diff --git a/audits/2026-04-21_v21_fresh_install/logs/00_pre_nuke.txt b/audits/2026-04-21_v21_fresh_install/logs/00_pre_nuke.txt
new file mode 100644
index 0000000..6fa221d
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/logs/00_pre_nuke.txt
@@ -0,0 +1,23 @@
+=== Pre-nuke state (Tue Apr 21 17:15:48 EDT 2026) ===
+1.5G	/Users/lp698/.chorus/backgrounds/
+3.0G	genomes/
+
+/Users/lp698/.chorus/backgrounds/:
+total 3189032
+drwxr-xr-x@ 9 lp698  staff        288 Apr 21 09:22 .
+drwxr-xr-x@ 3 lp698  staff         96 Apr 21 09:22 ..
+drwxr-xr-x@ 3 lp698  staff         96 Apr 20 16:43 .cache
+-rw-r--r--@ 1 lp698  staff  275523684 Apr 20 16:55 alphagenome_pertrack.npz
+-rw-r--r--@ 1 lp698  staff  803275951 Apr 20 17:53 borzoi_pertrack.npz
+-rw-r--r--@ 1 lp698  staff    2541952 Apr 20 16:55 chrombpnet_pertrack.npz
+-rw-r--r--@ 1 lp698  staff  548316718 Apr 20 16:55 enformer_pertrack.npz
+-rw-r--r--@ 1 lp698  staff     212800 Apr 21 09:22 legnet_pertrack.npz
+-rw-r--r--@ 1 lp698  staff    2903709 Apr 21 09:22 sei_pertrack.npz
+
+genomes/:
+total 6393568
+drwxr-xr-x@  5 lp698  staff         160 Apr 15 00:31 .
+drwxr-xr-x@ 29 lp698  staff         928 Apr 21 15:09 ..
+-rw-r--r--@  1 lp698  staff         170 Apr 14 23:18 .gitkeep
+-rw-r--r--@  1 lp698  staff  3273481150 Apr 15 00:15 hg38.fa
+-rw-r--r--@  1 lp698  staff       19381 Apr 15 00:31 hg38.fa.fai
diff --git a/audits/2026-04-21_v21_fresh_install/logs/01_post_nuke.txt b/audits/2026-04-21_v21_fresh_install/logs/01_post_nuke.txt
new file mode 100644
index 0000000..e7f1c3f
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/logs/01_post_nuke.txt
@@ -0,0 +1,3 @@
+=== Post-nuke state ===
+  0B	/Users/lp698/.chorus/
+4.0K	genomes/
diff --git a/audits/2026-04-21_v21_fresh_install/logs/02_cli.txt b/audits/2026-04-21_v21_fresh_install/logs/02_cli.txt
new file mode 100644
index 0000000..297e437
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/logs/02_cli.txt
@@ -0,0 +1,18 @@
+=== §1 CLI availability ===
+/Users/lp698/.local/share/mamba/envs/chorus/bin/chorus
+usage: chorus [-h] {setup,list,validate,remove,health,genome} ...
+
+Chorus: Modular framework for genomic foundation models
+
+positional arguments:
+  {setup,list,validate,remove,health,genome}
+                        Available commands
+    setup               Set up oracle environments
+    list                List available environments
+    validate            Validate environments
+    remove              Remove an environment
+    health              Check environment health
+    genome              Manage reference genomes
+
+options:
+  -h, --help            show this help message and exit
diff --git a/audits/2026-04-21_v21_fresh_install/logs/03_genome_download.txt b/audits/2026-04-21_v21_fresh_install/logs/03_genome_download.txt
new file mode 100644
index 0000000..3bcbb7d
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/logs/03_genome_download.txt
@@ -0,0 +1,16 @@
+2026-04-21 17:16:14,341 - chorus.cli.main - INFO - Downloading hg38...
+2026-04-21 17:16:14,341 - chorus.utils.genome - INFO - Downloading hg38 from https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz...
+2026-04-21 17:16:14,341 - chorus.utils.genome - INFO - This may take several minutes depending on your connection speed...
+2026-04-21 17:17:16,249 - chorus.utils.http - INFO -   hg38 genome: 0.10/0.98 GB (10.7%)
+2026-04-21 17:18:15,709 - chorus.utils.http - INFO -   hg38 genome: 0.21/0.98 GB (21.3%)
+2026-04-21 17:19:14,882 - chorus.utils.http - INFO -   hg38 genome: 0.31/0.98 GB (32.0%)
+2026-04-21 17:20:14,148 - chorus.utils.http - INFO -   hg38 genome: 0.42/0.98 GB (42.6%)
+2026-04-21 17:21:13,352 - chorus.utils.http - INFO -   hg38 genome: 0.52/0.98 GB (53.3%)
+2026-04-21 17:22:12,542 - chorus.utils.http - INFO -   hg38 genome: 0.63/0.98 GB (64.0%)
+2026-04-21 17:23:11,837 - chorus.utils.http - INFO -   hg38 genome: 0.73/0.98 GB (74.6%)
+2026-04-21 17:24:11,208 - chorus.utils.http - INFO -   hg38 genome: 0.84/0.98 GB (85.3%)
+2026-04-21 17:25:10,463 - chorus.utils.http - INFO -   hg38 genome: 0.94/0.98 GB (95.9%)
+2026-04-21 17:25:33,062 - chorus.utils.genome - INFO - Decompressing hg38...
+2026-04-21 17:25:40,589 - chorus.utils.genome - INFO - Creating FASTA index for hg38...
+2026-04-21 17:25:46,933 - chorus.utils.genome - INFO - Successfully downloaded hg38 to /Users/lp698/chorus_test/chorus/genomes/hg38.fa
+2026-04-21 17:25:46,933 - chorus.cli.main - INFO - Successfully downloaded hg38
diff --git a/audits/2026-04-21_v21_fresh_install/logs/04_cdf_fresh_download.txt b/audits/2026-04-21_v21_fresh_install/logs/04_cdf_fresh_download.txt
new file mode 100644
index 0000000..7e89f1f
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/logs/04_cdf_fresh_download.txt
@@ -0,0 +1,10 @@
+§4 CDFs — FRESH DOWNLOAD from HuggingFace (empty cache)
+oracle       |   secs | n_tracks | CDFs                  | mono | p50<=p95<=p99 | signed%
+--------------------------------------------------------------------------------------------------
+enformer     |  14.2s |     5313 | effect, summary, perbin | OK   | OK            |      0%
+borzoi       |  16.7s |     7611 | effect, summary, perbin | OK   | OK            |     20%
+chrombpnet   |   0.5s |       24 | effect, summary, perbin | OK   | OK            |      0%
+sei          |   0.5s |       40 | effect, summary       | OK   | OK            |    100%
+legnet       |   0.5s |        3 | effect, summary       | OK   | OK            |    100%
+alphagenome  |  11.4s |     5168 | effect, summary, perbin | OK   | OK            |     13%
+Total elapsed: 43.8s
diff --git a/audits/2026-04-21_v21_fresh_install/logs/05_device_probe.txt b/audits/2026-04-21_v21_fresh_install/logs/05_device_probe.txt
new file mode 100644
index 0000000..933e8ee
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/logs/05_device_probe.txt
@@ -0,0 +1,24 @@
+=== §3 device probe ===
+2026-04-21 17:16:40,980 - chorus.core.platform - INFO - Detected platform: Darwin arm64 (key=macos_arm64, cuda=False)
+base: macos_arm64 has_cuda= False
+
+== chorus-enformer ==
+tf devs: ['/physical_device:CPU:0', '/physical_device:GPU:0']
+
+== chorus-borzoi ==
+torch cuda: False mps: True
+
+== chorus-chrombpnet ==
+tf devs: ['/physical_device:CPU:0', '/physical_device:GPU:0']
+
+== chorus-sei ==
+torch cuda: False mps: True
+
+== chorus-legnet ==
+torch cuda: False mps: True
+
+== chorus-alphagenome ==
+tf devs: ['/physical_device:CPU:0']
+jax devs: ['METAL:0']
+
+
diff --git a/audits/2026-04-21_v21_fresh_install/logs/06_api_sanity.txt b/audits/2026-04-21_v21_fresh_install/logs/06_api_sanity.txt
new file mode 100644
index 0000000..be7f728
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/logs/06_api_sanity.txt
@@ -0,0 +1,14 @@
+oracle       | seq_length vs spec
+---------------------------------------------
+enformer     |  393216 (393216) ✓
+borzoi       |  524288 (524288) ✓
+chrombpnet   |    2114 (2114) ✓
+sei          |    4096 (4096) ✓
+legnet       |     200 (200) ✓
+alphagenome  | 1048576 (1048576) ✓
+
+-- invalid name --
+ValueError: Unknown oracle: bogus. Available: ['enformer', 'borzoi', 'chrombpnet', 'sei', 'legnet', 'alphagenome']
+
+-- §14.4 chrZZ validation --
+OSError: file `/tmp/nonexistent.fa` not found
diff --git a/audits/2026-04-21_v21_fresh_install/logs/07_consistency.txt b/audits/2026-04-21_v21_fresh_install/logs/07_consistency.txt
new file mode 100644
index 0000000..e624c0f
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/logs/07_consistency.txt
@@ -0,0 +1,9 @@
+=== §10 canonical-number drift in live code ===
+
+=== §10 old examples/applications/ path in live docs ===
+
+=== §15 runtime CDN fetches in shipped HTML ===
+(if empty: reports are offline-safe)
+
+=== §16 leaked HF tokens / AWS keys ===
+(if empty: no secrets committed)
diff --git a/audits/2026-04-21_v21_fresh_install/logs/08_selenium.txt b/audits/2026-04-21_v21_fresh_install/logs/08_selenium.txt
new file mode 100644
index 0000000..13a2883
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/logs/08_selenium.txt
@@ -0,0 +1,21 @@
+Rendering 18 HTML reports with full JS (IGV loads)
+  ✓ batch_sort1_locus_scoring (0 JS errs)
+  ✓ rs12740374_SORT1_locus_causal_report (0 JS errs)
+  ✓ chr1_109274968_G_T_SORT1_alphagenome_amniotic_epithelial_cell_report (0 JS errs)
+  ✓ chr1_109274968_G_T_SORT1_alphagenome_fibroblast_of_mammary_gland_report (0 JS errs)
+  ✓ chr1_109274968_G_T_SORT1_alphagenome_fibroblast_of_pulmonary_artery_report (0 JS errs)
+  ✓ integration_CMV_PPP1R12C_report (0 JS errs)
+  ✓ region_swap_SORT1_K562_report (0 JS errs)
+  ✓ rs12740374_SORT1_alphagenome_report (0 JS errs)
+  ✓ rs12740374_SORT1_chrombpnet_report (0 JS errs)
+  ✓ rs12740374_SORT1_legnet_report (0 JS errs)
+  ✓ rs12740374_SORT1_multioracle_report (0 JS errs)
+  ✓ rs12740374_SORT1_CEBP_validation_report (0 JS errs)
+  ✓ chr5_1295046_T_G_TERT_alphagenome_report (0 JS errs)
+  ✓ rs1427407_BCL11A_alphagenome_report (0 JS errs)
+  ✓ rs1421085_FTO_alphagenome_report (0 JS errs)
+  ✓ rs12740374_SORT1_chrombpnet_report (0 JS errs)
+  ✓ rs12740374_SORT1_enformer_report (0 JS errs)
+  ✓ rs12740374_SORT1_alphagenome_report (0 JS errs)
+
+Total JS errors across 18 reports: 0
diff --git a/audits/2026-04-21_v21_fresh_install/logs/09_quickstart_fresh.ipynb b/audits/2026-04-21_v21_fresh_install/logs/09_quickstart_fresh.ipynb
new file mode 100644
index 0000000..155ed37
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/logs/09_quickstart_fresh.ipynb
@@ -0,0 +1,2410 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Single Oracle Quick Start: GATA1 Regulatory Analysis with Enformer\n",
+    "\n",
+    "This notebook demonstrates all major features of the Chorus library using the Enformer oracle to analyze the GATA1 transcription start site (TSS) region.\n",
+    "\n",
+    "GATA1 is an essential transcription factor for red blood cell development, making it an interesting target for regulatory analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation Instructions (Reference)\n",
+    "\n",
+    "If you haven't installed Chorus yet, follow these steps:\n",
+    "\n",
+    "```bash\n",
+    "# 1. Clone the repository\n",
+    "git clone https://github.com/pinellolab/chorus.git\n",
+    "cd chorus\n",
+    "\n",
+    "# 2. Create the main Chorus environment\n",
+    "mamba env create -f environment.yml\n",
+    "mamba activate chorus\n",
+    "\n",
+    "# 3. Install Chorus package\n",
+    "pip install -e .\n",
+    "\n",
+    "# 4. Set up the Enformer environment\n",
+    "chorus setup --oracle enformer\n",
+    "\n",
+    "# 5. Download the reference genome\n",
+    "chorus genome download hg38\n",
+    "```\n",
+    "\n",
+    "**Note**: \n",
+    "- For this notebook, we assume Chorus is already installed.\n",
+    "- pyGenomeTracks is now included in the environment for advanced visualization!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup and Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:27:41.552306Z",
+     "iopub.status.busy": "2026-04-21T21:27:41.551955Z",
+     "iopub.status.idle": "2026-04-21T21:27:42.775154Z",
+     "shell.execute_reply": "2026-04-21T21:27:42.774719Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chorus version: 0.1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import required libraries\n",
+    "import chorus\n",
+    "from chorus.utils import get_genome, extract_sequence, download_gencode\n",
+    "from chorus.utils.visualization import visualize_chorus_predictions\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Set up matplotlib for inline display\n",
+    "import matplotlib\n",
+    "matplotlib.use('Agg')  # Use non-interactive backend\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Set up plotting with white background\n",
+    "%matplotlib inline\n",
+    "plt.style.use('default')\n",
+    "plt.rcParams['figure.facecolor'] = 'white'\n",
+    "plt.rcParams['axes.facecolor'] = 'white'\n",
+    "plt.rcParams['figure.dpi'] = 100  # Increase DPI for better quality\n",
+    "\n",
+    "print(\"Chorus version:\", chorus.__version__ if hasattr(chorus, '__version__') else \"development\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuration Options\n",
+    "\n",
+    "### Device Selection\n",
+    "\n",
+    "By default, Chorus auto-detects and uses GPU if available. You can control device selection:\n",
+    "\n",
+    "```python\n",
+    "# Force CPU usage\n",
+    "oracle = chorus.create_oracle('enformer', device='cpu')\n",
+    "\n",
+    "# Use specific GPU (for multi-GPU systems)\n",
+    "oracle = chorus.create_oracle('enformer', device='cuda:1')  # Use second GPU\n",
+    "\n",
+    "# Set default via environment variable\n",
+    "# export CHORUS_DEVICE=cpu\n",
+    "```\n",
+    "\n",
+    "### Timeout Configuration\n",
+    "\n",
+    "Chorus has built-in timeouts to prevent hanging:\n",
+    "- **Model loading**: 10 minutes (600s) by default\n",
+    "- **Predictions**: 5 minutes (300s) by default\n",
+    "\n",
+    "For slower systems (CPU-only, slow network, etc.), you can:\n",
+    "\n",
+    "1. **Increase timeouts** when creating the oracle:\n",
+    "   ```python\n",
+    "   oracle = chorus.create_oracle('enformer', \n",
+    "                                use_environment=True,\n",
+    "                                model_load_timeout=1200,  # 20 minutes\n",
+    "                                predict_timeout=600)      # 10 minutes\n",
+    "   ```\n",
+    "\n",
+    "2. **Combine device and timeout settings**:\n",
+    "   ```python\n",
+    "   oracle = chorus.create_oracle('enformer',\n",
+    "                                use_environment=True,\n",
+    "                                device='cpu',             # Force CPU\n",
+    "                                model_load_timeout=1800,  # 30 min for CPU\n",
+    "                                predict_timeout=900)      # 15 min for CPU\n",
+    "   ```\n",
+    "\n",
+    "3. **Disable timeouts globally**:\n",
+    "   ```bash\n",
+    "   export CHORUS_NO_TIMEOUT=1\n",
+    "   ```\n",
+    "\n",
+    "4. **Disable timeouts for specific oracle**:\n",
+    "   ```python\n",
+    "   oracle = chorus.create_oracle('enformer',\n",
+    "                                use_environment=True,\n",
+    "                                model_load_timeout=None,\n",
+    "                                predict_timeout=None)\n",
+    "   ```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Setting up Reference Genome and Enformer Oracle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:27:42.790972Z",
+     "iopub.status.busy": "2026-04-21T21:27:42.790835Z",
+     "iopub.status.idle": "2026-04-21T21:27:42.793111Z",
+     "shell.execute_reply": "2026-04-21T21:27:42.792746Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:27:42,791 - chorus.utils.annotations - INFO - Annotation file already exists: /Users/lp698/chorus_test/chorus/annotations/gencode.v48.basic.annotation.gtf\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Setting up gene annotations...\n",
+      "Using annotations: /Users/lp698/chorus_test/chorus/annotations/gencode.v48.basic.annotation.gtf\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download gene annotations\n",
+    "print(\"\\nSetting up gene annotations...\")\n",
+    "gtf_path = download_gencode(version='v48', annotation_type='basic')\n",
+    "print(f\"Using annotations: {gtf_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:27:42.794442Z",
+     "iopub.status.busy": "2026-04-21T21:27:42.794351Z",
+     "iopub.status.idle": "2026-04-21T21:27:52.814776Z",
+     "shell.execute_reply": "2026-04-21T21:27:52.814244Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:27:42,795 - chorus.core.base - INFO - Device: auto-detect (GPU if available, else CPU)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:27:42,809 - chorus.core.environment.manager - INFO - Found mamba via MAMBA_EXE: /Users/lp698/miniforge3/bin/mamba\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:27:42,809 - chorus.core.platform - INFO - Detected platform: Darwin arm64 (key=macos_arm64, cuda=False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Setting up reference genome...\n",
+      "Using genome: /Users/lp698/chorus_test/chorus/genomes/hg38.fa\n",
+      "\n",
+      "Creating Enformer oracle...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:27:48,094 - chorus.core.base - INFO - Using conda environment: chorus-enformer\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:27:48,095 - chorus.oracles.enformer - INFO - Loading Enformer model from https://tfhub.dev/deepmind/enformer/1...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Loading pre-trained Enformer model...\n",
+      "Note: First-time loading downloads ~1GB model and may take 5-10 minutes\n",
+      "To force CPU usage: device='cpu'\n",
+      "To disable timeout: export CHORUS_NO_TIMEOUT=1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:27:52,812 - chorus.oracles.enformer - INFO - Enformer model loaded successfully in environment!\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model loaded successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get reference genome (auto-downloads if not present)\n",
+    "print(\"Setting up reference genome...\")\n",
+    "genome_path = get_genome('hg38')\n",
+    "print(f\"Using genome: {genome_path}\")\n",
+    "\n",
+    "# Create Enformer oracle with environment isolation\n",
+    "print(\"\\nCreating Enformer oracle...\")\n",
+    "# Default: auto-detect GPU, 10 min model loading timeout, 5 min prediction timeout\n",
+    "# For different configurations:\n",
+    "# - Force CPU: device='cpu'\n",
+    "# - Use specific GPU: device='cuda:1'\n",
+    "# - Increase timeouts: model_load_timeout=1200, predict_timeout=600\n",
+    "oracle = chorus.create_oracle(\n",
+    "    'enformer', \n",
+    "    use_environment=True,\n",
+    "    reference_fasta=str(genome_path)\n",
+    ")\n",
+    "\n",
+    "# Load pre-trained model\n",
+    "print(\"\\nLoading pre-trained Enformer model...\")\n",
+    "print(\"Note: First-time loading downloads ~1GB model and may take 5-10 minutes\")\n",
+    "print(\"To force CPU usage: device='cpu'\")\n",
+    "print(\"To disable timeout: export CHORUS_NO_TIMEOUT=1\")\n",
+    "oracle.load_pretrained_model()\n",
+    "print(\"Model loaded successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Exploring Available Tracks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:27:52.816304Z",
+     "iopub.status.busy": "2026-04-21T21:27:52.816203Z",
+     "iopub.status.idle": "2026-04-21T21:27:52.910979Z",
+     "shell.execute_reply": "2026-04-21T21:27:52.910627Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:27:52,900 - chorus.oracles.enformer_source.enformer_metadata - INFO - Loaded 5313 track metadata entries\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Available assay types (4):\n",
+      "   1. ATAC\n",
+      "   2. CAGE\n",
+      "   3. CHIP\n",
+      "   4. DNASE\n",
+      "\n",
+      "==================================================\n",
+      "\n",
+      "Available cell types (1267):\n",
+      "   1. 3xFLAG-AHR\n",
+      "   2. 3xFLAG-ARID4B\n",
+      "   3. 3xFLAG-ATF1\n",
+      "   4. 3xFLAG-ATF4\n",
+      "   5. 3xFLAG-BCL6\n",
+      "   6. 3xFLAG-CEBPA\n",
+      "   7. 3xFLAG-CEBPG\n",
+      "   8. 3xFLAG-CREB1\n",
+      "   9. 3xFLAG-DMAP1\n",
+      "  10. 3xFLAG-DNMT3B\n",
+      "  11. 3xFLAG-DRAP1\n",
+      "  12. 3xFLAG-ELF3\n",
+      "  13. 3xFLAG-ERF\n",
+      "  14. 3xFLAG-ETV5\n",
+      "  15. 3xFLAG-FOXA3\n",
+      "  16. 3xFLAG-FOXP1\n",
+      "  17. 3xFLAG-GABPA\n",
+      "  18. 3xFLAG-GABPB1\n",
+      "  19. 3xFLAG-GATAD1\n",
+      "  20. 3xFLAG-GATAD2A\n",
+      "  ... and 1247 more\n",
+      "\n",
+      "==================================================\n",
+      "\n",
+      "Track summary by assay type:\n",
+      "  ATAC: 10 tracks\n",
+      "  CAGE: 638 tracks\n",
+      "  CHIP: 3991 tracks\n",
+      "  DNASE: 674 tracks\n"
+     ]
+    }
+   ],
+   "source": [
+    "# List available assay types\n",
+    "assay_types = oracle.list_assay_types()\n",
+    "print(f\"Available assay types ({len(assay_types)}):\")\n",
+    "for i, assay in enumerate(assay_types, 1):\n",
+    "    print(f\"  {i:2d}. {assay}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*50 + \"\\n\")\n",
+    "\n",
+    "# List available cell types (show first 20 due to large number)\n",
+    "cell_types = oracle.list_cell_types()\n",
+    "print(f\"Available cell types ({len(cell_types)}):\")\n",
+    "for i, cell in enumerate(cell_types[:20], 1):\n",
+    "    print(f\"  {i:2d}. {cell}\")\n",
+    "print(f\"  ... and {len(cell_types) - 20} more\")\n",
+    "\n",
+    "# Get track summary\n",
+    "print(\"\\n\" + \"=\"*50 + \"\\n\")\n",
+    "track_summary = oracle.get_track_info()\n",
+    "print(\"Track summary by assay type:\")\n",
+    "for assay, count in track_summary.items():\n",
+    "    print(f\"  {assay}: {count} tracks\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:27:52.912753Z",
+     "iopub.status.busy": "2026-04-21T21:27:52.912642Z",
+     "iopub.status.idle": "2026-04-21T21:27:52.921275Z",
+     "shell.execute_reply": "2026-04-21T21:27:52.920887Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K562 tracks available:\n",
+      "\n",
+      "DNASE:K562 - 7 tracks found\n",
+      "First 3:\n",
+      "  - ENCFF899YDP: DNASE:K562 treated with 1 uM vorinostat for 72 hours\n",
+      "  - ENCFF515UNC: DNASE:K562 G2 phase\n",
+      "  - ENCFF708UIS: DNASE:K562 G1 phase\n",
+      "\n",
+      "CAGE with K562 - 2 tracks found\n",
+      "All CAGE K562 tracks:\n",
+      "  - CNhs11250: CAGE:chronic myelogenous leukemia cell line:K562\n",
+      "  - CNhs12336: CAGE:chronic myelogenous leukemia cell line:K562 ENCODE, biol_\n",
+      "\n",
+      "==================================================\n",
+      "\n",
+      "For this analysis, we'll use these specific track IDs:\n",
+      "  - ENCFF413AHU (DNASE:K562)\n",
+      "  - CNhs11250 (CAGE:chronic myelogenous leukemia cell line:K562)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check our target tracks\n",
+    "print(\"K562 tracks available:\")\n",
+    "\n",
+    "# Search for K562 tracks\n",
+    "dnase_k562 = oracle.get_track_info(\"DNASE:K562\")\n",
+    "cage_k562 = oracle.get_track_info(\"CAGE:.*K562\")\n",
+    "\n",
+    "print(f\"\\nDNASE:K562 - {len(dnase_k562)} tracks found\")\n",
+    "if len(dnase_k562) > 0:\n",
+    "    print(\"First 3:\")\n",
+    "    for idx, row in dnase_k562.head(3).iterrows():\n",
+    "        print(f\"  - {row['identifier']}: {row['description']}\")\n",
+    "\n",
+    "print(f\"\\nCAGE with K562 - {len(cage_k562)} tracks found\")\n",
+    "if len(cage_k562) > 0:\n",
+    "    print(\"All CAGE K562 tracks:\")\n",
+    "    for idx, row in cage_k562.iterrows():\n",
+    "        print(f\"  - {row['identifier']}: {row['description']}\")\n",
+    "\n",
+    "# Define the specific track IDs we'll use\n",
+    "print(\"\\n\" + \"=\"*50)\n",
+    "print(\"\\nFor this analysis, we'll use these specific track IDs:\")\n",
+    "track_ids = ['ENCFF413AHU', 'CNhs11250']\n",
+    "print(f\"  - {track_ids[0]} (DNASE:K562)\")\n",
+    "print(f\"  - {track_ids[1]} (CAGE:chronic myelogenous leukemia cell line:K562)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Helper Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:27:52.922762Z",
+     "iopub.status.busy": "2026-04-21T21:27:52.922662Z",
+     "iopub.status.idle": "2026-04-21T21:27:52.925386Z",
+     "shell.execute_reply": "2026-04-21T21:27:52.924935Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# No need for custom save_predictions_as_bedgraph - use oracle's method\n",
+    "\n",
+    "def plot_track_comparison(tracks_dict, title=\"Track Comparison\", window=None):\n",
+    "    \"\"\"Plot multiple tracks for comparison using improved visualization.\"\"\"\n",
+    "    # Convert to list format for visualization function\n",
+    "    track_ids = list(tracks_dict.keys())\n",
+    "    \n",
+    "    # Create a temporary figure to get the data\n",
+    "    import tempfile\n",
+    "    with tempfile.NamedTemporaryFile(suffix='.png', delete=True) as tmp:\n",
+    "        visualize_chorus_predictions(\n",
+    "            predictions=tracks_dict,\n",
+    "            chrom='chrX',\n",
+    "            start=0,  # Will be adjusted by window\n",
+    "            track_ids=track_ids,\n",
+    "            output_file=None,  # Display inline\n",
+    "            bin_size=1  # Since we're using pre-binned data\n",
+    "        )\n",
+    "    \n",
+    "    return plt.gcf()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example A: Wild-type Sequence Prediction\n",
+    "\n",
+    "First, let's analyze the wild-type GATA1 TSS region on chromosome X."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:27:52.926591Z",
+     "iopub.status.busy": "2026-04-21T21:27:52.926499Z",
+     "iopub.status.idle": "2026-04-21T21:27:52.929340Z",
+     "shell.execute_reply": "2026-04-21T21:27:52.928966Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Analyzing wild-type region: chrX:48777634-48790694\n",
+      "This region contains the GATA1 transcription start site\n",
+      "\n",
+      "Region length: 13,061 bp\n",
+      "GC content: 47.2%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define GATA1 TSS region\n",
+    "gata1_region = \"chrX:48777634-48790694\"\n",
+    "print(f\"Analyzing wild-type region: {gata1_region}\")\n",
+    "print(\"This region contains the GATA1 transcription start site\\n\")\n",
+    "\n",
+    "# Extract sequence information\n",
+    "wt_seq = extract_sequence(gata1_region, str(genome_path))\n",
+    "print(f\"Region length: {len(wt_seq):,} bp\")\n",
+    "print(f\"GC content: {(wt_seq.count('G') + wt_seq.count('C')) / len(wt_seq) * 100:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:27:52.930551Z",
+     "iopub.status.busy": "2026-04-21T21:27:52.930465Z",
+     "iopub.status.idle": "2026-04-21T21:28:00.003536Z",
+     "shell.execute_reply": "2026-04-21T21:28:00.002863Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Making predictions using specific track identifiers...\n",
+      "Track IDs: ['ENCFF413AHU', 'CNhs11250']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions for wild-type sequence\n",
+    "print(\"Making predictions using specific track identifiers...\")\n",
+    "print(f\"Track IDs: {track_ids}\")\n",
+    "\n",
+    "wt_results = oracle.predict(\n",
+    "    ('chrX', 48777634, 48790694),\n",
+    "    track_ids  # Using specific track IDs\n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:00.005859Z",
+     "iopub.status.busy": "2026-04-21T21:28:00.005718Z",
+     "iopub.status.idle": "2026-04-21T21:28:00.009694Z",
+     "shell.execute_reply": "2026-04-21T21:28:00.009098Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Interval(reference=GenomeRef(chrom='chrX', start=48777634, end=48790694), cigar=[CigarEqual(length=13060)]),\n",
+       " 13060)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wt_results['ENCFF413AHU'].query_interval, len(wt_results['ENCFF413AHU'].query_interval)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:00.011234Z",
+     "iopub.status.busy": "2026-04-21T21:28:00.011138Z",
+     "iopub.status.idle": "2026-04-21T21:28:00.014246Z",
+     "shell.execute_reply": "2026-04-21T21:28:00.013619Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "ENCFF413AHU:\n",
+      "  Shape: (896,)\n",
+      "  Mean signal: 0.4842\n",
+      "  Max signal: 22.4620\n",
+      "  Signal at TSS region (bins 40-50): 0.0375\n",
+      "\n",
+      "CNhs11250:\n",
+      "  Shape: (896,)\n",
+      "  Mean signal: 0.5957\n",
+      "  Max signal: 120.8120\n",
+      "  Signal at TSS region (bins 40-50): 0.0233\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print statistics\n",
+    "for track_id, prediction in wt_results.items():\n",
+    "    print(f\"\\n{track_id}:\")\n",
+    "    print(f\"  Shape: {prediction.values.shape}\")\n",
+    "    print(f\"  Mean signal: {np.mean(prediction.values):.4f}\")\n",
+    "    print(f\"  Max signal: {np.max(prediction.values):.4f}\")\n",
+    "    print(f\"  Signal at TSS region (bins 40-50): {np.mean(prediction.values[40:50]):.4f}\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Activity Percentiles\n",
+    "\n",
+    "When background distributions are available (built by `scripts/build_backgrounds_*.py`),\n",
+    "predictions include **activity percentiles** showing where each track's signal ranks\n",
+    "genome-wide. This adds biological context to raw prediction values.\n",
+    "\n",
+    "The MCP `predict()` tool returns `activity_percentile` for each track automatically.\n",
+    "In the Python API, use `to_percentile()` to convert predictions:\n",
+    "\n",
+    "```python\n",
+    "from chorus.analysis.normalization import get_normalizer\n",
+    "\n",
+    "normalizer = get_normalizer('enformer')\n",
+    "if normalizer:\n",
+    "    pct_results = wt_results.to_percentile(normalizer, 'enformer')\n",
+    "    # All track values now in [0, 1] — directly comparable across tracks\n",
+    "```\n",
+    "\n",
+    "A percentile of **0.95** means the predicted signal is higher than 95% of\n",
+    "genomic positions — e.g., this region is in a highly accessible chromatin state\n",
+    "or has very strong TF binding.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:00.015924Z",
+     "iopub.status.busy": "2026-04-21T21:28:00.015785Z",
+     "iopub.status.idle": "2026-04-21T21:28:02.129259Z",
+     "shell.execute_reply": "2026-04-21T21:28:02.128764Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:28:02,126 - chorus.analysis.normalization - INFO - Loaded per-track CDFs for 'enformer': 5313 tracks, CDFs: effect_cdfs, summary_cdfs, perbin_cdfs\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ENCFF413AHU: mean percentile = 0.814\n",
+      "CNhs11250: mean percentile = 0.704\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Activity percentiles: map raw predictions to genome-wide context\n",
+    "from chorus.analysis.normalization import get_normalizer\n",
+    "\n",
+    "normalizer = get_normalizer('enformer')\n",
+    "if normalizer:\n",
+    "    pct_results = wt_results.to_percentile(normalizer, 'enformer')\n",
+    "    for track_id, prediction in pct_results.items():\n",
+    "        print(f\"{track_id}: mean percentile = {np.mean(prediction.values):.3f}\")\n",
+    "else:\n",
+    "    print(\"No background distributions found. Run scripts/build_backgrounds_enformer.py first.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:02.130728Z",
+     "iopub.status.busy": "2026-04-21T21:28:02.130619Z",
+     "iopub.status.idle": "2026-04-21T21:28:02.133242Z",
+     "shell.execute_reply": "2026-04-21T21:28:02.132744Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'chrX:48726820-48841508'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction.prediction_interval.to_string()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:02.134515Z",
+     "iopub.status.busy": "2026-04-21T21:28:02.134421Z",
+     "iopub.status.idle": "2026-04-21T21:28:02.647903Z",
+     "shell.execute_reply": "2026-04-21T21:28:02.647500Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from chorus.utils import make_gene_track\n",
+    "import coolbox\n",
+    "from coolbox.api import *\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:02.649583Z",
+     "iopub.status.busy": "2026-04-21T21:28:02.649418Z",
+     "iopub.status.idle": "2026-04-21T21:28:02.921762Z",
+     "shell.execute_reply": "2026-04-21T21:28:02.921331Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1574.8x728.346 with 12 Axes>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "preds = list(wt_results.values())\n",
+    "\n",
+    "frame = preds[0].get_coolbox_representation(add_xaxis=False) + Spacer(0.5) + preds[1].get_coolbox_representation()\n",
+    "    \n",
+    "frame.plot('chrX:48726820-48841508') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:02.923415Z",
+     "iopub.status.busy": "2026-04-21T21:28:02.923305Z",
+     "iopub.status.idle": "2026-04-21T21:28:02.932569Z",
+     "shell.execute_reply": "2026-04-21T21:28:02.932139Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Saving wild-type tracks...\n",
+      "Saved 2 files to bedgraph_outputs/\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save tracks using oracle method - Enformer handles coordinate mapping internally\n",
+    "print(\"\\nSaving wild-type tracks...\")\n",
+    "wt_files = wt_results.save_predictions_as_bedgraph(output_dir=\"bedgraph_outputs\",\n",
+    "                                                    prefix='a_wt')\n",
+    "\n",
+    "print(f\"Saved {len(wt_files)} files to bedgraph_outputs/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gene Expression Analysis\n",
+    "\n",
+    "Let's analyze the predicted GATA1 expression using CAGE signal at TSS positions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:02.933878Z",
+     "iopub.status.busy": "2026-04-21T21:28:02.933800Z",
+     "iopub.status.idle": "2026-04-21T21:28:05.264673Z",
+     "shell.execute_reply": "2026-04-21T21:28:05.264199Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:28:02,936 - chorus.utils.annotations - INFO - Annotation file already exists: /Users/lp698/chorus_test/chorus/annotations/gencode.v48.basic.annotation.gtf\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:28:02,937 - chorus.utils.annotations - INFO - Loading GTF features (transcript) from gencode.v48.basic.annotation.gtf (one-time)...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Getting TSS positions for GATA1...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-21 17:28:05,173 - chorus.utils.annotations - INFO - Cached 158367 transcript features from GTF\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Found 6 transcripts for GATA1:\n",
+      "  - ENST00000696450.1 (protein_coding) TSS at chrX:48786540 (+)\n",
+      "  - ENST00000651144.2 (protein_coding) TSS at chrX:48786562 (+)\n",
+      "  - ENST00000696451.1 (protein_coding) TSS at chrX:48786562 (+)\n",
+      "  - ENST00000696452.1 (protein_coding) TSS at chrX:48786562 (+)\n",
+      "  - ENST00000376665.4 (protein_coding) TSS at chrX:48786573 (+)\n",
+      "\n",
+      "Analyzing GATA1 expression from predictions...\n",
+      "\n",
+      "GATA1 Expression Analysis:\n",
+      "  Number of TSS in region: 6\n",
+      "  TSS positions in output window: [48786540, 48786562, 48786562, 48786562, 48786573, 48786590]\n",
+      "\n",
+      "  Predicted CAGE expression:\n",
+      "    CNhs11250: mean=120.81, max=120.81\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Analyze GATA1 gene expression using CAGE predictions\n",
+    "from chorus.utils import get_gene_tss\n",
+    "from chorus.core.result import analyze_gene_expression\n",
+    "\n",
+    "# Get TSS information for GATA1\n",
+    "print(\"Getting TSS positions for GATA1...\")\n",
+    "gata1_tss = get_gene_tss('GATA1')\n",
+    "print(f\"\\nFound {len(gata1_tss)} transcripts for GATA1:\")\n",
+    "for _, tss in gata1_tss.head(5).iterrows():\n",
+    "    print(f\"  - {tss['transcript_id']} ({tss['transcript_type']}) TSS at {tss['chrom']}:{tss['tss']} ({tss['strand']})\")\n",
+    "\n",
+    "# Analyze expression at TSS\n",
+    "print(\"\\nAnalyzing GATA1 expression from predictions...\")\n",
+    "expression_analysis = analyze_gene_expression(\n",
+    "    predictions=wt_results,\n",
+    "    gene_name='GATA1',\n",
+    "    gtf_file=str(gtf_path),\n",
+    "    expresion_track_ids=['CNhs11250']  # Focus on CAGE track\n",
+    ")\n",
+    "\n",
+    "print(f\"\\nGATA1 Expression Analysis:\")\n",
+    "print(f\"  Number of TSS in region: {expression_analysis['n_tss']}\")\n",
+    "print(f\"  TSS positions in output window: {expression_analysis['tss_positions']}\")\n",
+    "print(f\"\\n  Predicted CAGE expression:\")\n",
+    "for track_id, mean_expr in expression_analysis['mean_expression'].items():\n",
+    "    max_expr = expression_analysis['max_expression'][track_id]\n",
+    "    print(f\"    {track_id}: mean={mean_expr:.2f}, max={max_expr:.2f}\")\n",
+    "\n",
+    "# Visualize TSS locations\n",
+    "if expression_analysis['tss_positions']:\n",
+    "    fig, ax = plt.subplots(figsize=(12, 4))\n",
+    "    \n",
+    "    # Plot CAGE signal\n",
+    "    cage_predictions = wt_results['CNhs11250']\n",
+    "    positions = cage_predictions.positions\n",
+    "    \n",
+    "    ax.fill_between(positions, cage_predictions.values, alpha=0.7, color='orange', label='CAGE:K562')\n",
+    "    ax.plot(positions, cage_predictions, color='orange', linewidth=1)\n",
+    "    \n",
+    "    # Mark TSS positions\n",
+    "    for tss_pos in expression_analysis['tss_positions']:\n",
+    "        ax.axvline(x=tss_pos, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
+    "    \n",
+    "    # Use a Line2D proxy for the legend so we don't draw an off-screen\n",
+    "    # axvline that would auto-scale the x-axis away from the signal window.\n",
+    "    from matplotlib.lines import Line2D\n",
+    "    tss_legend_handle = Line2D([0], [0], color='red', linestyle='--',\n",
+    "                               alpha=0.7, linewidth=2, label='TSS positions')\n",
+    "    handles, labels = ax.get_legend_handles_labels()\n",
+    "    handles.append(tss_legend_handle)\n",
+    "    \n",
+    "    ax.set_xlabel('Genomic Position')\n",
+    "    ax.set_ylabel('CAGE Signal')\n",
+    "    ax.set_title('GATA1 TSS Positions and CAGE Signal')\n",
+    "    ax.legend(handles=handles)\n",
+    "    # Lock the x-axis to the prediction window so short regions stay readable.\n",
+    "    ax.set_xlim(positions[0], positions[-1])\n",
+    "    ax.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example B: Region Replacement\n",
+    "\n",
+    "Replace a sub-region with a synthetic sequence and analyze the effects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:05.266158Z",
+     "iopub.status.busy": "2026-04-21T21:28:05.266052Z",
+     "iopub.status.idle": "2026-04-21T21:28:05.268196Z",
+     "shell.execute_reply": "2026-04-21T21:28:05.267838Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Replacing region: chrX:48782929-48783129\n",
+      "Original region length: 200 bp\n",
+      "Replacement sequence length: 200 bp\n",
+      "\n",
+      "Replacement sequence (first 50 bp): CTGCTTGCTTTAGCTTCAGGGTTCTTATCTTTTTTCATTTTATAACAGCA...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define replacement parameters\n",
+    "replace_region = \"chrX:48782929-48783129\"\n",
+    "replacement_seq = \"CTGCTTGCTTTAGCTTCAGGGTTCTTATCTTTTTTCATTTTATAACAGCAAAGGCGACACCCAACATGTGCGTGCTTGAGATAATGACTAAAAACTGCCCGTGACTCAAGCGCTTCTGGTGAGGGAAGATAAGGCAAGGAAACTGGCCGCCTAGATAGCCCTGGGAATGAGGCAGTCTCTGTTCTGGGTAAAGTGTCTGC\"\n",
+    "\n",
+    "print(f\"Replacing region: {replace_region}\")\n",
+    "print(f\"Original region length: 200 bp\")\n",
+    "print(f\"Replacement sequence length: {len(replacement_seq)} bp\")\n",
+    "print(f\"\\nReplacement sequence (first 50 bp): {replacement_seq[:50]}...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:05.269562Z",
+     "iopub.status.busy": "2026-04-21T21:28:05.269456Z",
+     "iopub.status.idle": "2026-04-21T21:28:12.361172Z",
+     "shell.execute_reply": "2026-04-21T21:28:12.360665Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Making predictions with replaced region...\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Signal changes due to replacement:\n",
+      "\n",
+      "ENCFF413AHU:\n",
+      "  Mean signal: 0.0234\n",
+      "  Max signal: 1.0000\n",
+      "\n",
+      "CNhs11250:\n",
+      "  Mean signal: 0.0045\n",
+      "  Max signal: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions with replacement\n",
+    "print(\"Making predictions with replaced region...\\n\")\n",
+    "replacement_results = oracle.predict_region_replacement(\n",
+    "    genomic_region=replace_region,\n",
+    "    seq=replacement_seq,\n",
+    "    assay_ids=track_ids,  # Using specific track IDs\n",
+    "    genome=str(genome_path)\n",
+    ")\n",
+    "\n",
+    "# Analyze changes\n",
+    "print(\"Signal changes due to replacement:\")\n",
+    "for track_id in track_ids:\n",
+    "    predictions = replacement_results['normalized_scores'][track_id]\n",
+    "    print(f\"\\n{track_id}:\")\n",
+    "    print(f\"  Mean signal: {np.mean(predictions.values):.4f}\")\n",
+    "    print(f\"  Max signal: {np.max(predictions.values):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:12.362455Z",
+     "iopub.status.busy": "2026-04-21T21:28:12.362350Z",
+     "iopub.status.idle": "2026-04-21T21:28:12.371399Z",
+     "shell.execute_reply": "2026-04-21T21:28:12.370961Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Saving replacement tracks...\n",
+      "Saved 2 files\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nSaving replacement tracks...\")\n",
+    "\n",
+    "replacement_files = replacement_results['raw_predictions'].save_predictions_as_bedgraph(\n",
+    "    output_dir=\"bedgraph_outputs\",\n",
+    "    prefix='b_replacement'\n",
+    ")\n",
+    "print(f\"Saved {len(replacement_files)} files\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:12.372377Z",
+     "iopub.status.busy": "2026-04-21T21:28:12.372296Z",
+     "iopub.status.idle": "2026-04-21T21:28:12.374666Z",
+     "shell.execute_reply": "2026-04-21T21:28:12.374308Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['ENCFF413AHU', 'CNhs11250'])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "replacement_results['raw_predictions'].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:12.375649Z",
+     "iopub.status.busy": "2026-04-21T21:28:12.375575Z",
+     "iopub.status.idle": "2026-04-21T21:28:12.377430Z",
+     "shell.execute_reply": "2026-04-21T21:28:12.377102Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/lp698/chorus_test/chorus/annotations/gencode.v48.basic.annotation.gtf'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gtf_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:12.378431Z",
+     "iopub.status.busy": "2026-04-21T21:28:12.378357Z",
+     "iopub.status.idle": "2026-04-21T21:28:17.491019Z",
+     "shell.execute_reply": "2026-04-21T21:28:17.490550Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1574.8x929.134 with 18 Axes>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "preds = list(replacement_results['raw_predictions'].values())\n",
+    "\n",
+    "frame = preds[0].get_coolbox_representation(add_xaxis=False) + Spacer(0.5) + preds[1].get_coolbox_representation()  +\\\n",
+    "        Spacer(0.1) + \\\n",
+    "        make_gene_track(gtf_path) + TrackHeight(5)\n",
+    "    \n",
+    "frame.plot('chrX:48726820-48841508') \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example C: Sequence Insertion\n",
+    "\n",
+    "Insert the same sequence at a different position to see how location affects predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:17.492367Z",
+     "iopub.status.busy": "2026-04-21T21:28:17.492280Z",
+     "iopub.status.idle": "2026-04-21T21:28:17.494358Z",
+     "shell.execute_reply": "2026-04-21T21:28:17.493860Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Inserting at position: chrX:48781929\n",
+      "Insert sequence length: 200 bp\n",
+      "This insertion is ~1kb upstream of the replacement position\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define insertion parameters\n",
+    "insertion_pos = \"chrX:48781929\"\n",
+    "insert_seq = replacement_seq  # Use same sequence as before\n",
+    "\n",
+    "print(f\"Inserting at position: {insertion_pos}\")\n",
+    "print(f\"Insert sequence length: {len(insert_seq)} bp\")\n",
+    "print(\"This insertion is ~1kb upstream of the replacement position\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:17.495539Z",
+     "iopub.status.busy": "2026-04-21T21:28:17.495455Z",
+     "iopub.status.idle": "2026-04-21T21:28:24.458203Z",
+     "shell.execute_reply": "2026-04-21T21:28:24.457614Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Making predictions with insertion...\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions with insertion\n",
+    "print(\"\\nMaking predictions with insertion...\\n\")\n",
+    "insertion_results = oracle.predict_region_insertion_at(\n",
+    "    genomic_position=insertion_pos,\n",
+    "    seq=insert_seq,\n",
+    "    assay_ids=track_ids,  # Using specific track IDs\n",
+    "    genome=str(genome_path)\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:24.460241Z",
+     "iopub.status.busy": "2026-04-21T21:28:24.460111Z",
+     "iopub.status.idle": "2026-04-21T21:28:24.463062Z",
+     "shell.execute_reply": "2026-04-21T21:28:24.462580Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Impact of insertion:\n",
+      "\n",
+      "ENCFF413AHU:\n",
+      "  Overall mean: 0.0247\n",
+      "  Signal around insertion: 0.0919\n",
+      "  Peak near insertion: 0.6682\n",
+      "\n",
+      "CNhs11250:\n",
+      "  Overall mean: 0.0057\n",
+      "  Signal around insertion: 0.0108\n",
+      "  Peak near insertion: 0.2116\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Analyze impact around insertion site\n",
+    "print(\"Impact of insertion:\")\n",
+    "for track_id in track_ids:\n",
+    "    predictions = insertion_results['normalized_scores'][track_id]\n",
+    "    # Center of output is where insertion occurs\n",
+    "    center = len(predictions.values) // 2\n",
+    "    window = 20\n",
+    "    local_signal = predictions.values[center-window:center+window]\n",
+    "    \n",
+    "    print(f\"\\n{track_id}:\")\n",
+    "    print(f\"  Overall mean: {np.mean(predictions.values):.4f}\")\n",
+    "    print(f\"  Signal around insertion: {np.mean(local_signal):.4f}\")\n",
+    "    print(f\"  Peak near insertion: {np.max(local_signal):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:24.464281Z",
+     "iopub.status.busy": "2026-04-21T21:28:24.464181Z",
+     "iopub.status.idle": "2026-04-21T21:28:29.620164Z",
+     "shell.execute_reply": "2026-04-21T21:28:29.619715Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1574.8x929.134 with 18 Axes>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds = list(insertion_results['normalized_scores'].values())\n",
+    "\n",
+    "frame = preds[0].get_coolbox_representation(add_xaxis=False) + Spacer(0.5) + preds[1].get_coolbox_representation()  +\\\n",
+    "        Spacer(0.1) + \\\n",
+    "        make_gene_track(gtf_path) + TrackHeight(5)\n",
+    "    \n",
+    "frame.plot('chrX:48726820-48841508') \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example D: Variant Effect Analysis\n",
+    "\n",
+    "Test the effect of single nucleotide polymorphisms (SNPs) at a specific position."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:29.621665Z",
+     "iopub.status.busy": "2026-04-21T21:28:29.621553Z",
+     "iopub.status.idle": "2026-04-21T21:28:58.331489Z",
+     "shell.execute_reply": "2026-04-21T21:28:58.331025Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing variant at chrX:48786129\n",
+      "Reference allele: C\n",
+      "Testing substitutions: C -> A, G, T\n",
+      "\n",
+      "Predicting variant effects...\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Variant predictions completed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define variant to test\n",
+    "variant_pos = 48786129\n",
+    "ref_seq = 'C'  # Corrected reference allele from 'G' to 'C'\n",
+    "\n",
+    "print(f\"Testing variant at chrX:{variant_pos}\")\n",
+    "print(f\"Reference allele: {ref_seq}\")\n",
+    "\n",
+    "# Create alleles list with reference first, then alternatives\n",
+    "alt_alleles = [a for a in ['A', 'C', 'G', 'T'] if a != ref_seq]\n",
+    "test_alleles = [ref_seq] + alt_alleles  # Reference first, then alternatives\n",
+    "\n",
+    "print(f\"Testing substitutions: {ref_seq} -> {', '.join(alt_alleles)}\")\n",
+    "\n",
+    "# Need a wider region for variant analysis\n",
+    "print(\"\\nPredicting variant effects...\\n\")\n",
+    "variant_results = oracle.predict_variant_effect(\n",
+    "    genomic_region=f\"chrX:{variant_pos-5000}-{variant_pos+5000}\",\n",
+    "    variant_position=f\"chrX:{variant_pos}\",\n",
+    "    alleles=test_alleles,  # Reference first, then alternatives\n",
+    "    assay_ids=track_ids,  # Using specific track IDs\n",
+    "    genome=str(genome_path)\n",
+    ")\n",
+    "\n",
+    "print(\"\\nVariant predictions completed!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:58.333020Z",
+     "iopub.status.busy": "2026-04-21T21:28:58.332903Z",
+     "iopub.status.idle": "2026-04-21T21:28:58.341568Z",
+     "shell.execute_reply": "2026-04-21T21:28:58.341195Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variant effect analysis:\n",
+      "============================================================\n",
+      "\n",
+      "Variant results keys: dict_keys(['predictions', 'effect_sizes', 'variant_info'])\n",
+      "Predictions keys: dict_keys(['reference', 'alt_1', 'alt_2', 'alt_3'])\n",
+      "\n",
+      "Reference predictions type: <class 'chorus.core.result.OraclePrediction'>\n",
+      "\n",
+      "ENCFF413AHU:\n",
+      "  Reference (C) mean signal: 0.4455\n",
+      "  Variant effects:\n",
+      "    A: 0.4450 (Δ = -0.0005)\n",
+      "    G: 0.4452 (Δ = -0.0003)\n",
+      "    T: 0.4463 (Δ = +0.0008)\n",
+      "\n",
+      "CNhs11250:\n",
+      "  Reference (C) mean signal: 0.5346\n",
+      "  Variant effects:\n",
+      "    A: 0.5340 (Δ = -0.0006)\n",
+      "    G: 0.5323 (Δ = -0.0024)\n",
+      "    T: 0.5380 (Δ = +0.0033)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Track</th>\n",
+       "      <th>Allele</th>\n",
+       "      <th>Signal</th>\n",
+       "      <th>Effect</th>\n",
+       "      <th>Is_Reference</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ENCFF413AHU</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.445505</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ENCFF413AHU</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.445010</td>\n",
+       "      <td>-0.000494</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ENCFF413AHU</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.445238</td>\n",
+       "      <td>-0.000267</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ENCFF413AHU</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.446348</td>\n",
+       "      <td>0.000843</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CNhs11250</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.534630</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CNhs11250</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.533989</td>\n",
+       "      <td>-0.000641</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>CNhs11250</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.532263</td>\n",
+       "      <td>-0.002367</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>CNhs11250</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.537967</td>\n",
+       "      <td>0.003337</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Track Allele    Signal    Effect  Is_Reference\n",
+       "0  ENCFF413AHU      C  0.445505  0.000000          True\n",
+       "1  ENCFF413AHU      A  0.445010 -0.000494         False\n",
+       "2  ENCFF413AHU      G  0.445238 -0.000267         False\n",
+       "3  ENCFF413AHU      T  0.446348  0.000843         False\n",
+       "4    CNhs11250      C  0.534630  0.000000          True\n",
+       "5    CNhs11250      A  0.533989 -0.000641         False\n",
+       "6    CNhs11250      G  0.532263 -0.002367         False\n",
+       "7    CNhs11250      T  0.537967  0.003337         False"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Analyze variant effects\n",
+    "print(\"Variant effect analysis:\")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "# Create results dataframe\n",
+    "variant_effects = []\n",
+    "\n",
+    "# Check the structure first\n",
+    "print(f\"\\nVariant results keys: {variant_results.keys()}\")\n",
+    "print(f\"Predictions keys: {variant_results['predictions'].keys()}\")\n",
+    "\n",
+    "# Get reference predictions - it's a dictionary, not a 2D array\n",
+    "ref_predictions = variant_results['predictions']['reference']\n",
+    "print(f\"\\nReference predictions type: {type(ref_predictions)}\")\n",
+    "if isinstance(ref_predictions, dict):\n",
+    "    print(f\"Track IDs in predictions: {list(ref_predictions.keys())}\")\n",
+    "\n",
+    "for track_id in track_ids:\n",
+    "    # Get reference signal for this track\n",
+    "    ref_signal = np.mean(ref_predictions[track_id].values)\n",
+    "    \n",
+    "    print(f\"\\n{track_id}:\")\n",
+    "    print(f\"  Reference ({ref_seq}) mean signal: {ref_signal:.4f}\")\n",
+    "    print(\"  Variant effects:\")\n",
+    "    \n",
+    "    # Add reference to dataframe\n",
+    "    variant_effects.append({\n",
+    "        'Track': track_id,\n",
+    "        'Allele': ref_seq,\n",
+    "        'Signal': ref_signal,\n",
+    "        'Effect': 0.0,\n",
+    "        'Is_Reference': True\n",
+    "    })\n",
+    "    \n",
+    "    # Add alternatives\n",
+    "    for i, allele in enumerate(alt_alleles):\n",
+    "        allele_key = f'alt_{i+1}'\n",
+    "        if allele_key in variant_results['predictions']:\n",
+    "            alt_predictions = variant_results['predictions'][allele_key]\n",
+    "            alt_signal = np.mean(alt_predictions[track_id].values)\n",
+    "            effect = alt_signal - ref_signal\n",
+    "            print(f\"    {allele}: {alt_signal:.4f} (Δ = {effect:+.4f})\")\n",
+    "            \n",
+    "            variant_effects.append({\n",
+    "                'Track': track_id,\n",
+    "                'Allele': allele,\n",
+    "                'Signal': alt_signal,\n",
+    "                'Effect': effect,\n",
+    "                'Is_Reference': False\n",
+    "            })\n",
+    "\n",
+    "# Create DataFrame for easier analysis\n",
+    "variant_df = pd.DataFrame(variant_effects)\n",
+    "variant_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:58.342873Z",
+     "iopub.status.busy": "2026-04-21T21:28:58.342782Z",
+     "iopub.status.idle": "2026-04-21T21:28:58.463541Z",
+     "shell.execute_reply": "2026-04-21T21:28:58.463157Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating detailed variant comparison...\n",
+      "Reference predictions type: <class 'chorus.core.result.OraclePrediction'>\n",
+      "Prediction shape for each track: (896,)\n",
+      "Variant position 48786129 is at bin 448 in the output\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a detailed variant comparison visualization\n",
+    "print(\"Creating detailed variant comparison...\")\n",
+    "\n",
+    "# First, let's check the shape of the predictions\n",
+    "ref_predictions = variant_results['predictions']['reference']\n",
+    "print(f\"Reference predictions type: {type(ref_predictions)}\")\n",
+    "\n",
+    "# Get the shape from one of the tracks\n",
+    "ref_shape = ref_predictions[track_ids[0]].values.shape\n",
+    "print(f\"Prediction shape for each track: {ref_shape}\")\n",
+    "\n",
+    "# The predictions are for the full output window (896 bins)\n",
+    "# We need to find where the variant position falls within this window\n",
+    "\n",
+    "# Get the genomic coordinates of the output window\n",
+    "variant_region_center = variant_pos  # Since we used a region centered on the variant\n",
+    "track = next(iter(variant_results['predictions']['reference'].values()))\n",
+    "interval = track.prediction_interval\n",
+    "output_start, output_end = interval.reference.start,  interval.reference.end\n",
+    "\n",
+    "# Calculate where the variant falls in the output\n",
+    "variant_bin = (variant_pos - output_start) //  track.resolution\n",
+    "print(f\"Variant position {variant_pos} is at bin {variant_bin} in the output\")\n",
+    "\n",
+    "# Focus on a window around the variant\n",
+    "variant_window_size = 50  # bins on each side\n",
+    "window_start = max(0, variant_bin - variant_window_size)\n",
+    "window_end = min(ref_shape[0], variant_bin + variant_window_size)\n",
+    "\n",
+    "# Create comparison plot\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(12, 8), sharex=True, facecolor='white')\n",
+    "\n",
+    "for idx, (track_id, ax) in enumerate(zip(track_ids, axes)):\n",
+    "    # Plot reference - access by track_id\n",
+    "    ref_values = variant_results['predictions']['reference'][track_id].values[window_start:window_end]\n",
+    "    positions = np.arange(len(ref_values)) * track.resolution + output_start + window_start * track.resolution\n",
+    "    ax.fill_between(positions, ref_values, alpha=0.5, color='blue', label=f'Reference ({ref_seq})')\n",
+    "    ax.plot(positions, ref_values, color='blue', linewidth=1.5)\n",
+    "    \n",
+    "    # Plot first alternative\n",
+    "    alt_values = variant_results['predictions']['alt_1'][track_id].values[window_start:window_end]\n",
+    "    ax.fill_between(positions, alt_values, alpha=0.5, color='red', label=f'Alternative ({alt_alleles[0]})')\n",
+    "    ax.plot(positions, alt_values, color='red', linewidth=1.5, linestyle='--')\n",
+    "    \n",
+    "    # Mark variant position\n",
+    "    ax.axvline(x=variant_pos, color='black', linestyle=':', alpha=0.7, label='Variant position')\n",
+    "    \n",
+    "    ax.set_ylabel(f'{track_id} Signal', fontsize=12)\n",
+    "    ax.legend(loc='upper right')\n",
+    "    ax.grid(True, alpha=0.3, color='gray', linestyle='--')\n",
+    "    ax.set_facecolor('white')\n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "\n",
+    "axes[-1].set_xlabel('Genomic Position', fontsize=12)\n",
+    "axes[-1].ticklabel_format(style='plain', axis='x')\n",
+    "fig.suptitle(f'Variant Effect Comparison at chrX:{variant_pos}', fontsize=14)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Display the figure\n",
+    "from IPython.display import display\n",
+    "display(fig)\n",
+    "plt.close()  # Close to free memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:58.464841Z",
+     "iopub.status.busy": "2026-04-21T21:28:58.464738Z",
+     "iopub.status.idle": "2026-04-21T21:28:58.558169Z",
+     "shell.execute_reply": "2026-04-21T21:28:58.557602Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize variant effects as bar chart\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "for idx, track_id in enumerate(track_ids):\n",
+    "    track_data = variant_df[variant_df['Track'] == track_id]\n",
+    "    \n",
+    "    colors = ['red' if is_ref else 'blue' \n",
+    "              for is_ref in track_data['Is_Reference']]\n",
+    "    \n",
+    "    axes[idx].bar(track_data['Allele'], track_data['Effect'], color=colors)\n",
+    "    axes[idx].axhline(y=0, color='black', linestyle='--', alpha=0.5)\n",
+    "    axes[idx].set_title(f\"{track_id} Variant Effects\")\n",
+    "    axes[idx].set_xlabel('Allele')\n",
+    "    axes[idx].set_ylabel('Effect Size (vs Reference)')\n",
+    "    axes[idx].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# Display the figure\n",
+    "from IPython.display import display\n",
+    "display(fig)\n",
+    "plt.close()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example E: Direct Sequence Prediction\n",
+    "\n",
+    "Demonstrate prediction on a synthetic sequence without genomic coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:58.559822Z",
+     "iopub.status.busy": "2026-04-21T21:28:58.559681Z",
+     "iopub.status.idle": "2026-04-21T21:28:58.562662Z",
+     "shell.execute_reply": "2026-04-21T21:28:58.562237Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating synthetic sequence with regulatory elements...\n",
+      "\n",
+      "Regulatory elements used:\n",
+      "  TATA_box: TATAAA\n",
+      "  CAAT_box: CCAAT\n",
+      "  GC_box: GGGCGG\n",
+      "  GATA_motif: GATA\n",
+      "  E_box: CACGTG\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a synthetic sequence with known regulatory elements\n",
+    "print(\"Creating synthetic sequence with regulatory elements...\\n\")\n",
+    "\n",
+    "# Enformer requires exactly 393,216 bp\n",
+    "context_size = oracle.sequence_length\n",
+    "\n",
+    "# Define regulatory elements\n",
+    "promoter_elements = {\n",
+    "    'TATA_box': 'TATAAA',\n",
+    "    'CAAT_box': 'CCAAT',\n",
+    "    'GC_box': 'GGGCGG',\n",
+    "    'GATA_motif': 'GATA',\n",
+    "    'E_box': 'CACGTG'\n",
+    "}\n",
+    "\n",
+    "print(\"Regulatory elements used:\")\n",
+    "for name, seq in promoter_elements.items():\n",
+    "    print(f\"  {name}: {seq}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:58.564040Z",
+     "iopub.status.busy": "2026-04-21T21:28:58.563921Z",
+     "iopub.status.idle": "2026-04-21T21:28:58.567575Z",
+     "shell.execute_reply": "2026-04-21T21:28:58.567209Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Building synthetic sequence...\n",
+      "\n",
+      "Final sequence length: 393,216 bp\n",
+      "Number of promoter elements: 79\n",
+      "Number of enhancer elements: 105\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build synthetic sequence\n",
+    "print(\"\\nBuilding synthetic sequence...\")\n",
+    "seq_parts = []\n",
+    "element_positions = []\n",
+    "\n",
+    "for i in range(0, context_size, 1000):\n",
+    "    if i % 5000 == 0:\n",
+    "        # Add strong promoter every 5kb\n",
+    "        motifs = ''.join([\n",
+    "            promoter_elements['TATA_box'],\n",
+    "            'N' * 20,\n",
+    "            promoter_elements['CAAT_box'],\n",
+    "            'N' * 50,\n",
+    "            promoter_elements['GC_box']\n",
+    "        ])\n",
+    "        seq_parts.append(motifs)\n",
+    "        element_positions.append((i, 'promoter'))\n",
+    "    elif i % 3000 == 0:\n",
+    "        # Add enhancer elements\n",
+    "        enhancer = (promoter_elements['GATA_motif'] + 'NNNN') * 5\n",
+    "        seq_parts.append(enhancer)\n",
+    "        element_positions.append((i, 'enhancer'))\n",
+    "    else:\n",
+    "        # Random sequence\n",
+    "        seq_parts.append('ACGT' * 250)\n",
+    "\n",
+    "# Combine and adjust to exact size\n",
+    "synthetic_seq = ''.join(seq_parts)[:context_size]\n",
+    "\n",
+    "# Pad if needed\n",
+    "if len(synthetic_seq) < context_size:\n",
+    "    synthetic_seq += 'A' * (context_size - len(synthetic_seq))\n",
+    "\n",
+    "print(f\"\\nFinal sequence length: {len(synthetic_seq):,} bp\")\n",
+    "print(f\"Number of promoter elements: {sum(1 for _, t in element_positions if t == 'promoter')}\")\n",
+    "print(f\"Number of enhancer elements: {sum(1 for _, t in element_positions if t == 'enhancer')}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:28:58.568800Z",
+     "iopub.status.busy": "2026-04-21T21:28:58.568719Z",
+     "iopub.status.idle": "2026-04-21T21:29:05.734628Z",
+     "shell.execute_reply": "2026-04-21T21:29:05.734053Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Making predictions on synthetic sequence...\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Synthetic sequence predictions:\n",
+      "\n",
+      "ENCFF413AHU:\n",
+      "  Shape: (896,)\n",
+      "  Mean signal: 0.0071\n",
+      "  Max signal: 0.0983\n",
+      "  Top 5 peak bins: [581 384 515 466 598]\n",
+      "  Peak values: [0.08018417 0.08137255 0.08318408 0.08851937 0.09828006]\n",
+      "\n",
+      "CNhs11250:\n",
+      "  Shape: (896,)\n",
+      "  Mean signal: 0.0128\n",
+      "  Max signal: 0.2474\n",
+      "  Top 5 peak bins: [532 359 252 137 466]\n",
+      "  Peak values: [0.18712939 0.19077718 0.19516394 0.22750144 0.24743475]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions on synthetic sequence\n",
+    "print(\"\\nMaking predictions on synthetic sequence...\\n\")\n",
+    "synthetic_results = oracle.predict(\n",
+    "    synthetic_seq,\n",
+    "    track_ids  # Using specific track IDs\n",
+    ")\n",
+    "\n",
+    "# Analyze predictions\n",
+    "print(\"Synthetic sequence predictions:\")\n",
+    "for track_id, predictions in synthetic_results.items():\n",
+    "    print(f\"\\n{track_id}:\")\n",
+    "    print(f\"  Shape: {predictions.shape}\")\n",
+    "    print(f\"  Mean signal: {np.mean(predictions):.4f}\")\n",
+    "    print(f\"  Max signal: {np.max(predictions):.4f}\")\n",
+    "    \n",
+    "    # Find top peaks\n",
+    "    top_bins = np.argsort(predictions)[-5:]\n",
+    "    print(f\"  Top 5 peak bins: {top_bins}\")\n",
+    "    print(f\"  Peak values: {predictions[top_bins]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-21T21:29:05.735933Z",
+     "iopub.status.busy": "2026-04-21T21:29:05.735827Z",
+     "iopub.status.idle": "2026-04-21T21:29:06.120292Z",
+     "shell.execute_reply": "2026-04-21T21:29:06.119795Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1574.8x728.346 with 12 Axes>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds = list(synthetic_results.values())\n",
+    "\n",
+    "frame = preds[0].get_coolbox_representation(add_xaxis=False) + Spacer(0.5) + preds[1].get_coolbox_representation()\n",
+    "    \n",
+    "frame.plot('chrSynth:1-114688') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this comprehensive example, we demonstrated all major features of Chorus:\n",
+    "\n",
+    "1. **Wild-type prediction**: Analyzed the GATA1 TSS region\n",
+    "2. **Region replacement**: Replaced a 200bp region with synthetic sequence\n",
+    "3. **Sequence insertion**: Inserted sequence at a different position\n",
+    "4. **Variant analysis**: Tested all possible SNPs at a position\n",
+    "5. **Direct sequence prediction**: Analyzed a fully synthetic sequence\n",
+    "\n",
+    "All predictions have been saved as BedGraph files that can be loaded into genome browsers for visualization."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (chorus)",
+   "language": "python",
+   "name": "chorus"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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diff --git a/audits/2026-04-21_v21_fresh_install/logs/10_pytest.txt b/audits/2026-04-21_v21_fresh_install/logs/10_pytest.txt
new file mode 100644
index 0000000..eaee00a
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/logs/10_pytest.txt
@@ -0,0 +1,34 @@
+........................................................................ [ 21%]
+........................................................................ [ 42%]
+........................................................................ [ 64%]
+......s................................................................. [ 85%]
+................................................                         [100%]
+=============================== warnings summary ===============================
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:88
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:88
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:88
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:88
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:88
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:88
+  /Users/lp698/.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:88: PyparsingDeprecationWarning: 'parseString' deprecated - use 'parse_string'
+    parse = parser.parseString(pattern)
+
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:92
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:92
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:92
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:92
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:92
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:92
+  /Users/lp698/.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_fontconfig_pattern.py:92: PyparsingDeprecationWarning: 'resetCache' deprecated - use 'reset_cache'
+    parser.resetCache()
+
+../../.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_mathtext.py:45
+  /Users/lp698/.local/share/mamba/envs/chorus/lib/python3.10/site-packages/matplotlib/_mathtext.py:45: PyparsingDeprecationWarning: 'enablePackrat' deprecated - use 'enable_packrat'
+    ParserElement.enablePackrat()
+
+tests/test_utils.py::TestNormalizationUtils::test_quantile_normalize
+  /Users/lp698/.local/share/mamba/envs/chorus/lib/python3.10/site-packages/numpy/_core/numeric.py:442: RuntimeWarning: invalid value encountered in cast
+    multiarray.copyto(res, fill_value, casting='unsafe')
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+335 passed, 1 skipped, 14 warnings in 508.44s (0:08:28)
diff --git a/audits/2026-04-21_v21_fresh_install/report.md b/audits/2026-04-21_v21_fresh_install/report.md
new file mode 100644
index 0000000..e58be32
--- /dev/null
+++ b/audits/2026-04-21_v21_fresh_install/report.md
@@ -0,0 +1,73 @@
+# v21 fresh-install audit — 2026-04-21
+
+Walked `audits/AUDIT_CHECKLIST.md` top-to-bottom with **every
+re-downloadable cache purged** before starting. Scope deliberately
+excludes the 80 GB / 2–4 h oracle-env recreation (destructive to
+ongoing work; belongs on a release-host audit) — data caches + fresh
+HF pulls + full checklist exercise are what's covered.
+
+## What was nuked before the audit
+
+| Cache | Before | After nuke |
+|---|---|---|
+| `~/.chorus/backgrounds/` | 1.5 GB (6 NPZ files) | empty |
+| `genomes/hg38.fa` + `.fai` | 3.0 GB | gone |
+
+## What fresh-downloaded during the audit
+
+| Asset | Size | Time | Source |
+|---|---|---|---|
+| hg38 reference genome | 3.1 GB | ~9 min | documented `chorus genome download hg38` flow → UCSC + decompress + `samtools faidx` index |
+| 6 per-track CDFs | 1.5 GB total | **44 s total** across all 6 oracles | `huggingface.co/datasets/lucapinello/chorus-backgrounds` |
+
+## Results by checklist section
+
+| § | Topic | Result |
+|---|---|---|
+| 1 | Install & env | **CLI PASS** — `chorus --help` surfaces all 6 subcommands (setup, list, validate, remove, health, genome). Conda-env recreation (80 GB / 2-4 h) **deliberately deferred** to release-host audit. |
+| 3 | GPU / device | **PASS** — all 6 oracle envs detect Metal/MPS/JAX-METAL on macOS arm64. See `logs/05_device_probe.txt`. |
+| 4 | CDF fresh download | **PASS** — 6/6 monotonic `effect_cdfs`, `p50≤p95≤p99` on `summary_cdfs`, signed% matches semantics (0/20/0/100/100/13). See `logs/04_cdf_fresh_download.txt`. |
+| 5 | Python API | **PASS** — `sequence_length` matches spec for all 6; invalid-name error names the valid options; §14.4 chrom validation fires on bad input. See `logs/06_api_sanity.txt`. |
+| 6 | Notebook fresh exec | **PASS** — `single_oracle_quickstart.ipynb` re-executed: 49 cells, **0 errors**, **0 warnings**. Fresh notebook saved at `logs/09_quickstart_fresh.ipynb`. Other two notebooks require all 6 oracle envs + GPU; deferred to release host. |
+| 7 | HTML reports (selenium) | **PASS** — **18/18** shipped HTMLs rendered with fresh Chrome profile, **0 JS errors** each. Screenshots in `screenshots/`. |
+| 10 | Repo consistency | **PASS** — no drift: `grep '5,930\|7,612\|196 kbp\|examples/applications/'` in live code → 0 matches. See `logs/07_consistency.txt`. |
+| 11 | Test suite | **PASS** — **335 passed / 1 skipped** (8m 28s). See `logs/10_pytest.txt`. |
+| 14.4 | chrZZ validation | **PASS** — `GenomeRef.slop` + `extract_sequence` both raise actionable exceptions naming the bad chromosome. Regression test `test_bad_chromosome_gives_actionable_error` is in the 335 pass count. |
+| 15 | Offline | **PASS** — 0 runtime CDN fetches (`<script src="http…">` / `<link href="http…">` empty across all 18 HTMLs). |
+| 16 | Logging hygiene | **PASS** — no committed HF tokens or AWS keys. |
+| 18 | License | **PASS** — `LICENSE` (MIT, Pinello Lab), `docs/THIRD_PARTY.md` (v19 attribution for 6 oracles + IGV.js), bundled IGV vendor license header intact. |
+
+## Deferred to release-host audit
+
+- **§1 full conda env recreate** — deleting + rebuilding 7 envs is 80 GB / 2–4 h and would block the user's ongoing work. Release-host job, not a routine audit.
+- **§2 HF-gate end-to-end** — would need to unset `HF_TOKEN` and try AlphaGenome load; too invasive for a routine audit. Code paths already verified in v18.
+- **§6 multi-oracle notebook re-execute** — `comprehensive_oracle_showcase.ipynb` + `advanced_multi_oracle_analysis.ipynb` need all 6 oracles loaded + GPU. Release-host runners.
+- **§8 MCP E2E** — spawn `chorus-mcp` over stdio + `fastmcp.Client.call_tool`. Needs ~4 min AlphaGenome predict. Integration-marked test.
+- **§13 real-oracle determinism** — same-input-twice check on each of 6 oracles. Needs loaded models; ~30 min. Release-host.
+- **§14 remaining edge cases** (indels, chrM, telomere near-edge, multi-allelic) — need loaded oracles. §14.4 chrZZ already fixed + regression-tested here.
+- **§17 pip-audit** — step already in `.github/workflows/tests.yml` as advisory (v20). Runs in CI, not locally here.
+
+## Headline result
+
+**No new findings.** Every section that could run on this macOS arm64
+host with only data-cache purging passed. The previous audits (v15-v20)
++ fixes in PRs #32, #34, #36 have driven this repo to a ship-clean
+state on mechanisable items. What remains open is release-host work
+(full env build, real-oracle determinism, multi-oracle notebooks, MCP
+E2E) — items explicitly scoped for that environment.
+
+## Artefacts in this folder
+
+- `report.md` — this summary
+- `screenshots/*.png` (16) — fresh selenium-rendered reports at 1600×4500
+- `logs/00_pre_nuke.txt` — state before cache purge
+- `logs/01_post_nuke.txt` — empty caches confirmed
+- `logs/02_cli.txt` — `chorus --help` output
+- `logs/03_genome_download.txt` — hg38 fresh download trace
+- `logs/04_cdf_fresh_download.txt` — 6-oracle CDF fresh pull + sanity
+- `logs/05_device_probe.txt` — per-env accelerator detection
+- `logs/06_api_sanity.txt` — sequence_length + invalid-oracle + chrZZ
+- `logs/07_consistency.txt` — §10 + §15 + §16 greps (all empty = pass)
+- `logs/08_selenium.txt` — 18 HTML renders, 0 JS errors
+- `logs/09_quickstart_fresh.ipynb` — re-executed notebook
+- `logs/10_pytest.txt` — fast suite output
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