pick best transcript per variant (1:1) default, --all-transcripts for expansion
- offline GTF now picks MANE>appris1>canonical>basic>protein_coding (318 vs 3018 rows at VAF>10%) - online Ensembl REST likewise picks best (mane_select/canonical) - --all-transcripts restores previous all-transcripts behavior
This commit is contained in:
+81
-40
@@ -54,13 +54,12 @@ def load_token(path="~/.config/oncokb/token"):
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return p.read_text().strip()
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return os.environ.get("ONCOKB_TOKEN", "").strip()
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def vep_annotate(df, reference, gtf_path=None, offline=False):
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def vep_annotate(df, reference, gtf_path=None, offline=False, all_transcripts=False):
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"""Try VEP if installed and cache exists, else GTF offline, else Ensembl REST, else fallback."""
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import shutil
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vep_bin = shutil.which("vep")
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if vep_bin and gtf_path and Path(gtf_path).expanduser().is_file():
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pass
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# Load GTF for offline all-transcript expansion
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gtf_transcripts = None
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if offline and gtf_path:
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gtf_file = Path(gtf_path).expanduser()
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@@ -89,22 +88,37 @@ def vep_annotate(df, reference, gtf_path=None, offline=False):
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})
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continue
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if offline and gtf_transcripts is not None:
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# GTF offline: all transcripts overlapping pos
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hits = [t for t in gtf_transcripts if t["chrom"] == chrom and t["start"] <= pos1 <= t["end"]]
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if hits:
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for t in hits:
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# Simple HGVS: use transcript ID + positional offset (placeholder, VEP would give exact c./p.)
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hgvs_c = f"{t['tx']}:c.{pos1}{ref}>{alt}"
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hgvs_p = "p.(?)"
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effect = t["biotype"] or "transcript_variant"
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rows.append({
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"_orig_idx": r.name,
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"Ген": t["gene"],
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"HGVS_c": hgvs_c,
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"HGVS_p": hgvs_p,
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"HGVS": f"{hgvs_c} {hgvs_p} ({hgvs_g})",
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"Тип варианта и эффект": effect,
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})
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if all_transcripts:
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for t in hits:
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hgvs_c = f"{t['tx']}:c.{pos1}{ref}>{alt}"
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hgvs_p = "p.(?)"
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effect = t["biotype"] or "transcript_variant"
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rows.append({
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"_orig_idx": r.name,
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"Ген": t["gene"],
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"HGVS_c": hgvs_c,
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"HGVS_p": hgvs_p,
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"HGVS": f"{hgvs_c} {hgvs_p} ({hgvs_g})",
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"Тип варианта и эффект": effect,
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})
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continue
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# pick best transcript (MANE > appris1 > canonical > basic > protein_coding)
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def _score(t):
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return (t["is_mane"], t["appris"] == 1, t["appris"] == 2, t["is_canonical"], t["is_basic"], t["is_ccds"], t["biotype"] == "protein_coding")
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best = max(hits, key=_score)
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hgvs_c = f"{best['tx']}:c.{pos1}{ref}>{alt}"
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hgvs_p = "p.(?)"
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effect = best["biotype"] or "transcript_variant"
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rows.append({
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"_orig_idx": r.name,
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"Ген": best["gene"],
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"HGVS_c": hgvs_c,
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"HGVS_p": hgvs_p,
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"HGVS": f"{hgvs_c} {hgvs_p} ({hgvs_g})",
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"Тип варианта и эффект": effect,
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})
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continue
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rows.append({
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"_orig_idx": r.name,
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@@ -125,7 +139,7 @@ def vep_annotate(df, reference, gtf_path=None, offline=False):
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"Тип варианта и эффект": "SNV, missense_variant (predicted)" if len(ref)==1 and len(alt)==1 else "indel",
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})
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continue
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# Online: Ensembl REST, expand all transcripts
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# Online: Ensembl REST
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try:
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hgvs_ens = f"{chrom.replace('chr','')}:g.{pos1}{ref}>{alt}"
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url = f"https://rest.ensembl.org/vep/homo_sapiens/hgvs/{hgvs_ens}?content-type=application/json"
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@@ -134,24 +148,46 @@ def vep_annotate(df, reference, gtf_path=None, offline=False):
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j = resp.json()
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tcs = j[0].get("transcript_consequences", []) if j and isinstance(j, list) and j[0] else []
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if tcs:
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for tc in tcs:
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gene = tc.get("gene_symbol") or "intergenic"
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hgvs_c = tc.get("hgvsc") or f"c.{pos1}{ref}>{alt}"
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hgvs_p = tc.get("hgvsp") or "p.(?)"
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cons = tc.get("consequence_terms", [])
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effect = ", ".join(cons) if cons else ("SNV, missense_variant (predicted)" if len(ref)==1 and len(alt)==1 else "indel")
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if ":" in hgvs_c:
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hgvs_c = hgvs_c.split(":")[-1]
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if ":" in hgvs_p:
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hgvs_p = hgvs_p.split(":")[-1]
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rows.append({
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"_orig_idx": r.name,
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"Ген": gene,
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"HGVS_c": hgvs_c,
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"HGVS_p": hgvs_p,
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"HGVS": f"{hgvs_c} {hgvs_p} ({hgvs_g})",
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"Тип варианта и эффект": effect,
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})
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if all_transcripts:
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for tc in tcs:
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gene = tc.get("gene_symbol") or "intergenic"
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hgvs_c = tc.get("hgvsc") or f"c.{pos1}{ref}>{alt}"
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hgvs_p = tc.get("hgvsp") or "p.(?)"
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cons = tc.get("consequence_terms", [])
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effect = ", ".join(cons) if cons else ("SNV, missense_variant (predicted)" if len(ref)==1 and len(alt)==1 else "indel")
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if ":" in hgvs_c:
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hgvs_c = hgvs_c.split(":")[-1]
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if ":" in hgvs_p:
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hgvs_p = hgvs_p.split(":")[-1]
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rows.append({
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"_orig_idx": r.name,
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"Ген": gene,
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"HGVS_c": hgvs_c,
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"HGVS_p": hgvs_p,
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"HGVS": f"{hgvs_c} {hgvs_p} ({hgvs_g})",
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"Тип варианта и эффект": effect,
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})
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continue
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def _score_tc(tc):
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return (tc.get("mane_select") is not None, tc.get("canonical") == 1, tc.get("biotype") == "protein_coding", tc.get("impact") == "HIGH")
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tc = max(tcs, key=_score_tc)
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gene = tc.get("gene_symbol") or "intergenic"
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hgvs_c = tc.get("hgvsc") or f"c.{pos1}{ref}>{alt}"
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hgvs_p = tc.get("hgvsp") or "p.(?)"
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cons = tc.get("consequence_terms", [])
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effect = ", ".join(cons) if cons else ("SNV, missense_variant (predicted)" if len(ref)==1 and len(alt)==1 else "indel")
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if ":" in hgvs_c:
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hgvs_c = hgvs_c.split(":")[-1]
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if ":" in hgvs_p:
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hgvs_p = hgvs_p.split(":")[-1]
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rows.append({
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"_orig_idx": r.name,
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"Ген": gene,
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"HGVS_c": hgvs_c,
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"HGVS_p": hgvs_p,
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"HGVS": f"{hgvs_c} {hgvs_p} ({hgvs_g})",
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"Тип варианта и эффект": effect,
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})
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continue
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except Exception:
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pass
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@@ -295,6 +331,7 @@ def build_excel(df_clean, df_annot, out_path):
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def _load_gtf(gtf_path):
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import gzip
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import re
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transcripts = []
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try:
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opener = gzip.open if str(gtf_path).endswith(".gz") else open
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@@ -308,13 +345,11 @@ def _load_gtf(gtf_path):
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chrom = parts[0]
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start = int(parts[3]); end = int(parts[4])
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attr = parts[8]
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# parse gene_name, transcript_id
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import re
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m_gene = re.search(r'gene_name "([^"]+)"', attr)
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m_tx = re.search(r'transcript_id "([^"]+)"', attr)
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m_biotype = re.search(r'transcript_type "([^"]+)"', attr)
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if m_gene and m_tx:
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# Normalize chr prefix: GTF uses chr1, our clean uses chr1 -> keep as is
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tags = set(re.findall(r'tag "([^"]+)"', attr))
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transcripts.append({
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"chrom": chrom,
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"start": start,
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@@ -322,6 +357,11 @@ def _load_gtf(gtf_path):
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"gene": m_gene.group(1),
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"tx": m_tx.group(1),
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"biotype": m_biotype.group(1) if m_biotype else "",
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"is_mane": "MANE_Select" in tags,
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"is_canonical": "Ensembl_canonical" in tags,
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"is_basic": "basic" in tags,
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"is_ccds": "CCDS" in tags,
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"appris": next((int(t.split("_")[-1]) for t in tags if t.startswith("appris_principal_")), 99),
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})
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except Exception as e:
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print(f"[gtf] failed to load {gtf_path}: {e}", file=sys.stderr)
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@@ -337,10 +377,11 @@ def main():
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ap.add_argument("--tumor-type", default="All Solid Tumors", help="OncoKB tumor type (pan-cancer default)")
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ap.add_argument("--token", default="~/.config/oncokb/token", help="OncoKB token file or env")
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ap.add_argument("--offline", action="store_true", help="skip Ensembl/OncoKB network calls (fast, offline)")
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ap.add_argument("--all-transcripts", action="store_true", help="expand all transcripts (default: pick best MANE/canonical per variant)")
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ap.add_argument("--min-vaf", type=float, default=None, help="filter VAF > threshold (e.g. 0.10 for 10%%)")
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ap.add_argument("--min-depth", type=int, default=None, help="filter depth >= threshold")
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ap.add_argument("--gtf", default="~/Projects/ffpe_damage/references/gencode.v44.annotation.gtf.gz",
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help="GENCODE GTF for offline all-transcript expansion")
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help="GENCODE GTF for offline expansion (pick best when not --all-transcripts)")
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args = ap.parse_args()
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clean_path = Path(args.clean)
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@@ -365,7 +406,7 @@ def main():
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print(f"[warn] {len(df_clean)} variants - network annotation will be slow. Use --offline for fast placeholder.", flush=True)
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# HGVS / gene / effect
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df_annot = vep_annotate(df_clean, args.reference, gtf_path=args.gtf, offline=args.offline)
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df_annot = vep_annotate(df_clean, args.reference, gtf_path=args.gtf, offline=args.offline, all_transcripts=args.all_transcripts)
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# OncoKB
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token = load_token(args.token)
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