{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "7adaa9f8",
      "metadata": {},
      "source": [
        "# 🧬 NTv3 Post-Trained Inference on Human Genomic Windows\n",
        "\n",
        "This notebook demonstrates how to use the **NTv3 post-trained model** to predict functional genomics tracks and genomic element annotations from DNA sequences.\n",
        "\n",
        "> 📝 **Note for Google Colab users:** This notebook is compatible with Colab! For faster inference, make sure to enable GPU: Runtime → Change runtime type → GPU (T4 or better recommended).\n",
        "\n",
        "## 📋 Overview\n",
        "\n",
        "Given a genomic window from the **human genome (hg38)**, the model performs inference and generates:\n",
        "\n",
        "- **Functional tracks** (`bigwig_tracks_logits`): Predictions for thousands of functional genomics tracks (e.g., ChIP-seq, RNA-seq, DNase-seq) across multiple cell types and conditions\n",
        "- **Genomic element annotations** (`bed_tracks_logits`): Classification predictions for genomic elements such as genes, exons, introns, splice sites, promoters, enhancers, and more\n",
        "- **Masked Language Model logits** (`logits`): Standard transformer language model outputs\n",
        "\n",
        "> 💡 **Note:** Functional tracks and genomic element annotations are predicted only for the center 37.5% of the input sequence, where the model is more confident due to having full context on both sides.\n",
        "\n",
        "## 📚 Notebook Structure\n",
        "\n",
        "1. **Setup**: Install dependencies and define the genomic window of interest\n",
        "2. **Data Loading**: Download and fetch the chromosome sequence from UCSC\n",
        "3. **Model Loading**: Load the NTv3 post-trained model and tokenizers\n",
        "4. **Inference**: Run the model on the genomic window to generate predictions\n",
        "5. **Visualization**: Plot selected functional tracks and genomic element predictions together in a unified view\n",
        "\n",
        "## ✨ Additional Features\n",
        "\n",
        "- Supports multiple NTv3 post-trained models\n",
        "- Supports the 24 species that NTv3 was post-trained on"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "77046e68",
      "metadata": {},
      "source": [
        "## 0) 📦 Imports + setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "0ff509fd",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Install dependencies\n",
        "!pip -q install \"transformers>=4.55\" \"huggingface_hub>=0.23\" safetensors torch pyfaidx requests seaborn matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "608d67e1",
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import re\n",
        "import math\n",
        "\n",
        "import torch\n",
        "from transformers import AutoConfig, AutoModel, AutoTokenizer\n",
        "\n",
        "from pyfaidx import Fasta\n",
        "import requests\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "2354e2aa",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "device: cpu dtype: torch.float16\n"
          ]
        }
      ],
      "source": [
        "# Device\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "dtype = torch.bfloat16 if (device == \"cuda\" and torch.cuda.get_device_capability(0)[0] >= 8) else torch.float16\n",
        "print(\"device:\", device, \"dtype:\", dtype)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "19db4774",
      "metadata": {},
      "source": [
        "## 1) 📦 Configuration\n",
        "\n",
        "Set your NTv3 model and genomic window here"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "795a576f",
      "metadata": {},
      "outputs": [],
      "source": [
        "# -----------------------------\n",
        "# User inputs\n",
        "# -----------------------------\n",
        "model_name = \"InstaDeepAI/NTv3_100M_pos\" # options: \"InstaDeepAI/NTv3_100M_pos\" or \"InstaDeepAI/NTv3_650M_pos\"\n",
        "\n",
        "# Example window from a given species (edit these) - needs to be multiple of 128 due to the model downsampling\n",
        "species = \"human\"  # will use for condition the model on species\n",
        "assembly = \"hg38\"  # will use for fetching the chromosome sequence\n",
        "chrom = \"chr19\"\n",
        "start = 6_700_000\n",
        "end   = 6_831_072\n",
        "\n",
        "# Optional\n",
        "HF_TOKEN = os.getenv(\"HF_TOKEN\", None)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "94b54a99",
      "metadata": {},
      "source": [
        "## 2) 📥 Fetch chromosome sequence for the chosen window"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "2e0026e4",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Original sequence length: 131072\n",
            "Cropped sequence length: 131072, 1024.0 transformer tokens\n"
          ]
        }
      ],
      "source": [
        "# Get the sequence from the UCSC API\n",
        "url = f\"https://api.genome.ucsc.edu/getData/sequence?genome={assembly};chrom={chrom};start={start};end={end}\"\n",
        "seq = requests.get(url).json()[\"dna\"].upper()\n",
        "print(f\"Original sequence length: {len(seq)}\")\n",
        "\n",
        "# Crop to multiple of 128 (the pipeline will crop again, but this is a no-op once divisible)\n",
        "seq = seq[:int(len(seq) // 128) * 128]\n",
        "print(f\"Cropped sequence length: {len(seq)}, {len(seq) / 128} transformer tokens\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9f82945c",
      "metadata": {},
      "source": [
        "## 3) 🤖 Load NTv3 model + tokenizers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "e09f0469",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "NTv3Model(\n",
              "  (core): Core(\n",
              "    (embed_layer): Embedding(11, 16, padding_idx=1)\n",
              "    (stem): Stem(\n",
              "      (conv): Conv1d(16, 768, kernel_size=(15,), stride=(1,), padding=same)\n",
              "    )\n",
              "    (cond_tables): ModuleList(\n",
              "      (0): Embedding(30, 16)\n",
              "    )\n",
              "    (conv_tower_blocks): ModuleList(\n",
              "      (0-6): 7 x ConditionedConvTowerBlock(\n",
              "        (conv): AdaptiveConvBlock(\n",
              "          (conv): Conv1d(768, 768, kernel_size=(5,), stride=(1,), padding=same)\n",
              "          (layer_norm): AdaptiveLayerNorm(\n",
              "            (np.int64(768),), eps=1e-05, elementwise_affine=True\n",
              "            (modulation_layers): ModuleList(\n",
              "              (0): Linear(in_features=16, out_features=1536, bias=True)\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "        (res_conv): AdaptiveResidualConvBlock(\n",
              "          (conv_block): AdaptiveConvBlock(\n",
              "            (conv): Conv1d(768, 768, kernel_size=(1,), stride=(1,), padding=same)\n",
              "            (layer_norm): AdaptiveLayerNorm(\n",
              "              (np.int64(768),), eps=1e-05, elementwise_affine=True\n",
              "              (modulation_layers): ModuleList(\n",
              "                (0): Linear(in_features=16, out_features=1536, bias=True)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (modulation_layers): ModuleList(\n",
              "            (0): Linear(in_features=16, out_features=768, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (avg_pool): AvgPool1d(kernel_size=(2,), stride=(2,), padding=(0,))\n",
              "      )\n",
              "    )\n",
              "    (transformer_blocks): ModuleList(\n",
              "      (0-5): 6 x AdaptiveSelfAttentionBlock(\n",
              "        (self_attention_layer_norm): AdaptiveLayerNorm(\n",
              "          (768,), eps=1e-05, elementwise_affine=True\n",
              "          (modulation_layers): ModuleList(\n",
              "            (0): Linear(in_features=16, out_features=1536, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (final_layer_norm): AdaptiveLayerNorm(\n",
              "          (768,), eps=1e-05, elementwise_affine=True\n",
              "          (modulation_layers): ModuleList(\n",
              "            (0): Linear(in_features=16, out_features=1536, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (sa_layer): MultiHeadAttention(\n",
              "          (query_head): LinearProjectionHeInit(\n",
              "            (linear): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (key_head): LinearProjectionHeInit(\n",
              "            (linear): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (value_head): LinearProjectionHeInit(\n",
              "            (linear): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (mha_output): Linear(in_features=768, out_features=768, bias=True)\n",
              "          (rotary_embedding): RotaryEmbedding()\n",
              "        )\n",
              "        (fc1): Linear(in_features=768, out_features=6144, bias=False)\n",
              "        (fc2): Linear(in_features=3072, out_features=768, bias=False)\n",
              "        (_ffn_activation_fn): SiLU()\n",
              "        (modulation_layers): ModuleList(\n",
              "          (0): Linear(in_features=16, out_features=768, bias=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (deconv_tower_blocks): ModuleList(\n",
              "      (0-6): 7 x ConditionedDeConvTowerBlock(\n",
              "        (conv): AdaptiveDeConvBlock(\n",
              "          (conv): Conv1d(768, 768, kernel_size=(5,), stride=(1,), padding=same)\n",
              "          (layer_norm): AdaptiveLayerNorm(\n",
              "            (np.int64(768),), eps=1e-05, elementwise_affine=True\n",
              "            (modulation_layers): ModuleList(\n",
              "              (0): Linear(in_features=16, out_features=1536, bias=True)\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "        (res_conv): AdaptiveResidualDeConvBlock(\n",
              "          (conv_block): AdaptiveDeConvBlock(\n",
              "            (conv): ConvTranspose1d(768, 768, kernel_size=(1,), stride=(1,))\n",
              "            (layer_norm): AdaptiveLayerNorm(\n",
              "              (np.int64(768),), eps=1e-05, elementwise_affine=True\n",
              "              (modulation_layers): ModuleList(\n",
              "                (0): Linear(in_features=16, out_features=1536, bias=True)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (modulation_layers): ModuleList(\n",
              "            (0): Linear(in_features=16, out_features=768, bias=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (bigwig_head): MultiSpeciesHead(\n",
              "      (species_heads): ModuleList(\n",
              "        (0-4): 5 x ZeroHead()\n",
              "        (5): LinearHead(\n",
              "          (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (head): Linear(in_features=768, out_features=590, bias=True)\n",
              "        )\n",
              "        (6): LinearHead(\n",
              "          (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (head): Linear(in_features=768, out_features=319, bias=True)\n",
              "        )\n",
              "        (7): LinearHead(\n",
              "          (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (head): Linear(in_features=768, out_features=1392, bias=True)\n",
              "        )\n",
              "        (8): LinearHead(\n",
              "          (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (head): Linear(in_features=768, out_features=776, bias=True)\n",
              "        )\n",
              "        (9-12): 4 x ZeroHead()\n",
              "        (13): LinearHead(\n",
              "          (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (head): Linear(in_features=768, out_features=1899, bias=True)\n",
              "        )\n",
              "        (14-15): 2 x ZeroHead()\n",
              "        (16): LinearHead(\n",
              "          (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (head): Linear(in_features=768, out_features=921, bias=True)\n",
              "        )\n",
              "        (17): ZeroHead()\n",
              "        (18): LinearHead(\n",
              "          (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (head): Linear(in_features=768, out_features=180, bias=True)\n",
              "        )\n",
              "        (19-20): 2 x ZeroHead()\n",
              "        (21): LinearHead(\n",
              "          (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (head): Linear(in_features=768, out_features=7362, bias=True)\n",
              "        )\n",
              "        (22): ZeroHead()\n",
              "        (23): LinearHead(\n",
              "          (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (head): Linear(in_features=768, out_features=2450, bias=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (bed_head): ClassificationHead(\n",
              "      (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "      (head): Linear(in_features=768, out_features=42, bias=True)\n",
              "    )\n",
              "    (conditions_heads): ModuleList(\n",
              "      (0): Linear(in_features=768, out_features=30, bias=True)\n",
              "    )\n",
              "    (lm_head): ModuleDict(\n",
              "      (hidden_layers): ModuleList()\n",
              "      (head): Linear(in_features=768, out_features=11, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Load model\n",
        "cfg = AutoConfig.from_pretrained(model_name, trust_remote_code=True)\n",
        "model = AutoModel.from_pretrained(model_name, trust_remote_code=True).to(device)\n",
        "\n",
        "# Load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "\n",
        "# Load condition tokenizer\n",
        "species_tokenizer = AutoTokenizer.from_pretrained(\n",
        "    model_name, subfolder=\"species_tokenizer\", trust_remote_code=True,\n",
        ")\n",
        "\n",
        "# Set model to evaluation mode\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "43154959",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "7362 functional tracks for hg38. First 10: ['kai1', 'kai2', 'kai3', 'kai4', 'kai5', 'kai6', 'kai7', 'kai8', 'kai10', 'kai9']\n",
            "Genomic elements predicted: ['protein_coding_gene', 'lncRNA', 'exon', 'intron', 'splice_donor', 'splice_acceptor', 'CTCF-bound', 'polyA_signal', 'enhancer_Tissue_specific', 'enhancer_Tissue_invariant', 'promoter_Tissue_specific', 'promoter_Tissue_invariant', '5UTR+', '5UTR-', '3UTR+', '3UTR-', 'skipped_exon', 'always_on_exon', 'start_codon', 'stop_codon', 'ORF']\n"
          ]
        }
      ],
      "source": [
        "# Inspect output functional tracks\n",
        "bigwig_names = cfg.bigwigs_per_file_assembly[assembly]\n",
        "print(f\"{len(bigwig_names)} functional tracks for {assembly}. First 10:\", bigwig_names[:10])\n",
        "\n",
        "# Inspect output genomic elements\n",
        "bed_element_names = cfg.bed_elements_names\n",
        "print(\"Genomic elements predicted:\", bed_element_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "70413b72",
      "metadata": {},
      "source": [
        "## 4) ⚡ Tokenize the window and run inference\n",
        "\n",
        "We pass:\n",
        "\n",
        "- `input_ids`: tokenized DNA window\n",
        "- `species_ids`: species tokens (`human`)\n",
        "\n",
        "Outputs include:\n",
        "\n",
        "- `bigwig_tracks_logits`: per-position track predictions\n",
        "- `bed_tracks_logits`: per-position classification logits for BED elements\n",
        "- `logits`: masked LM logits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "6765a9b9",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "bigwig_tracks_logits: (1, 49152, 7362)\n",
            "bed_tracks_logits: (1, 49152, 21, 2)\n",
            "language model logits: (1, 131072, 11)\n"
          ]
        }
      ],
      "source": [
        "# Tokenize DNA sequence (single example batch)\n",
        "batch = tokenizer([seq], add_special_tokens=False, return_tensors=\"pt\")\n",
        "input_ids = batch[\"input_ids\"].to(device)\n",
        "\n",
        "# Condition tokens (e.g., species)\n",
        "species = 'human'\n",
        "species_ids = species_tokenizer([species] * len(batch['input_ids']), add_special_tokens=False, return_tensors='pt')\n",
        "\n",
        "# Run inference\n",
        "out = model(\n",
        "    input_ids=input_ids,\n",
        "    species_ids=species_ids['input_ids'],\n",
        "    return_dict=True\n",
        ")\n",
        "\n",
        "# 7k human tracks over 37.5 % center region of the input sequence\n",
        "print(\"bigwig_tracks_logits:\", tuple(out[\"bigwig_tracks_logits\"].shape))\n",
        "# Location of 21 genomic elements over 37.5 % center region of the input sequence\n",
        "print(\"bed_tracks_logits:\", tuple(out[\"bed_tracks_logits\"].shape))\n",
        "# Language model logits for whole sequence over vocabulary\n",
        "print(\"language model logits:\", tuple(out[\"logits\"].shape))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b8423e62",
      "metadata": {},
      "source": [
        "## 5) 📊 Plot functional tracks and genome annotation predictions\n",
        "\n",
        "This plots track probabilities for selected functional tracks and genomic elements.\n",
        "\n",
        "For functional tracks we plot logits directly here. If you have a calibrated mapping (e.g., sigmoid/softplus), apply it before plotting.\n",
        "\n",
        "Genome annotation tracks have a final dimension for classes (`[neg, pos]`). Here, we:\n",
        "- convert logits to probabilities using softmax\n",
        "- extract the positive-class probability for selected elements\n",
        "- plot them as continuous tracks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "a26e9dcc",
      "metadata": {},
      "outputs": [],
      "source": [
        "def plot_tracks(tracks, start, end, height=1.0):\n",
        "    fig, axes = plt.subplots(len(tracks), 1, figsize=(20, height * len(tracks)), sharex=True)\n",
        "    for ax, (title, y) in zip(axes, tracks.items()):\n",
        "        ax.fill_between(np.linspace(start, end, num=len(y)), y)\n",
        "        ax.set_title(title)\n",
        "        sns.despine(top=True, right=True, bottom=True)\n",
        "    ax.set_xlabel(f\"{chrom}:{start}-{end} ({assembly})\")\n",
        "    plt.tight_layout()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "717539e2",
      "metadata": {},
      "outputs": [],
      "source": [
        "### 🎯 Select functional tracks to plot\n",
        "tracks_to_plot = {\n",
        "    \"K562 RNA-seq\": \"ENCSR056HPM\",\n",
        "    \"K562 DNAse\": \"ENCSR921NMD\",\n",
        "    \"K562 H3k4me3\": \"ENCSR000DWD\",\n",
        "    \"K562 CTCF\": \"ENCSR000AKO\",\n",
        "    \"HepG2 RNA-seq\": \"ENCSR561FEE_P\",\n",
        "    \"HepG2 DNAse\": \"ENCSR000EJV\",\n",
        "    \"HepG2 H3k4me3\": \"ENCSR000AMP\",\n",
        "    \"HepG2 CTCF\": \"ENCSR000BIE\",\n",
        "}\n",
        "\n",
        "# Check that all tracks are available\n",
        "missing_tracks = [v for k,v in tracks_to_plot.items() if v not in bigwig_names]\n",
        "if missing_tracks:\n",
        "    raise ValueError(\n",
        "        f\"The following tracks are not available in bigwig_names: {missing_tracks}\\n\"\n",
        "        f\"Available tracks: {bigwig_names}\"\n",
        "    )\n",
        "    \n",
        "### 🧬 Select genomic elements to plot\n",
        "elements_to_plot = [\n",
        "    \"protein_coding_gene\",\n",
        "    \"exon\",\n",
        "    \"intron\",\n",
        "    \"splice_donor\",\n",
        "    \"splice_acceptor\",\n",
        "]\n",
        "\n",
        "# Check that all elements are available\n",
        "missing_elements = [elem for elem in elements_to_plot if elem not in bed_element_names]\n",
        "if missing_elements:\n",
        "    raise ValueError(\n",
        "        f\"The following elements are not available in bed_element_names: {missing_elements}\\n\"\n",
        "        f\"Available elements: {bed_element_names}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "7ba9a397",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x1300 with 13 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Extract functional tracks for selected tracks\n",
        "bigwig = out[\"bigwig_tracks_logits\"].detach().float().cpu().numpy()[0]  # shape: (sequence length, number of tracks)\n",
        "bigwig_tracks = {}  # track_name -> logits track\n",
        "for track_name, track_id in tracks_to_plot.items():\n",
        "    track_idx = bigwig_names.index(track_id)\n",
        "    bigwig_tracks[track_name] = bigwig[:, track_idx]\n",
        "\n",
        "# Genomic element predictions\n",
        "bed_logits = out[\"bed_tracks_logits\"].detach().float().cpu().numpy()[0] # shape: (sequence length, number of elements, 2 classes)\n",
        "# Convert annotation logits to probabilities using softmax\n",
        "exp = np.exp(bed_logits - bed_logits.max(axis=-1, keepdims=True))\n",
        "probs = exp / exp.sum(axis=-1, keepdims=True)\n",
        "\n",
        "# Extract positive class probabilities (index 1) for selected elements\n",
        "bed_probs = {}  # element_name -> probability track\n",
        "for element_name in elements_to_plot:\n",
        "    if element_name in bed_element_names:\n",
        "        element_idx = bed_element_names.index(element_name)\n",
        "        bed_probs[element_name] = probs[:, element_idx, 1]  # positive class probability\n",
        "\n",
        "# Model predicts for middle 37.5% of input sequence\n",
        "# So predictions start at: start + (window_len - window_len * 0.375) / 2 = start + window_len * 0.3125\n",
        "window_len = end - start\n",
        "prediction_start = start + int(window_len * 0.3125)\n",
        "prediction_end = prediction_start + int(window_len * 0.375)\n",
        "x = np.arange(prediction_start, prediction_end)\n",
        "\n",
        "# Combine bigwig and bed tracks\n",
        "all_tracks = {**bigwig_tracks, **bed_probs}\n",
        "\n",
        "# Plot all tracks together\n",
        "plot_tracks(all_tracks, prediction_start, prediction_end)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ce34dc4",
      "metadata": {},
      "source": [
        "# 💡 To improve\n",
        "- Add gene annotation at top"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "hf-finetune",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.18"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}