncertainty estimates.
'''
# BEAST2 typically configured via BEAUti GUI
# For automation, use templates from beast2.org
print('BEAST2 setup:')
print('1. Install BEAST2: https://beast2.org')
print('2. Run BEAUti to configure analysis')
print('3. Execute: beast -threads 4 analysis.xml')
print('4. Summarize: treeannotator -burnin 10 trees.trees output.tree')
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