diff --git a/annotate_clean.py b/annotate_clean.py
index 1d4722c..61a3550 100644
--- a/annotate_clean.py
+++ b/annotate_clean.py
@@ -61,17 +61,23 @@ def vep_annotate(df, reference, gtf_path=None, offline=False, all_transcripts=Fa
     if vep_bin and gtf_path and Path(gtf_path).expanduser().is_file():
         pass
     gtf_transcripts = None
-    if offline and gtf_path:
+    gtf_by_chrom = None
+    if gtf_path:
         gtf_file = Path(gtf_path).expanduser()
         if gtf_file.is_file():
             print(f"  loading GTF {gtf_file} ...", flush=True)
             gtf_transcripts = _load_gtf(gtf_file)
             print(f"  GTF loaded: {len(gtf_transcripts)} transcripts", flush=True)
+            # Index by chrom for fast lookup
+            from collections import defaultdict as _dd
+            gtf_by_chrom = _dd(list)
+            for t in gtf_transcripts:
+                gtf_by_chrom[t["chrom"]].append(t)
     rows = []
     total = len(df)
     for idx, (_, r) in enumerate(df.iterrows()):
-        if idx % 500 == 0 and total > 500:
-            print(f"  annotate {idx}/{total} ...", flush=True)
+        if idx % 10 == 0:
+            print(f"  annotate {idx+1}/{total} ...", flush=True)
         chrom = str(r["chrom"])
         pos1 = int(r["position"]) + 1
         ref = str(r["ref"]); alt = str(r["alt"])
@@ -87,8 +93,8 @@ def vep_annotate(df, reference, gtf_path=None, offline=False, all_transcripts=Fa
                 "Тип варианта и эффект": "SNV, missense_variant (predicted)" if len(ref)==1 and len(alt)==1 else "indel",
             })
             continue
-        if offline and gtf_transcripts is not None:
-            hits = [t for t in gtf_transcripts if t["chrom"] == chrom and t["start"] <= pos1 <= t["end"]]
+        if gtf_by_chrom is not None:
+            hits = [t for t in gtf_by_chrom.get(chrom, []) if t["start"] <= pos1 <= t["end"]]
             if hits:
                 if all_transcripts:
                     for t in hits:
@@ -211,8 +217,8 @@ def fetch_oncokb(hgvs_g_list, token, tumor_type="All Solid Tumors", offline=Fals
     headers = {"Authorization": f"Bearer {token}"}
     out = {}
     for i, hgvs in enumerate(hgvs_g_list):
-        if i % 100 == 0 and len(hgvs_g_list) > 100:
-            print(f"  [oncokb] {i}/{len(hgvs_g_list)} ...", flush=True)
+        if i % 10 == 0:
+            print(f"  [oncokb] {i+1}/{len(hgvs_g_list)} {hgvs} ...", flush=True)
         try:
             # hgvs like "7:g.140753336A>T" -> genomicLocation "7,140753336,140753336,A,T"
             try:
@@ -472,10 +478,10 @@ def main():
                     help="ClinVar variant_summary.txt.gz for ACMG P/L (auto if exists)")
     args = ap.parse_args()
 
-    # VAF>20% default for online (pan-cancer), leave --min-vaf configurable, offline keeps None
+    # VAF>30% default for online (pan-cancer), leave --min-vaf configurable, offline keeps None
     if args.min_vaf is None and not args.offline:
-        args.min_vaf = 0.20
-        print(f"[info] online default --min-vaf 0.20 (use --min-vaf 0.05 to keep more)", flush=True)
+        args.min_vaf = 0.30
+        print(f"[info] online default --min-vaf 0.30 (use --min-vaf 0.05 to keep more)", flush=True)
 
     clean_path = Path(args.clean)
     df_clean = pd.read_csv(clean_path)