Synteny Analysis
MCScanX Workflow
'''Synteny analysis with MCScanX and visualization'''
import subprocess
import pandas as pd
from collections import defaultdict
def prepare_mcscanx_input(gff_file, fasta_file, species_prefix):
'''Prepare input files for MCScanX
MCScanX requires:
1. .gff file: gene positions (sp gene chr start end)
2. .blast file: all-vs-all BLASTP results
'''
genes = []
with open(gff_file) as f:
for line in f:
if line.startswith('#'):
continue
parts = line.strip().split('\t')
if parts[2] == 'gene':
chrom = parts[0]
start, end = int(parts[3]), int(parts[4])
gene_id = parts[8].split('ID=')[1].split(';')[0]
genes.append(f'{species_prefix}\t{gene_id}\t{chrom}\t{start}\t{end}')
with open(f'{species_prefix}.gff', 'w') as f:
f.write('\n'.join(genes))
return f'{species_prefix}.gff'
def run_mcscanx(gff1, gff2, blast_file, output_prefix):
'''Run MCScanX for synteny detection
Key parameters:
-k: Match score for collinear genes (default 50)
-g: Gap penalty (default -1)
-s: Minimum syntenic block size (default 5 genes)
-e: E-value threshold for BLAST (default 1e-5)
'''
# Combine GFF files
subprocess.run(f'cat {gff1} {gff2} > {output_prefix}.gff', shell=True)
# Copy BLAST file
subprocess.run(f'cp {blast_file} {output_prefix}.blast', shell=True)
# Run MCScanX
# -s 5: Minimum 5 genes per syntenic block (smaller = more noise)
# -m 25: Maximum gaps allowed (larger = more relaxed blocks)
cmd = f'MCScanX -s 5 -m 25 {output_prefix}'
subprocess.run(cmd, shell=True)
return f'{output_prefix}.collinearity'
def parse_collinearity(collinearity_file):
'''Parse MCScanX collinearity output
Output format:
## Alignment X: score=N e_value=X N genes
X-Y: gene1 gene2
'''
blocks = []
current_block = None
with open(collinearity_file) as f:
for line in f:
if line.startswith('## Alignment'):
if current_block:
blocks.append(current_block)
parts = line.strip().split()
score = int(parts[3].split('=')[1])
e_value = float(parts[4].split('=')[1])
n_genes = int(parts[5])
current_block = {
'score': score,
'e_value': e_value,
'n_genes': n_genes,
'gene_pairs': []
}
elif current_block and '-' in line and ':' in line:
parts = line.strip().split()
if len(parts) >= 3:
gene1, gene2 = parts[1], parts[2]
current_block['gene_pairs'].append((gene1, gene2))
if current_block:
blocks.append(current_block)
return blocks
def classify_synteny_type(blocks, species1_chroms, species2_chroms):
'''Classify syntenic relationships
Types:
- 1:1: Direct orthology (conserved)
- 1:many: Lineage-specific duplication
- many:many: Ancient WGD or complex rearrangement
'''
sp1_coverage = defaultdict(list)
sp2_coverage = defaultdict(list)
for block in blocks:
for gene1, gene2 in block['gene_pairs']:
chr1 = species1_chroms.get(gene1)
chr2 = species2_chroms.get(gene2)
if chr1 and chr2:
sp1_coverage[chr1].append(chr2)
sp2_coverage[chr2].append(chr1)
classifications = []
for chr1, partners in sp1_coverage.items():
unique_partners = len(set(partners))
if unique_partners == 1:
classifications.append(('1:1', chr1, partners[0]))
else:
classifications.append(('1:many', chr1, set(partners)))
return classifications
SyRI for Structural Variants
def run_syri(ref_genome, query_genome, alignment_file, output_prefix):
'''Run SyRI for structural rearrangement identification
SyRI detects:
- Syntenic regions (SYN)
- Inversions (INV)
- Translocations (TRANS)
- Duplications (DUP)
- Insertions/Deletions (INS/DEL)
Requires whole-genome alignment (minimap2 or MUMmer)
'''
# Align genomes with minimap2
align_cmd = f'minimap2 -ax asm5 {ref_genome} {query_genome} > {output_prefix}.sam'
subprocess.run(align_cmd, shell=True)
# Run SyRI
syri_cmd = f'syri -c {output_prefix}.sam -r {ref_genome} -q {query_genome} -F S --prefix {output_prefix}'
subprocess.run(syri_cmd, shell=True)
return f'{output_prefix}syri.out'
def parse_syri_output(syri_file):
'''Parse SyRI structural variant output'''
variants = []
with open(syri_file) as f:
for line in f:
parts = line.strip().split('\t')
if len(parts) >= 10:
var_type = parts[10]
ref_chr, ref_start, ref_end = parts[0], int(parts[1]), int(parts[2])
qry_chr, qry_start, qry_end = parts[5], int(parts[6]), int(parts[7])
variants.append({
'type': var_type,
'ref_chr': ref_chr,
'ref_start': ref_start,
'ref_end': ref_end,
'qry_chr': qry_chr,
'qry_start': qry_start,
'qry_end': qry_end,
'size': ref_end - ref_start
})
return pd.DataFrame(variants)
JCVI Visualization
def create_synteny_plot(blocks_file, layout_file, output_file):
'''Create synteny dot plot with JCVI
JCVI (python-jcvi) provides publication-ready figures
'''
from jcvi.graphics.dotplot import dotplot
from jcvi.graphics.karyotype import karyotype
# Dotplot shows collinear blocks as diagonal lines
# Good for detecting WGD (parallel diagonals)
dotplot_cmd = f'python -m jcvi.graphics.dotplot {blocks_file}'
subprocess.run(dotplot_cmd, shell=True)
# Karyotype view for chromosome-level comparison
karyotype_cmd = f'python -m jcvi.graphics.karyotype {layout_file}'
subprocess.run(karyotype_cmd, shell=True)
def detect_wgd(blocks, min_parallel_blocks=3):
'''Detect whole-genome duplication signatures
WGD indicators:
- Multiple parallel syntenic blocks
- 2:1 or 4:1 chromosome ratios
- Similar Ks peaks across blocks
min_parallel_blocks: Minimum parallel blocks to call WGD
A 2:1 ratio suggests one WGD, 4:1 suggests two WGDs
'''
chrom_ratios = defaultdict(list)
for block in blocks:
if block['n_genes'] >= 10: # Focus on substantial blocks
pairs = block['gene_pairs']
# Extract chromosome info from gene names
# Implementation depends on naming convention
pass
return chrom_ratios
Ks Analysis for Dating
def calculate_ks_for_pairs(cds_file1, cds_file2, gene_pairs):
'''Calculate synonymous substitution rate (Ks) for gene pairs
Ks interpretation:
- Ks < 0.1: Very recent divergence or gene conversion
- Ks 0.1-0.5: Within-species duplication
- Ks 0.5-1.5: Between closely related species
- Ks > 1.5: Saturated, unreliable
Ks peaks indicate WGD events
'''
from Bio import SeqIO
from Bio.Seq import Seq
# Load CDS sequences
cds1 = SeqIO.to_dict(SeqIO.parse(cds_file1, 'fasta'))
cds2 = SeqIO.to_dict(SeqIO.parse(cds_file2, 'fasta'))
ks_values = []
for gene1, gene2 in gene_pairs:
if gene1 in cds1 and gene2 in cds2:
# Run yn00 or codeml for Ks calculation
# Simplified - actual implementation uses PAML
pass
return ks_values
def plot_ks_distribution(ks_values, output_file):
'''Plot Ks distribution to identify WGD peaks'''
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde
# Filter saturated values
# Ks > 2 is typically saturated and unreliable
ks_filtered = [k for k in ks_values if 0 < k < 2]
fig, ax = plt.subplots(figsize=(10, 6))
# Histogram
ax.hist(ks_filtered, bins=50, density=True, alpha=0.7, label='Ks distribution')
# KDE for peak detection
if len(ks_filtered) > 10:
kde = gaussian_kde(ks_filtered)
x = np.linspace(0, 2, 200)
ax.plot(x, kde(x), 'r-', linewidth=2, label='KDE')
ax.set_xlabel('Ks (synonymous substitution rate)')
ax.set_ylabel('Density')
ax.legend()
plt.savefig(output_file)
return fig
Related Skills
- comparative-genomics/positive-selection - dN/dS analysis on syntenic genes
- comparative-genomics/ortholog-inference - Identify orthologs for synteny
- phylogenetics/modern-tree-inference - Phylogenetic context for synteny dating
- alignment/pairwise-alignment - Sequence alignment for Ks calculation
FridrichMethod