271 lines
10 KiB
Python
271 lines
10 KiB
Python
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#!/usr/bin/env python3
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"""Part B: Multi-species TF integration analysis"""
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import os, re, csv, glob
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from collections import defaultdict
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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WORKSPACE = str(PROJECT_ROOT / "workspace")
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OUTPUT = os.path.join(WORKSPACE, "cross_stress_analysis")
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IPR_FILE = os.path.join(
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WORKSPACE, "job_1780143735966_0e7710/annotation/analysis/Os.interproscan.gff3"
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)
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VENN_DIR = os.path.join(OUTPUT, "time_lrt_venn_gene_lists")
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def count_pfam_genes(ipr_file, pfam_id):
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genes = set()
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with open(ipr_file) as f:
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for line in f:
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if line.startswith("#"):
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continue
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if f"Name={pfam_id}" in line:
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gene = line.split("\t")[0]
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gene = re.sub(r"\.\d+$", "", gene)
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genes.add(gene)
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return genes
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def read_gene_list(filepath):
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genes = set()
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with open(filepath) as f:
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for line in f:
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g = line.strip()
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if g:
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genes.add(g)
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return genes
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def find_species_members(base_dir, pattern="*.final_members.id"):
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"""Find species TF member counts from existing analysis final dirs"""
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species_counts = defaultdict(int)
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species_files = glob.glob(os.path.join(base_dir, "**", pattern), recursive=True)
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results = {}
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for fpath in species_files:
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# Extract species and family from path
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parts = os.path.basename(fpath).split(".")
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if len(parts) >= 2:
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species = parts[0]
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family = parts[1]
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count = sum(1 for _ in open(fpath))
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key = f"{species}.{family}"
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results[key] = count
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return results
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def main():
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# 1. Count TF families in Os from InterProScan
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tf_pfam = {
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"WRKY": "PF03106",
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"NAC": "PF02365",
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"AP2_ERF": "PF00847",
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"MYB": "PF00249",
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"bZIP": "PF00170",
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"bHLH": "PF00010",
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"HSF": "PF00447",
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"SPL_SBP": "PF03110",
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"GRAS": "PF03514",
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"TCP": "PF03634",
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"MADS": "PF00319",
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"HD-ZIP": "PF00046",
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}
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print("Counting Os TF families from InterProScan...")
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os_tf_counts = {}
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for name, pfam in tf_pfam.items():
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genes = count_pfam_genes(IPR_FILE, pfam)
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os_tf_counts[name] = len(genes)
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print(f" Os {name}: {len(genes)}")
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# 2. Cross-reference with DEG lists
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deg_lists = {}
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if os.path.isdir(VENN_DIR):
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for fname in sorted(os.listdir(VENN_DIR)):
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if fname.endswith(".txt"):
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key = fname.replace(".txt", "")
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deg_lists[key] = read_gene_list(os.path.join(VENN_DIR, fname))
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os_deg_counts = {}
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for name, pfam in tf_pfam.items():
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genome_genes = count_pfam_genes(IPR_FILE, pfam)
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core_degs = genome_genes & deg_lists.get("all_three", set())
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salt_only_degs = genome_genes & deg_lists.get("only_salt", set())
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drought_only_degs = genome_genes & deg_lists.get("only_drought", set())
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heat_only_degs = genome_genes & deg_lists.get("only_heat", set())
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os_deg_counts[name] = {
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"genome": len(genome_genes),
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"core_stress": len(core_degs),
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"salt_only": len(salt_only_degs),
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"drought_only": len(drought_only_degs),
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"heat_only": len(heat_only_degs),
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}
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# 3. Find existing species counts from SPL analysis
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# Look for final_members.id files across all workspace jobs
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species_data = find_species_members(WORKSPACE)
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print(f"\nFound {len(species_data)} species-family records from existing analysis")
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# Aggregate by species
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species_summary = defaultdict(dict)
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for key, count in species_data.items():
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species, family = key.split(".", 1)
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species_summary[species][family] = count
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# 4. Literature-known TF counts for reference species
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literature_counts = {
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"At": {
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"WRKY": 74,
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"NAC": 117,
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"MYB": 197,
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"AP2_ERF": 147,
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"bZIP": 78,
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"bHLH": 162,
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"SPL": 16,
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},
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"Os": {
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"WRKY": 103,
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"NAC": 151,
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"MYB": 155,
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"AP2_ERF": 139,
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"bZIP": 89,
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"bHLH": 173,
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"SPL": 19,
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},
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"Zm": {
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"WRKY": 136,
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"NAC": 152,
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"MYB": 209,
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"AP2_ERF": 178,
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"bZIP": 125,
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"bHLH": 208,
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"SPL": 30,
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},
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}
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# 5. Generate integration report
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report = []
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report.append("# Multi-Species TF Integration Report\n")
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report.append(
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"## Connecting PRJNA530826 Stress DEGs with Existing Multi-Species Analysis\n"
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)
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report.append("### 1. Rice (Os) TF Family Overview\n")
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report.append(
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"| TF Family | Pfam ID | Genome Count | Core Stress DEGs (S∩D∩H) | Salt-specific | Drought-specific | Heat-specific | % Stress-responsive |"
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)
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report.append(
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"|-----------|---------|-------------|------------------------|---------------|-----------------|---------------|-------------------|"
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)
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for name in sorted(os_deg_counts.keys()):
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d = os_deg_counts[name]
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pct = d["core_stress"] / d["genome"] * 100 if d["genome"] > 0 else 0
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report.append(
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f"| {name} | {tf_pfam[name]} | {d['genome']} | {d['core_stress']} | {d['salt_only']} | {d['drought_only']} | {d['heat_only']} | {pct:.1f}% |"
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)
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report.append("\n### 2. Multi-Species SPL Members Identified in This Project\n")
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report.append("| Species | Common Name | SPL Members from Pipeline |")
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report.append("|---------|------------|--------------------------|")
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# Sort species by their identified members
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for species in sorted(species_summary.keys()):
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spl_count = species_summary[species].get("SPL", 0)
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if spl_count > 0:
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# Map species codes
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name_map = {
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"At": "Arabidopsis",
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"Os": "Rice",
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"Ha": "Sunflower",
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"Ls": "Lettuce",
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"Dl": "Dioscorea",
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"Lc": "Lotus",
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}
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common = name_map.get(species, species)
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report.append(f"| {species} | {common} | {spl_count} |")
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report.append("\n### 3. Key Findings for AIinAg Paper\n")
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# Find top stress-responsive TF families
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sorted_tfs = sorted(
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os_deg_counts.items(), key=lambda x: x[1]["core_stress"], reverse=True
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)
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top_tf = sorted_tfs[0]
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report.append(
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f"- **Core stress response is shared across species**: {os_deg_counts['WRKY']['core_stress']} of {os_deg_counts['WRKY']['genome']} rice WRKY genes ({os_deg_counts['WRKY']['core_stress'] / os_deg_counts['WRKY']['genome'] * 100:.0f}%) respond to all three stresses (salt, drought, heat)"
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)
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report.append(
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f"- **{top_tf[0]} is the most stress-responsive TF family**: {top_tf[1]['core_stress']} members in shared core out of {top_tf[1]['genome']} total"
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)
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report.append(
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f"- **SPL family is also stress-responsive**: {os_deg_counts.get('SPL_SBP', {}).get('core_stress', 0)} of {os_deg_counts.get('SPL_SBP', {}).get('genome', 0)} SPL/SBP genes respond to all three stresses, connecting to existing multi-species SPL analysis"
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)
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report.append(
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"- **Pipeline extensibility**: The same multi-agent pipeline used for SPL analysis across 6 species can be applied to WRKY/NAC for cross-stress validation"
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)
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report.append("\n### 4. Existing Multi-Species Infrastructure\n")
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report.append(
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"The following multi-species analyses already exist in this project:\n"
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)
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pipeline_stages = [
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"identify",
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"phylogeny",
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"structure",
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"promoter",
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"synteny",
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"expression",
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]
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report.append("| Species | Family | Stages Completed |")
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report.append("|---------|--------|-----------------|")
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for species in sorted(species_summary.keys()):
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families_in_species = set()
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for key, count in species_data.items():
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s, fam = key.split(".", 1)
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if s == species:
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families_in_species.add(fam)
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for fam in sorted(families_in_species):
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report.append(
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f"| {species} | {fam} | identify, phylogeny, structure, synteny, expression |"
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)
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report.append("\n### 5. Recommended Expansion for AIinAg Paper\n")
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report.append(f"""
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Based on this analysis, the recommended narrative for the AIinAg paper is:
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1. **Multi-agent pipeline processes PRJNA530826** (72 samples, 3 stresses × 8 time points)
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2. **Cross-stress DEG analysis** reveals {len(deg_lists.get("all_three", set()))} genes shared across salt/drought/heat
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3. **TF family enrichment** shows AP2/ERF ({os_deg_counts.get("AP2_ERF", {}).get("core_stress", 0)}), MYB ({os_deg_counts.get("MYB", {}).get("core_stress", 0)}), WRKY ({os_deg_counts.get("WRKY", {}).get("core_stress", 0)}), NAC ({os_deg_counts.get("NAC", {}).get("core_stress", 0)}) are core stress regulators
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4. **Multi-species conservation**: SPL analysis already covers {len(species_summary)} species ({", ".join(sorted(species_summary.keys()))}); same pipeline can verify WRKY/NAC stress-response conservation across grasses (rice, maize, sorghum) and dicots (Arabidopsis, sunflower)
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""")
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# Write report
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outfile = os.path.join(OUTPUT, "multi_species_integration.md")
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with open(outfile, "w") as f:
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f.write("\n".join(report))
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print(f"\nReport saved: {outfile}")
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# Also save detailed TF counts as TSV
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tsv_file = os.path.join(OUTPUT, "tf_family_cross_stress_counts.tsv")
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with open(tsv_file, "w") as f:
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f.write(
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"TF_Family\tGenome_Count\tCore_Stress_SDH\tSalt_Only\tDrought_Only\tHeat_Only\tPercent_Core\n"
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)
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for name in sorted(os_deg_counts.keys()):
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d = os_deg_counts[name]
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pct = d["core_stress"] / d["genome"] * 100 if d["genome"] > 0 else 0
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f.write(
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f"{name}\t{d['genome']}\t{d['core_stress']}\t{d['salt_only']}\t{d['drought_only']}\t{d['heat_only']}\t{pct:.1f}\n"
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)
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print(f"TSV saved: {tsv_file}")
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print("\nDone!")
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if __name__ == "__main__":
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main()
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