{
  "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": null,
      "id": "38ec1971",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Login to HuggingFace (required for gated models)\n",
        "from huggingface_hub import login\n",
        "login()"
      ]
    },
    {
      "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": null,
      "id": "2354e2aa",
      "metadata": {},
      "outputs": [],
      "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_post\" # options: \"InstaDeepAI/NTv3_100M_post\" or \"InstaDeepAI/NTv3_650M_post\"\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_732_768\n",
        "# Limiting to 32kb to work on Google Colab T4 GPU -> increase up to 1 million nucleotides if you have a better GPU\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: 32768\n",
            "Cropped sequence length: 32768, 256.0 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": [
              "NTv3PostTrained(\n",
              "  (core): NTv3PostTrainedCore(\n",
              "    (embed_layer): Embedding(11, 16)\n",
              "    (stem): Stem(\n",
              "      (conv): Conv1d(16, 768, kernel_size=(15,), stride=(1,), padding=same)\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",
              "    (lm_head): ModuleDict(\n",
              "      (hidden_layers): ModuleList()\n",
              "      (head): Linear(in_features=768, out_features=11, bias=True)\n",
              "    )\n",
              "    (cond_tables): ModuleList(\n",
              "      (0): Embedding(30, 16)\n",
              "    )\n",
              "    (conditions_heads): ModuleList(\n",
              "      (0): Linear(in_features=768, out_features=30, bias=True)\n",
              "    )\n",
              "    (bigwig_head): MultiSpeciesHead(\n",
              "      (species_heads): ModuleList(\n",
              "        (0-4): 5 x ZeroHead()\n",
              "        (5): LinearHead(\n",
              "          (layer_norm): LayerNormFP32((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): LayerNormFP32((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): LayerNormFP32((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): LayerNormFP32((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): LayerNormFP32((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): LayerNormFP32((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): LayerNormFP32((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): LayerNormFP32((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): LayerNormFP32((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): LayerNormFP32((768,), eps=1e-05, elementwise_affine=True)\n",
              "      (head): Linear(in_features=768, out_features=42, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Load model\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",
        "# 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 human. 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",
        "cfg = AutoConfig.from_pretrained(model_name, trust_remote_code=True)\n",
        "bigwig_names = cfg.bigwigs_per_species[species]\n",
        "print(f\"{len(bigwig_names)} functional tracks for {species}. 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, 12288, 7362)\n",
            "bed_tracks_logits: (1, 12288, 21, 2)\n",
            "language model logits: (1, 32768, 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 = model.encode_species(species).to(device)\n",
        "\n",
        "# Run inference\n",
        "out = model(\n",
        "    input_ids=input_ids,\n",
        "    species_ids=species_ids,\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": 11,
      "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"
      ]
    }
  ],
  "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
}