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README.md

@@ -13,7 +13,7 @@ size_categories:
 # enformer-data
 
 ## Dataset Summary
-This dataset contains the specific genomic intervals used for training, validating, and testing the Enformer model, a deep learning architecture for predicting functional genomic tracks from DNA sequence. The intervals are provided for both human and mouse genomes.
+This dataset contains the specific genomic intervals used for training, validating, and testing the Enformer model, a deep learning architecture for predicting functional genomic tracks from DNA sequence. The intervals are provided for both human and mouse genomes. As done in the publication, we modified the Basenji2 dataset by extending the input sequence to 196,608 bp from the original 131,072 bp using the hg38 reference genome.
 
 - **Source Publication:** [Avsec, Ž., et al. "Effective gene expression prediction from sequence by integrating long-range interactions." Nat Methods 18, 1196–1203 (2021).](https://www.nature.com/articles/s41592-021-01252-x)
 - **Genome Builds:**
@@ -21,11 +21,13 @@ This dataset contains the specific genomic intervals used for training, validati
   - Mouse: mm10
 
 
-
 ## Repository Content
-The repository includes two tab-separated values (TSV) files:
+The repository includes two tab-separated values (TSV) files and two Jupyter notebooks:
 1. `human_intervals.tsv`: 38,171 genomic regions (excluding header).
 2. `mouse_intervals.tsv`: 33,521 genomic regions (excluding header).
+3. `data_human.ipynb`: Code to create `human_intervals.tsv`.
+4. `data_mouse.ipynb`: Code to create `mouse_intervals.tsv`.
+
 
 ## Dataset Structure
 

data_human.ipynb

@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c7451d98-b6f2-4d5f-b57f-c3a800d937c9",
+   "metadata": {},
+   "source": [
+    "# Process and save Enformer genomic intervals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b30b38e-fdd0-4c04-8b50-576e8f09dc30",
+   "metadata": {},
+   "source": [
+    "## Set up wandb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d374bd3d-c131-4086-ad8b-a9b8c01bd1e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mavantikalal\u001b[0m (\u001b[33mgrelu\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+     ]
+    }
+   ],
+   "source": [
+    "import wandb\n",
+    "import pandas as pd\n",
+    "\n",
+    "wandb.login(host=\"https://api.wandb.ai\")\n",
+    "project_name='enformer'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b356a72f-628a-4fbb-9e95-df9c38d3bedf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.19.7"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/code/github/gReLU-applications/enformer/wandb/run-20250305_234119-9gws7tjk</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/grelu/enformer/runs/9gws7tjk' target=\"_blank\">prep-intervals-human</a></strong> to <a href='https://wandb.ai/grelu/enformer' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/grelu/enformer' target=\"_blank\">https://wandb.ai/grelu/enformer</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/grelu/enformer/runs/9gws7tjk' target=\"_blank\">https://wandb.ai/grelu/enformer/runs/9gws7tjk</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run = wandb.init(entity='grelu', project=project_name, job_type='preprocessing', name='prep-intervals-human',\n",
+    "    settings=wandb.Settings(\n",
+    "        program_relpath='data_human.ipynb',\n",
+    "        program_abspath='/code/github/gReLU-applications/enformer/data_human.ipynb'\n",
+    "    ))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82818c93-972b-4426-9420-b8e1957b3d4e",
+   "metadata": {},
+   "source": [
+    "## Load intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e2c21829-cc9f-4c74-a3a2-376cc9c88851",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sequences_path = '/gstore/data/resbioai/grelu/enformer/sequences.bed'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "02bc916e-2322-4676-bc93-c0838384a0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>chrom</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>split</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr18</td>\n",
+       "      <td>928386</td>\n",
+       "      <td>1059458</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>113630947</td>\n",
+       "      <td>113762019</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>18427720</td>\n",
+       "      <td>18558792</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   chrom      start        end  split\n",
+       "0  chr18     928386    1059458  train\n",
+       "1   chr4  113630947  113762019  train\n",
+       "2  chr11   18427720   18558792  train"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "intervals = pd.read_table(sequences_path, header=None)\n",
+    "intervals.columns = ['chrom', 'start', 'end', 'split']\n",
+    "intervals.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01824d66-4434-400a-a030-16d9237efde1",
+   "metadata": {},
+   "source": [
+    "## Resize intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f7915dc8-2cca-427a-918d-8bf12a995ec5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\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>chrom</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>split</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr18</td>\n",
+       "      <td>895618</td>\n",
+       "      <td>1092226</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>113598179</td>\n",
+       "      <td>113794787</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>18394952</td>\n",
+       "      <td>18591560</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr16</td>\n",
+       "      <td>85772913</td>\n",
+       "      <td>85969521</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr3</td>\n",
+       "      <td>158353420</td>\n",
+       "      <td>158550028</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   chrom      start        end  split\n",
+       "0  chr18     895618    1092226  train\n",
+       "1   chr4  113598179  113794787  train\n",
+       "2  chr11   18394952   18591560  train\n",
+       "3  chr16   85772913   85969521  train\n",
+       "4   chr3  158353420  158550028  train"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from grelu.sequence.utils import resize\n",
+    "intervals = resize(intervals, 196608)\n",
+    "intervals.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b079e2e-8ad5-4cac-808a-3f6ab84e1bb3",
+   "metadata": {},
+   "source": [
+    "## Save"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2f0b7a9f-3760-4ae1-8ef3-cee84090a41f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intervals.to_csv(\"human_intervals.tsv\", index=False, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d5b7b1fd-7c09-498d-9fdf-53332321ae25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Artifact human_intervals>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "artifact = wandb.Artifact('human_intervals', type='dataset')\n",
+    "artifact.add_file(local_path=\"human_intervals.tsv\", name=\"data.tsv\")\n",
+    "run.log_artifact(artifact)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bbfe1c93-c9fa-48e2-9291-5532597f761e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">prep-intervals-human</strong> at: <a href='https://wandb.ai/grelu/enformer/runs/9gws7tjk' target=\"_blank\">https://wandb.ai/grelu/enformer/runs/9gws7tjk</a><br> View project at: <a href='https://wandb.ai/grelu/enformer' target=\"_blank\">https://wandb.ai/grelu/enformer</a><br>Synced 6 W&B file(s), 0 media file(s), 2 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20250305_234119-9gws7tjk/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run.finish()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10041781-c2a0-44c0-b2db-388bc455645d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

data_mouse.ipynb

@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c7451d98-b6f2-4d5f-b57f-c3a800d937c9",
+   "metadata": {},
+   "source": [
+    "# Process and save Enformer genomic intervals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b30b38e-fdd0-4c04-8b50-576e8f09dc30",
+   "metadata": {},
+   "source": [
+    "## Set up wandb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d374bd3d-c131-4086-ad8b-a9b8c01bd1e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mavantikalal\u001b[0m (\u001b[33mgrelu\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+     ]
+    }
+   ],
+   "source": [
+    "import wandb\n",
+    "import pandas as pd\n",
+    "\n",
+    "wandb.login(host=\"https://api.wandb.ai\")\n",
+    "project_name='enformer'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b356a72f-628a-4fbb-9e95-df9c38d3bedf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.19.7"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/code/github/gReLU-applications/enformer/wandb/run-20250305_234301-of3e4sm1</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/grelu/enformer/runs/of3e4sm1' target=\"_blank\">prep-intervals-mouse</a></strong> to <a href='https://wandb.ai/grelu/enformer' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/grelu/enformer' target=\"_blank\">https://wandb.ai/grelu/enformer</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/grelu/enformer/runs/of3e4sm1' target=\"_blank\">https://wandb.ai/grelu/enformer/runs/of3e4sm1</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run = wandb.init(entity='grelu', project=project_name, job_type='preprocessing', name='prep-intervals-mouse',\n",
+    "    settings=wandb.Settings(\n",
+    "        program_relpath='data_mouse.ipynb',\n",
+    "        program_abspath='/code/github/gReLU-applications/enformer/data_mouse.ipynb'\n",
+    "    ))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82818c93-972b-4426-9420-b8e1957b3d4e",
+   "metadata": {},
+   "source": [
+    "## Load intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e2c21829-cc9f-4c74-a3a2-376cc9c88851",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sequences_path = '/gstore/data/resbioai/grelu/enformer/sequences-mouse.bed'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "02bc916e-2322-4676-bc93-c0838384a0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>chrom</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>split</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>34106647</td>\n",
+       "      <td>34237719</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr5</td>\n",
+       "      <td>52207747</td>\n",
+       "      <td>52338819</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr19</td>\n",
+       "      <td>20136862</td>\n",
+       "      <td>20267934</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   chrom     start       end  split\n",
+       "0   chr4  34106647  34237719  train\n",
+       "1   chr5  52207747  52338819  train\n",
+       "2  chr19  20136862  20267934  train"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "intervals = pd.read_table(sequences_path, header=None)\n",
+    "intervals.columns = ['chrom', 'start', 'end', 'split']\n",
+    "intervals.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01824d66-4434-400a-a030-16d9237efde1",
+   "metadata": {},
+   "source": [
+    "## Resize intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f7915dc8-2cca-427a-918d-8bf12a995ec5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\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>chrom</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>split</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>34073879</td>\n",
+       "      <td>34270487</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr5</td>\n",
+       "      <td>52174979</td>\n",
+       "      <td>52371587</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr19</td>\n",
+       "      <td>20104094</td>\n",
+       "      <td>20300702</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr14</td>\n",
+       "      <td>61812671</td>\n",
+       "      <td>62009279</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr15</td>\n",
+       "      <td>6559578</td>\n",
+       "      <td>6756186</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   chrom     start       end  split\n",
+       "0   chr4  34073879  34270487  train\n",
+       "1   chr5  52174979  52371587  train\n",
+       "2  chr19  20104094  20300702  train\n",
+       "3  chr14  61812671  62009279  train\n",
+       "4  chr15   6559578   6756186  train"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from grelu.sequence.utils import resize\n",
+    "intervals = resize(intervals, 196608)\n",
+    "intervals.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b079e2e-8ad5-4cac-808a-3f6ab84e1bb3",
+   "metadata": {},
+   "source": [
+    "## Save"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2f0b7a9f-3760-4ae1-8ef3-cee84090a41f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intervals.to_csv(\"mouse_intervals.tsv\", index=False, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d5b7b1fd-7c09-498d-9fdf-53332321ae25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Artifact mouse_intervals>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "artifact = wandb.Artifact('mouse_intervals', type='dataset')\n",
+    "artifact.add_file(local_path=\"mouse_intervals.tsv\", name=\"data.tsv\")\n",
+    "run.log_artifact(artifact)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bbfe1c93-c9fa-48e2-9291-5532597f761e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">prep-intervals-mouse</strong> at: <a href='https://wandb.ai/grelu/enformer/runs/of3e4sm1' target=\"_blank\">https://wandb.ai/grelu/enformer/runs/of3e4sm1</a><br> View project at: <a href='https://wandb.ai/grelu/enformer' target=\"_blank\">https://wandb.ai/grelu/enformer</a><br>Synced 6 W&B file(s), 0 media file(s), 2 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20250305_234301-of3e4sm1/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run.finish()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10041781-c2a0-44c0-b2db-388bc455645d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}