| """Generate per-species codon-usage tables from real Kazusa CUTG data.
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| Reads ``core/data/cutg_plant_subset.spsum`` (the nuclear codon counts for the 18
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| supported crops, taken verbatim from the Kazusa Codon Usage Tabulated from
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| GenBank plant division) and writes ``core/codon_usage_tables.py`` containing:
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| CODON_USAGE_TABLES {species: {aa: {codon: fraction}}} real per-AA usage
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| SPECIES_CDS_COUNT {species: int} CDS compiled (provenance)
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| Every species therefore uses its OWN measured codon usage — no cross-species
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| proxies. Run from the ``cool/`` directory:
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| python -m tools.build_codon_usage
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| This keeps the data reproducible: the spsum subset is committed, and re-running
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| the generator reproduces the table module byte-for-byte.
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| """
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| import os
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| from core.codons import CODON_TO_AMINO_ACID
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| HERE = os.path.dirname(os.path.abspath(__file__))
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| CORE = os.path.normpath(os.path.join(HERE, "..", "core"))
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| SUBSET = os.path.join(CORE, "data", "cutg_plant_subset.spsum")
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| OUT = os.path.join(CORE, "codon_usage_tables.py")
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| ORDER_RNA = ("CGA CGC CGG CGU AGA AGG CUA CUC CUG CUU UUA UUG UCA UCC UCG UCU AGC AGU "
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| "ACA ACC ACG ACU CCA CCC CCG CCU GCA GCC GCG GCU GGA GGC GGG GGU GUA GUC GUG GUU "
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| "AAA AAG AAC AAU CAA CAG CAC CAU GAA GAG GAC GAU UAC UAU UGC UGU UUC UUU "
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| "AUA AUC AUU AUG UGG UAA UAG UGA").split()
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| ORDER = [c.replace("U", "T") for c in ORDER_RNA]
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| assert len(ORDER) == 64
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| AA_ORDER = list("ACDEFGHIKLMNPQRSTVWY") + ["_"]
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| def parse_subset(path):
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| species, counts, names = {}, {}, {}
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| cur = None
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| with open(path, errors="replace") as fh:
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| lines = [ln.rstrip("\n") for ln in fh]
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| i = 0
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| while i < len(lines):
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| ln = lines[i]
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| if ln.startswith("# species="):
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| cur = ln.split("=", 1)[1].strip()
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| elif cur and ln and ln[0].isdigit() and ":" in ln:
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| names[cur] = ln.split(":", 1)[1].rsplit(":", 1)[0].strip()
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| counts[cur] = int(ln.rsplit(":", 1)[1])
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| nums = list(map(int, lines[i + 1].split()))
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| assert len(nums) == 64, f"{cur}: expected 64 codon counts"
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| species[cur] = nums
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| i += 1
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| cur = None
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| i += 1
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| return species, counts, names
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| def to_usage(raw):
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| by_aa = {}
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| for codon, n in zip(ORDER, raw):
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| by_aa.setdefault(CODON_TO_AMINO_ACID[codon], {})[codon] = n
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| out = {}
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| for aa, cod in by_aa.items():
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| tot = sum(cod.values())
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| out[aa] = {c: (round(v / tot, 3) if tot else 0.0) for c, v in cod.items()}
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| return out
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| def fmt_table(usage):
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| rows = []
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| for aa in AA_ORDER:
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| if aa not in usage:
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| continue
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| inner = ", ".join(f"'{c}': {f:.3f}" for c, f in usage[aa].items())
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| rows.append(f" '{aa}': {{{inner}}},")
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| return "\n".join(rows)
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| def main():
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| species, counts, names = parse_subset(SUBSET)
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| order = ['arabidopsis', 'rice', 'maize', 'tomato', 'soybean', 'wheat', 'barley',
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| 'sorghum', 'potato', 'cassava', 'tobacco', 'grape', 'cotton', 'sugarcane',
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| 'canola', 'banana', 'peanut', 'sunflower']
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| lines = ['"""Per-species codon-usage tables — GENERATED, do not edit by hand.',
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| "",
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| "Source: Kazusa CUTG (Codon Usage Tabulated from GenBank), plant division.",
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| "Each table is the measured nuclear codon usage for that species' own taxid —",
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| "no cross-species proxies. Regenerate with: python -m tools.build_codon_usage",
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| "",
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| "SPECIES_CDS_COUNT records how many CDS were compiled per species (provenance /",
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| "confidence): larger = more reliable codon statistics.",
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| '"""',
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| "",
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| "CODON_USAGE_TABLES = {"]
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| for sp in order:
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| u = to_usage(species[sp])
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| lines.append(f" # {names[sp]} (taxid in subset; {counts[sp]:,} CDS compiled)")
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| lines.append(f" '{sp}': {{")
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| lines.append(fmt_table(u))
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| lines.append(" },")
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| lines.append("}")
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| lines.append("")
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| lines.append("SPECIES_CDS_COUNT = {")
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| for sp in order:
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| lines.append(f" '{sp}': {counts[sp]},")
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| lines.append("}")
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| lines.append("")
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| with open(OUT, "w") as fh:
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| fh.write("\n".join(lines))
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| print("wrote", os.path.relpath(OUT), "—", len(order), "species")
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| for sp in order:
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| print(f" {sp:12} {counts[sp]:>7,} CDS")
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| if __name__ == "__main__":
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| main()
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|
