diff --git a/.github/workflows/bump.yaml b/.github/workflows/bump.yaml
index 0e83d994..225c2790 100644
--- a/.github/workflows/bump.yaml
+++ b/.github/workflows/bump.yaml
@@ -22,6 +22,6 @@ jobs:
         uses: softprops/action-gh-release@v2
         with:
           body_path: "body.md"
-          tag_name: ${{ env.REVISION }}
+          tag_name: v${{ env.REVISION }}
         env:
           GITHUB_TOKEN: ${{ secrets.COMMITIZEN }}
diff --git a/CHANGELOG.md b/docs/source/changelog.md
similarity index 98%
rename from CHANGELOG.md
rename to docs/source/changelog.md
index e0e51bda..34eb10dd 100644
--- a/CHANGELOG.md
+++ b/docs/source/changelog.md
@@ -852,6 +852,15 @@
 
 ## v0.9.0 (2025-03-06)
 
+This is a breaking change for GVL. Users should view the ["What's a `gvl.Dataset`?"](https://genvarloader.readthedocs.io/en/latest/dataset.html) page in the documentation for details, but major breaks include:
+
+- removed the `length` argument from `gvl.write()`. Regions/BED files are now used as-is. If you want uniform length regions centered on inputs/peaks as before, preprocess your BED file with `gvl.with_length`.
+- changed `Dataset.output_length` from a property to a dynamic setting with behavior describe in the "What's a gvl.Dataset?" page.
+- changed track output shape to have a track axis.
+- Datasets are now deterministic by default.
+
+As a result of these changes, GVL seamlessly supports ragged length output and also paves the way for on-the-fly splicing. Since many changes were made, I wouldn't be surprised if a few bugs crop up despite my best efforts -- please leave issues if so!
+
 ### Feat
 
 - option to return ragged data from gvl.Dataset. output_length is set dynamically. fix: hap reconstruction matches bcftools. change default for Dataset.deterministic from False to True. change track output from a list of arrays to having a track dimension i.e. from shape (b [p] l) to (b t [p] l). docs: add dataset.md, faq.md and overhaul geuvadis.ipynb to be simpler and reflect changes in API.
diff --git a/docs/source/changelog.md.j2 b/docs/source/changelog.md.j2
new file mode 100644
index 00000000..46209d0a
--- /dev/null
+++ b/docs/source/changelog.md.j2
@@ -0,0 +1,21 @@
+# Changelog
+
+{% for entry in tree %}
+
+## {{ entry.version }}{% if entry.date %} ({{ entry.date }}){% endif %}
+
+{% for change_key, changes in entry.changes.items() %}
+
+{% if change_key %}
+### {{ change_key }}
+{% endif %}
+
+{% for change in changes %}
+{% if change.scope %}
+- **{{ change.scope }}**: {{ change.message }}
+{% elif change.message %}
+- {{ change.message }}
+{% endif %}
+{% endfor %}
+{% endfor %}
+{% endfor %}
\ No newline at end of file
diff --git a/docs/source/index.md b/docs/source/index.md
index 73136df9..08c3a0df 100644
--- a/docs/source/index.md
+++ b/docs/source/index.md
@@ -6,6 +6,7 @@ write
 geuvadis
 faq
 api
+changelog
 ```
 
 # GenVarLoader
diff --git a/docs/source/splicing.ipynb b/docs/source/splicing.ipynb
new file mode 100644
index 00000000..81a293d8
--- /dev/null
+++ b/docs/source/splicing.ipynb
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import genvarloader as gvl\n",
+    "import polars as pl\n",
+    "import pooch\n",
+    "from pathlib import Path\n",
+    "from tempfile import TemporaryDirectory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GRCh38 chromosome 22 sequence\n",
+    "reference = pooch.retrieve(\n",
+    "    url=\"https://ftp.ensembl.org/pub/release-112/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.chromosome.22.fa.gz\",\n",
+    "    known_hash=\"sha256:974f97ac8ef7ffae971b63b47608feda327403be40c27e391ee4a1a78b800df5\",\n",
+    "    progressbar=True,\n",
+    ")\n",
+    "if not Path(f\"{reference[:-3]}.bgz\").exists():\n",
+    "    !gzip -dc {reference} | bgzip > {reference[:-3]}.bgz\n",
+    "reference = reference[:-3] + \".bgz\"\n",
+    "\n",
+    "# PLINK 2 files\n",
+    "variants = pooch.retrieve(\n",
+    "    url=\"doi:10.5281/zenodo.13656224/1kGP.chr22.pgen\",\n",
+    "    known_hash=\"md5:31aba970e35f816701b2b99118dfc2aa\",\n",
+    "    progressbar=True,\n",
+    "    fname=\"1kGP.chr22.pgen\",\n",
+    ")\n",
+    "pooch.retrieve(\n",
+    "    url=\"doi:10.5281/zenodo.13656224/1kGP.chr22.psam\",\n",
+    "    known_hash=\"md5:eefa7aad5acffe62bf41df0a4600129c\",\n",
+    "    progressbar=True,\n",
+    "    fname=\"1kGP.chr22.psam\",\n",
+    ")\n",
+    "pooch.retrieve(\n",
+    "    url=\"doi:10.5281/zenodo.13656224/1kGP.chr22.pvar\",\n",
+    "    known_hash=\"md5:5f922af91c1a2f6822e2f1bb4469d12b\",\n",
+    "    progressbar=True,\n",
+    "    fname=\"1kGP.chr22.pvar\",\n",
+    ")\n",
+    "\n",
+    "# GRCh38 Gencode GFF3\n",
+    "gff = pooch.retrieve(\n",
+    "    url=\"https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.annotation.gff3.gz\",\n",
+    "    known_hash=\"md5:918948a059c72088e44861835ade8a89\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>seqname</th><th>source</th><th>feature</th><th>start</th><th>end</th><th>score</th><th>strand</th><th>frame</th><th>attribute</th></tr><tr><td>str</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;chr1&quot;</td><td>&quot;HAVANA&quot;</td><td>&quot;gene&quot;</td><td>11121</td><td>24894</td><td>&quot;.&quot;</td><td>&quot;+&quot;</td><td>&quot;.&quot;</td><td>&quot;ID=ENSG00000290825.2;gene_id=E…</td></tr><tr><td>&quot;chr1&quot;</td><td>&quot;HAVANA&quot;</td><td>&quot;transcript&quot;</td><td>11121</td><td>14413</td><td>&quot;.&quot;</td><td>&quot;+&quot;</td><td>&quot;.&quot;</td><td>&quot;ID=ENST00000832824.1;Parent=EN…</td></tr><tr><td>&quot;chr1&quot;</td><td>&quot;HAVANA&quot;</td><td>&quot;exon&quot;</td><td>11121</td><td>11211</td><td>&quot;.&quot;</td><td>&quot;+&quot;</td><td>&quot;.&quot;</td><td>&quot;ID=exon:ENST00000832824.1:1;Pa…</td></tr><tr><td>&quot;chr1&quot;</td><td>&quot;HAVANA&quot;</td><td>&quot;exon&quot;</td><td>12010</td><td>12227</td><td>&quot;.&quot;</td><td>&quot;+&quot;</td><td>&quot;.&quot;</td><td>&quot;ID=exon:ENST00000832824.1:2;Pa…</td></tr><tr><td>&quot;chr1&quot;</td><td>&quot;HAVANA&quot;</td><td>&quot;exon&quot;</td><td>12613</td><td>12721</td><td>&quot;.&quot;</td><td>&quot;+&quot;</td><td>&quot;.&quot;</td><td>&quot;ID=exon:ENST00000832824.1:3;Pa…</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 9)\n",
+       "┌─────────┬────────┬────────────┬───────┬───┬───────┬────────┬───────┬─────────────────────────────┐\n",
+       "│ seqname ┆ source ┆ feature    ┆ start ┆ … ┆ score ┆ strand ┆ frame ┆ attribute                   │\n",
+       "│ ---     ┆ ---    ┆ ---        ┆ ---   ┆   ┆ ---   ┆ ---    ┆ ---   ┆ ---                         │\n",
+       "│ str     ┆ str    ┆ str        ┆ i64   ┆   ┆ str   ┆ str    ┆ str   ┆ str                         │\n",
+       "╞═════════╪════════╪════════════╪═══════╪═══╪═══════╪════════╪═══════╪═════════════════════════════╡\n",
+       "│ chr1    ┆ HAVANA ┆ gene       ┆ 11121 ┆ … ┆ .     ┆ +      ┆ .     ┆ ID=ENSG00000290825.2;gene_i │\n",
+       "│         ┆        ┆            ┆       ┆   ┆       ┆        ┆       ┆ d=E…                        │\n",
+       "│ chr1    ┆ HAVANA ┆ transcript ┆ 11121 ┆ … ┆ .     ┆ +      ┆ .     ┆ ID=ENST00000832824.1;Parent │\n",
+       "│         ┆        ┆            ┆       ┆   ┆       ┆        ┆       ┆ =EN…                        │\n",
+       "│ chr1    ┆ HAVANA ┆ exon       ┆ 11121 ┆ … ┆ .     ┆ +      ┆ .     ┆ ID=exon:ENST00000832824.1:1 │\n",
+       "│         ┆        ┆            ┆       ┆   ┆       ┆        ┆       ┆ ;Pa…                        │\n",
+       "│ chr1    ┆ HAVANA ┆ exon       ┆ 12010 ┆ … ┆ .     ┆ +      ┆ .     ┆ ID=exon:ENST00000832824.1:2 │\n",
+       "│         ┆        ┆            ┆       ┆   ┆       ┆        ┆       ┆ ;Pa…                        │\n",
+       "│ chr1    ┆ HAVANA ┆ exon       ┆ 12613 ┆ … ┆ .     ┆ +      ┆ .     ┆ ID=exon:ENST00000832824.1:3 │\n",
+       "│         ┆        ┆            ┆       ┆   ┆       ┆        ┆       ┆ ;Pa…                        │\n",
+       "└─────────┴────────┴────────────┴───────┴───┴───────┴────────┴───────┴─────────────────────────────┘"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gff_df = pl.read_csv(\n",
+    "    gff,\n",
+    "    separator=\"\\t\",\n",
+    "    comment_prefix=\"#\",\n",
+    "    has_header=False,\n",
+    "    new_columns=[\n",
+    "        \"seqname\",\n",
+    "        \"source\",\n",
+    "        \"feature\",\n",
+    "        \"start\",\n",
+    "        \"end\",\n",
+    "        \"score\",\n",
+    "        \"strand\",\n",
+    "        \"frame\",\n",
+    "        \"attribute\",\n",
+    "    ],\n",
+    ")\n",
+    "gff_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>chrom</th><th>source</th><th>feature</th><th>chromStart</th><th>chromEnd</th><th>score</th><th>strand</th><th>frame</th><th>attribute</th><th>gene</th><th>exon_num</th></tr><tr><td>str</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;chr22&quot;</td><td>&quot;HAVANA&quot;</td><td>&quot;exon&quot;</td><td>10529036</td><td>10529164</td><td>&quot;.&quot;</td><td>&quot;-&quot;</td><td>&quot;.&quot;</td><td>&quot;ID=exon:ENST00000724296.1:1;Pa…</td><td>&quot;ENSG00000294541.1&quot;</td><td>&quot;1&quot;</td></tr><tr><td>&quot;chr22&quot;</td><td>&quot;HAVANA&quot;</td><td>&quot;exon&quot;</td><td>10527853</td><td>10528040</td><td>&quot;.&quot;</td><td>&quot;-&quot;</td><td>&quot;.&quot;</td><td>&quot;ID=exon:ENST00000724296.1:2;Pa…</td><td>&quot;ENSG00000294541.1&quot;</td><td>&quot;2&quot;</td></tr><tr><td>&quot;chr22&quot;</td><td>&quot;HAVANA&quot;</td><td>&quot;exon&quot;</td><td>10524345</td><td>10524446</td><td>&quot;.&quot;</td><td>&quot;-&quot;</td><td>&quot;.&quot;</td><td>&quot;ID=exon:ENST00000724296.1:3;Pa…</td><td>&quot;ENSG00000294541.1&quot;</td><td>&quot;3&quot;</td></tr><tr><td>&quot;chr22&quot;</td><td>&quot;ENSEMBL&quot;</td><td>&quot;exon&quot;</td><td>10736171</td><td>10736283</td><td>&quot;.&quot;</td><td>&quot;-&quot;</td><td>&quot;.&quot;</td><td>&quot;ID=exon:ENST00000615943.1:1;Pa…</td><td>&quot;ENSG00000277248.1&quot;</td><td>&quot;1&quot;</td></tr><tr><td>&quot;chr22&quot;</td><td>&quot;HAVANA&quot;</td><td>&quot;exon&quot;</td><td>10742050</td><td>10742191</td><td>&quot;.&quot;</td><td>&quot;+&quot;</td><td>&quot;.&quot;</td><td>&quot;ID=exon:ENST00000779064.1:1;Pa…</td><td>&quot;ENSG00000301473.1&quot;</td><td>&quot;1&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 11)\n",
+       "┌───────┬─────────┬─────────┬────────────┬───┬───────┬─────────────────┬────────────────┬──────────┐\n",
+       "│ chrom ┆ source  ┆ feature ┆ chromStart ┆ … ┆ frame ┆ attribute       ┆ gene           ┆ exon_num │\n",
+       "│ ---   ┆ ---     ┆ ---     ┆ ---        ┆   ┆ ---   ┆ ---             ┆ ---            ┆ ---      │\n",
+       "│ str   ┆ str     ┆ str     ┆ i64        ┆   ┆ str   ┆ str             ┆ str            ┆ str      │\n",
+       "╞═══════╪═════════╪═════════╪════════════╪═══╪═══════╪═════════════════╪════════════════╪══════════╡\n",
+       "│ chr22 ┆ HAVANA  ┆ exon    ┆ 10529036   ┆ … ┆ .     ┆ ID=exon:ENST000 ┆ ENSG0000029454 ┆ 1        │\n",
+       "│       ┆         ┆         ┆            ┆   ┆       ┆ 00724296.1:1;Pa ┆ 1.1            ┆          │\n",
+       "│       ┆         ┆         ┆            ┆   ┆       ┆ …               ┆                ┆          │\n",
+       "│ chr22 ┆ HAVANA  ┆ exon    ┆ 10527853   ┆ … ┆ .     ┆ ID=exon:ENST000 ┆ ENSG0000029454 ┆ 2        │\n",
+       "│       ┆         ┆         ┆            ┆   ┆       ┆ 00724296.1:2;Pa ┆ 1.1            ┆          │\n",
+       "│       ┆         ┆         ┆            ┆   ┆       ┆ …               ┆                ┆          │\n",
+       "│ chr22 ┆ HAVANA  ┆ exon    ┆ 10524345   ┆ … ┆ .     ┆ ID=exon:ENST000 ┆ ENSG0000029454 ┆ 3        │\n",
+       "│       ┆         ┆         ┆            ┆   ┆       ┆ 00724296.1:3;Pa ┆ 1.1            ┆          │\n",
+       "│       ┆         ┆         ┆            ┆   ┆       ┆ …               ┆                ┆          │\n",
+       "│ chr22 ┆ ENSEMBL ┆ exon    ┆ 10736171   ┆ … ┆ .     ┆ ID=exon:ENST000 ┆ ENSG0000027724 ┆ 1        │\n",
+       "│       ┆         ┆         ┆            ┆   ┆       ┆ 00615943.1:1;Pa ┆ 8.1            ┆          │\n",
+       "│       ┆         ┆         ┆            ┆   ┆       ┆ …               ┆                ┆          │\n",
+       "│ chr22 ┆ HAVANA  ┆ exon    ┆ 10742050   ┆ … ┆ .     ┆ ID=exon:ENST000 ┆ ENSG0000030147 ┆ 1        │\n",
+       "│       ┆         ┆         ┆            ┆   ┆       ┆ 00779064.1:1;Pa ┆ 3.1            ┆          │\n",
+       "│       ┆         ┆         ┆            ┆   ┆       ┆ …               ┆                ┆          │\n",
+       "└───────┴─────────┴─────────┴────────────┴───┴───────┴─────────────────┴────────────────┴──────────┘"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chr22_genes = (\n",
+    "    gff_df.filter(pl.col(\"seqname\") == \"chr22\")\n",
+    "    .filter(pl.col(\"feature\") == \"exon\")\n",
+    "    .with_columns(\n",
+    "        gene=pl.col(\"attribute\").str.extract(r\"gene_id=(.*?);\"),\n",
+    "        exon_num=pl.col(\"attribute\").str.extract(r\"exon_number=(.*?);\"),\n",
+    "    )\n",
+    "    .rename({\"seqname\": \"chrom\", \"start\": \"chromStart\", \"end\": \"chromEnd\"})\n",
+    ")\n",
+    "chr22_genes.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26cf9d1b27a94b2cb454d8e32a90f98c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_path = TemporaryDirectory(suffix=\".gvl\").name\n",
+    "gvl.write(ds_path, chr22_genes[:100], variants)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = gvl.Dataset.open(ds_path, reference, splice_info=(\"gene\", \"exon_num\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "cannot slice RegularArray (of length 1) with [3]: index out of range while attempting to get index 3 (in compiled code: https://github.com/scikit-hep/awkward/blob/awkward-cpp-44/awkward-cpp/src/cpu-kernels/awkward_RegularArray_getitem_next_array_regularize.cpp#L19)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_spliced\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mS1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m/carter/users/dlaub/projects/GenVarLoader/python/genvarloader/_dataset/__init__.py:1358\u001b[0m, in \u001b[0;36mDataset._getitem_spliced\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m   1344\u001b[0m new_lengths \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39madd\u001b[38;5;241m.\u001b[39mreduceat(\n\u001b[1;32m   1345\u001b[0m     haps\u001b[38;5;241m.\u001b[39mlengths,\n\u001b[1;32m   1346\u001b[0m     _lengths_to_offsets(ak\u001b[38;5;241m.\u001b[39mcount(splice_idx, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mto_numpy())[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m   1347\u001b[0m     axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,  \u001b[38;5;66;03m# this might not generalized to ndim indices\u001b[39;00m\n\u001b[1;32m   1348\u001b[0m )\n\u001b[1;32m   1349\u001b[0m b, p \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m   1350\u001b[0m     ak\u001b[38;5;241m.\u001b[39mflatten(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\u001b[38;5;241m.\u001b[39mto_numpy()\n\u001b[1;32m   1351\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m ak\u001b[38;5;241m.\u001b[39mbroadcast_arrays(\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1354\u001b[0m     )\n\u001b[1;32m   1355\u001b[0m ]\n\u001b[1;32m   1357\u001b[0m haps \u001b[38;5;241m=\u001b[39m Ragged\u001b[38;5;241m.\u001b[39mfrom_lengths(\n\u001b[0;32m-> 1358\u001b[0m     ak\u001b[38;5;241m.\u001b[39mflatten(\u001b[43mhaps\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_awkward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m]\u001b[49m)\u001b[38;5;241m.\u001b[39mto_numpy()\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mS1\u001b[39m\u001b[38;5;124m\"\u001b[39m), new_lengths\n\u001b[1;32m   1359\u001b[0m )\n\u001b[1;32m   1361\u001b[0m ragged_out\u001b[38;5;241m.\u001b[39mappend(haps)\n\u001b[1;32m   1362\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_annotations:\n",
+      "File \u001b[0;32m/carter/users/dlaub/projects/GenVarLoader/.pixi/envs/docs/lib/python3.12/site-packages/awkward/highlevel.py:1105\u001b[0m, in \u001b[0;36mArray.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m    676\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, where):\n\u001b[1;32m    677\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    678\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m    679\u001b[0m \u001b[38;5;124;03m        where (many types supported; see below): Index of positions to\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1103\u001b[0m \u001b[38;5;124;03m    have the same dimension as the array being indexed.\u001b[39;00m\n\u001b[1;32m   1104\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1105\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ak\u001b[38;5;241m.\u001b[39m_errors\u001b[38;5;241m.\u001b[39mSlicingErrorContext(\u001b[38;5;28mself\u001b[39m, where):\n\u001b[1;32m   1106\u001b[0m         \u001b[38;5;66;03m# Handle named axis\u001b[39;00m\n\u001b[1;32m   1107\u001b[0m         (_, ndim) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_layout\u001b[38;5;241m.\u001b[39mminmax_depth\n\u001b[1;32m   1108\u001b[0m         named_axis \u001b[38;5;241m=\u001b[39m _get_named_axis(\u001b[38;5;28mself\u001b[39m)\n",
+      "File \u001b[0;32m/carter/users/dlaub/projects/GenVarLoader/.pixi/envs/docs/lib/python3.12/site-packages/awkward/_errors.py:80\u001b[0m, in \u001b[0;36mErrorContext.__exit__\u001b[0;34m(self, exception_type, exception_value, traceback)\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slate\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mclear()\n\u001b[1;32m     79\u001b[0m     \u001b[38;5;66;03m# Handle caught exception\u001b[39;00m\n\u001b[0;32m---> 80\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecorate_exception(exception_type, exception_value)\n\u001b[1;32m     81\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     82\u001b[0m     \u001b[38;5;66;03m# Step out of the way so that another ErrorContext can become primary.\u001b[39;00m\n\u001b[1;32m     83\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprimary() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n",
+      "File \u001b[0;32m/carter/users/dlaub/projects/GenVarLoader/.pixi/envs/docs/lib/python3.12/site-packages/awkward/highlevel.py:1113\u001b[0m, in \u001b[0;36mArray.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   1109\u001b[0m where \u001b[38;5;241m=\u001b[39m _normalize_named_slice(named_axis, where, ndim)\n\u001b[1;32m   1111\u001b[0m NamedAxis\u001b[38;5;241m.\u001b[39mmapping \u001b[38;5;241m=\u001b[39m named_axis\n\u001b[0;32m-> 1113\u001b[0m indexed_layout \u001b[38;5;241m=\u001b[39m prepare_layout(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_layout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNamedAxis\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m NamedAxis\u001b[38;5;241m.\u001b[39mmapping:\n\u001b[1;32m   1116\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ak\u001b[38;5;241m.\u001b[39moperations\u001b[38;5;241m.\u001b[39mak_with_named_axis\u001b[38;5;241m.\u001b[39m_impl(\n\u001b[1;32m   1117\u001b[0m         indexed_layout,\n\u001b[1;32m   1118\u001b[0m         named_axis\u001b[38;5;241m=\u001b[39mNamedAxis\u001b[38;5;241m.\u001b[39mmapping,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1121\u001b[0m         attrs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attrs,\n\u001b[1;32m   1122\u001b[0m     )\n",
+      "File \u001b[0;32m/carter/users/dlaub/projects/GenVarLoader/.pixi/envs/docs/lib/python3.12/site-packages/awkward/contents/content.py:649\u001b[0m, in \u001b[0;36mContent._getitem\u001b[0;34m(self, where, named_axis)\u001b[0m\n\u001b[1;32m    640\u001b[0m named_axis\u001b[38;5;241m.\u001b[39mmapping \u001b[38;5;241m=\u001b[39m _named_axis\n\u001b[1;32m    642\u001b[0m \u001b[38;5;28mnext\u001b[39m \u001b[38;5;241m=\u001b[39m ak\u001b[38;5;241m.\u001b[39mcontents\u001b[38;5;241m.\u001b[39mRegularArray(\n\u001b[1;32m    643\u001b[0m     this,\n\u001b[1;32m    644\u001b[0m     this\u001b[38;5;241m.\u001b[39mlength,\n\u001b[1;32m    645\u001b[0m     \u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m    646\u001b[0m     parameters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    647\u001b[0m )\n\u001b[0;32m--> 649\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_next\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnextwhere\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnextwhere\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    651\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m unknown_length \u001b[38;5;129;01mand\u001b[39;00m out\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    652\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m out\u001b[38;5;241m.\u001b[39m_getitem_nothing()\n",
+      "File \u001b[0;32m/carter/users/dlaub/projects/GenVarLoader/.pixi/envs/docs/lib/python3.12/site-packages/awkward/contents/regulararray.py:575\u001b[0m, in \u001b[0;36mRegularArray._getitem_next\u001b[0;34m(self, head, tail, advanced)\u001b[0m\n\u001b[1;32m    573\u001b[0m regular_flathead \u001b[38;5;241m=\u001b[39m ak\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mIndex64\u001b[38;5;241m.\u001b[39mempty(flathead\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], index_nplike)\n\u001b[1;32m    574\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m regular_flathead\u001b[38;5;241m.\u001b[39mnplike \u001b[38;5;129;01mis\u001b[39;00m index_nplike\n\u001b[0;32m--> 575\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_index_error\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    576\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_backend\u001b[49m\u001b[43m[\u001b[49m\n\u001b[1;32m    577\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mawkward_RegularArray_getitem_next_array_regularize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    578\u001b[0m \u001b[43m        \u001b[49m\u001b[43mregular_flathead\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    579\u001b[0m \u001b[43m        \u001b[49m\u001b[43mflathead\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    580\u001b[0m \u001b[43m    \u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    581\u001b[0m \u001b[43m        \u001b[49m\u001b[43mregular_flathead\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    582\u001b[0m \u001b[43m        \u001b[49m\u001b[43mflathead\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    583\u001b[0m \u001b[43m        \u001b[49m\u001b[43mflathead\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    584\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    585\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    586\u001b[0m \u001b[43m    \u001b[49m\u001b[43mslicer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    587\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    589\u001b[0m nexthead, nexttail \u001b[38;5;241m=\u001b[39m ak\u001b[38;5;241m.\u001b[39m_slicing\u001b[38;5;241m.\u001b[39mhead_tail(tail)\n\u001b[1;32m    590\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m advanced \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m    591\u001b[0m     advanced\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m unknown_length \u001b[38;5;129;01mand\u001b[39;00m advanced\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m    592\u001b[0m ):\n",
+      "File \u001b[0;32m/carter/users/dlaub/projects/GenVarLoader/.pixi/envs/docs/lib/python3.12/site-packages/awkward/contents/content.py:295\u001b[0m, in \u001b[0;36mContent._maybe_index_error\u001b[0;34m(self, error, slicer)\u001b[0m\n\u001b[1;32m    293\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    294\u001b[0m     message \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mformat_kernel_error(error)\n\u001b[0;32m--> 295\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ak\u001b[38;5;241m.\u001b[39m_errors\u001b[38;5;241m.\u001b[39mindex_error(\u001b[38;5;28mself\u001b[39m, slicer, message)\n",
+      "\u001b[0;31mIndexError\u001b[0m: cannot slice RegularArray (of length 1) with [3]: index out of range while attempting to get index 3 (in compiled code: https://github.com/scikit-hep/awkward/blob/awkward-cpp-44/awkward-cpp/src/cpu-kernels/awkward_RegularArray_getitem_next_array_regularize.cpp#L19)",
+      "\u001b[0m\nThis error occurred while attempting to slice\n\n    <Array [[[71, 67, 84, ..., 84, 67, 71], ...]] type='1 * 2 * var * uint8'>\n\nwith\n\n    (array([3], dtype=uint32), array([0]))"
+     ]
+    }
+   ],
+   "source": [
+    "ds._getitem_spliced([1])[0].view(\"S1\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "GVL Docs",
+   "language": "python",
+   "name": "gvl-docs"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pixi.lock b/pixi.lock
index 41c7830c..af217d13 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -12,24 +12,24 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/_x86_64-microarch-level-1-2_x86_64.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda
+  sha256: ed10c9283974d311855ae08a16dfd7e56241fac632aec3b92e3cfe73cff31038
+  md5: f6ebe2cb3f82ba6c057dde5d9debe4f7
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 14780
+  timestamp: 1734229004433
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda
+  sha256: 6b250f3e59db07c2514057944a3ea2044d6a8cdde8a47b6497c254520fade1ee
+  md5: 8035c64cb77ed555e3f150b7b3972480
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 19901
+  timestamp: 1727794976192
 - conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2
   sha256: a4e34c710eeb26945bdbdaba82d3d74f60a78f54a874ec10d373811a5d217535
   md5: 4cb3ad778ec2d5a7acbdf254eb1c42ae
@@ -10003,21 +8667,6 @@ packages:
   purls: []
   size: 92286
   timestamp: 1727963153079
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_1.conda
-  sha256: 96f96336f76443f5efb05f8a7232cc62f8fff969c27d03aa4aae181745f6f961
-  md5: 0316e8d0e00c00631a6de89207db5b09
-  depends:
-  - __glibc >=2.17,<3.0.a0
-  - cffi >=1.11
-  - libgcc >=13
-  - python >=3.10,<3.11.0a0
-  - python_abi 3.10.* *_cp310
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=hash-mapping
-  size: 720871
-  timestamp: 1741853413225
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda
   sha256: f9b76c2f8a0f96e656843553272e547170182f5b8aba1a6bcba28f7611d87c23
   md5: f9254b5b0193982416b91edcb4b2676f
@@ -10048,21 +8697,6 @@ packages:
   - pkg:pypi/zstandard?source=hash-mapping
   size: 731883
   timestamp: 1745869796301
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312h66e93f0_1.conda
-  sha256: b4fd6bd1cb87a183a8bbe85b4e87a1e7c51473309d0d82cd88d38fb021bcf41e
-  md5: d28b82fcc8d1b462b595af4b15a6cdcf
-  depends:
-  - __glibc >=2.17,<3.0.a0
-  - cffi >=1.11
-  - libgcc >=13
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=hash-mapping
-  size: 731658
-  timestamp: 1741853415477
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312h66e93f0_2.conda
   sha256: ff62d2e1ed98a3ec18de7e5cf26c0634fd338cb87304cf03ad8cbafe6fe674ba
   md5: 630db208bc7bbb96725ce9832c7423bb
diff --git a/pixi.toml b/pixi.toml
index 283702e3..3be60b8b 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -58,6 +58,8 @@ ipywidgets = "*"
 sphinx-book-theme = "*"
 sphinx-autobuild = "*"
 sphinx-autodoc-typehints = "*"
+seaborn = "*"
+fast-histogram = "*"
 
 [feature.pytorch-cpu.dependencies]
 pytorch-cpu = ">=2,<3"
@@ -97,6 +99,7 @@ gen = "python tests/data/generate_ground_truth.py"
 test = { cmd = "pytest tests && cargo test --release", depends-on = ["gen"] }
 
 [feature.docs.tasks]
+install-e = "uv pip install -e /cellar/users/dlaub/projects/ML4GLand/SeqPro -e /cellar/users/dlaub/projects/genoray -e ."
 i-kernel = "ipython kernel install --user --name 'gvl-docs' --display-name 'GVL Docs'"
 i-kernel-gpu = "ipython kernel install --user --name 'gvl-docs-gpu' --display-name 'GVL Docs GPU'"
 doc = "cd docs && make clean && make html"
diff --git a/pyproject.toml b/pyproject.toml
index 0e8017ce..a60c21df 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -66,6 +66,7 @@ reportUninitializedInstanceVariable = false
 [tool.maturin]
 python-source = "python"
 features = ["pyo3/extension-module"]
+# compatibility = "manylinux_2_28"
 
 [tool.pytest.ini_options]
 filterwarnings = [
@@ -83,6 +84,10 @@ legacy_tag_formats = ['v$version']
 version_scheme = "semver2"
 version_provider = "pep621"
 update_changelog_on_bump = true
+changelog_file = 'docs/source/changelog.md'
+changlog_incremental = true
+changelog_start_rev = "v0.9.1"
+template = "docs/source/changelog.md.j2"
 major_version_zero = true
 allowed_prefixes = ["Merge", "Revert", "Pull request", "fixup!", "squash!", "[pre-commit.ci]"]
 
diff --git a/python/genvarloader/_dataset/_genotypes.py b/python/genvarloader/_dataset/_genotypes.py
index a35b579f..a96bb203 100644
--- a/python/genvarloader/_dataset/_genotypes.py
+++ b/python/genvarloader/_dataset/_genotypes.py
@@ -100,7 +100,7 @@ def get_diffs_sparse(
     return diffs
 
 
-@nb.njit(parallel=True, nogil=True, cache=True)
+# @nb.njit(parallel=True, nogil=True, cache=True)
 def reconstruct_haplotypes_from_sparse(
     out: NDArray[np.uint8],
     out_offsets: NDArray[np.integer],
@@ -117,9 +117,9 @@ def reconstruct_haplotypes_from_sparse(
     ref_offsets: NDArray[np.integer],
     pad_char: int,
     keep: NDArray[np.bool_] | None = None,
-    keep_offsets: NDArray[np.int64] | None = None,
-    annot_v_idxs: NDArray[np.int32] | None = None,
-    annot_ref_pos: NDArray[np.int32] | None = None,
+    keep_offsets: NDArray[np.integer] | None = None,
+    annot_v_idxs: NDArray[np.integer] | None = None,
+    annot_ref_pos: NDArray[np.integer] | None = None,
 ):
     """Reconstruct haplotypes from reference sequence and variants.
 
@@ -211,7 +211,7 @@ def reconstruct_haplotypes_from_sparse(
             )
 
 
-@nb.njit(nogil=True, cache=True)
+# @nb.njit(nogil=True, cache=True)
 def reconstruct_haplotype_from_sparse(
     offset_idx: int,
     geno_v_idxs: NDArray[np.integer],
@@ -407,3 +407,103 @@ def reconstruct_haplotype_from_sparse(
                 annot_v_idxs[out_end_idx:] = -1
             if annot_ref_pos is not None:
                 annot_ref_pos[out_end_idx:] = np.iinfo(np.int32).max
+
+
+@nb.njit(parallel=True, nogil=True, cache=True)
+def choose_exonic_variants(
+    starts: NDArray[np.integer],
+    ends: NDArray[np.integer],
+    geno_offset_idxs: NDArray[np.integer],
+    geno_v_idxs: NDArray[np.integer],
+    geno_offsets: NDArray[np.integer],
+    v_starts: NDArray[np.integer],
+    ilens: NDArray[np.integer],
+) -> tuple[NDArray[np.bool_], NDArray[np.integer]]:
+    """Mark variants to keep for each haplotype.
+
+    Parameters
+    ----------
+    starts : NDArray[np.int32]
+        Shape = (n_regions) Start positions for each region.
+    ends : NDArray[np.int32]
+        Shape = (n_regions) Ends for each region.
+    geno_offset_idxs : NDArray[np.intp]
+        Shape = (n_regions, ploidy) Indices for each region into offsets.
+    offsets : NDArray[np.int64]
+        Shape = (total_variants + 1) Offsets into sparse genotypes.
+    sparse_genos : NDArray[np.int32]
+        Shape = (total_variants) Sparse genotypes i.e. variant indices for ALT genotypes.
+    positions : NDArray[np.int32]
+        Shape = (total_variants) Positions of variants.
+    sizes : NDArray[np.int32]
+        Shape = (total_variants) Sizes of variants.
+    deterministic : bool
+        Whether to deterministically assign variants to groups
+    """
+    n_regions, ploidy = geno_offset_idxs.shape
+
+    lengths = np.empty((n_regions, ploidy), np.int64)
+    for query in nb.prange(n_regions):
+        for hap in range(ploidy):
+            o_idx = geno_offset_idxs[query, hap]
+            if geno_offsets.ndim == 1:
+                o_s, o_e = geno_offsets[o_idx], geno_offsets[o_idx + 1]
+            else:
+                o_s, o_e = geno_offsets[o_idx]
+            lengths[query, hap] = o_e - o_s
+    keep_offsets = np.empty(n_regions * ploidy + 1, np.int64)
+    keep_offsets[0] = 0
+    keep_offsets[1:] = lengths.cumsum()
+
+    n_variants = keep_offsets[-1]
+    keep = np.empty(n_variants, np.bool_)
+
+    for query in nb.prange(n_regions):
+        ref_start: int = starts[query]
+        ref_end: int = ends[query]
+        for hap in nb.prange(ploidy):
+            o_idx = geno_offset_idxs[query, hap]
+            o_s, o_e = geno_offsets[o_idx], geno_offsets[o_idx + 1]
+            qh_genos = geno_v_idxs[o_s:o_e]
+
+            k_idx = query * ploidy + hap
+            k_s, k_e = keep_offsets[k_idx], keep_offsets[k_idx + 1]
+            qh_keep = keep[k_s:k_e]
+
+            _choose_exonic_variants(
+                query_start=ref_start,
+                query_end=ref_end,
+                variant_idxs=qh_genos,
+                positions=v_starts,
+                sizes=ilens,
+                keep=qh_keep,
+            )
+
+    return keep, keep_offsets
+
+
+@nb.njit(nogil=True, cache=True)
+def _choose_exonic_variants(
+    query_start: int,
+    query_end: int,
+    variant_idxs: NDArray[np.integer],  # (v)
+    positions: NDArray[np.integer],  # (total variants)
+    sizes: NDArray[np.integer],  # (total variants)
+    keep: NDArray[np.bool_],  # (v)
+):
+    """Create a mask for variants that are fully contained within the query interval, which is
+    assumed to correspond to the exon boundaries."""
+    # no variants
+    if len(variant_idxs) == 0:
+        return
+
+    for v in range(len(variant_idxs)):
+        v_idx: int = variant_idxs[v]
+        v_pos = positions[v_idx]
+        # +1 for atomized
+        v_ref_end = v_pos - min(0, sizes[v_idx]) + 1
+
+        if v_pos >= query_start and v_ref_end <= query_end:
+            keep[v] = True
+        else:
+            keep[v] = False
diff --git a/python/genvarloader/_dataset/_impl.py b/python/genvarloader/_dataset/_impl.py
index b6c1b60a..0ef2e70a 100644
--- a/python/genvarloader/_dataset/_impl.py
+++ b/python/genvarloader/_dataset/_impl.py
@@ -5,13 +5,17 @@
 from pathlib import Path
 from typing import Callable, Generic, Literal, TypeVar, cast, overload
 
+import awkward as ak
 import numpy as np
 import polars as pl
 import seqpro as sp
 from attrs import define, evolve, field
+from awkward.contents import ListOffsetArray
+from awkward.index import Index64
 from genoray._svar import SparseGenotypes
 from genoray._utils import ContigNormalizer
 from loguru import logger
+from more_itertools import collapse
 from numpy.typing import NDArray
 from typing_extensions import NoReturn, Self, assert_never
 
@@ -27,9 +31,14 @@
     to_padded,
 )
 from .._torch import TORCH_AVAILABLE, TorchDataset, get_dataloader
-from .._types import DTYPE, AnnotatedHaps, Idx
-from .._utils import idx_like_to_array, lengths_to_offsets, normalize_contig_name
-from ._indexing import DatasetIndexer
+from .._types import DTYPE, AnnotatedHaps, Idx, StrIdx
+from .._utils import (
+    idx_like_to_array,
+    is_dtype,
+    lengths_to_offsets,
+    normalize_contig_name,
+)
+from ._indexing import DatasetIndexer, SpliceIndexer
 from ._rag_variants import RaggedVariants
 from ._reconstruct import Haps, HapsTracks, Ref, RefTracks, Tracks
 from ._reference import Reference
@@ -82,8 +91,8 @@ class Dataset:
     - :meth:`Dataset.with_len() <Dataset.with_len()>`
     """
 
-    @overload
     @staticmethod
+    @overload
     def open(
         path: str | Path,
         reference: None = None,
@@ -91,9 +100,11 @@ def open(
         rng: int | np.random.Generator | None = False,
         deterministic: bool = True,
         rc_neg: bool = True,
+        splice_info: str | tuple[str, str] | None = None,
+        var_filter: Literal["exonic"] | None = None,
     ) -> RaggedDataset[None, MaybeRTRK]: ...
-    @overload
     @staticmethod
+    @overload
     def open(
         path: str | Path,
         reference: str | Path | Reference,
@@ -101,6 +112,8 @@ def open(
         rng: int | np.random.Generator | None = False,
         deterministic: bool = True,
         rc_neg: bool = True,
+        splice_info: str | tuple[str, str] | None = None,
+        var_filter: Literal["exonic"] | None = None,
     ) -> RaggedDataset[RaggedSeqs, MaybeRTRK]: ...
     @staticmethod
     def open(
@@ -110,6 +123,8 @@ def open(
         rng: int | np.random.Generator | None = False,
         deterministic: bool = True,
         rc_neg: bool = True,
+        splice_info: str | tuple[str, str] | None = None,
+        var_filter: Literal["exonic"] | None = None,
     ) -> RaggedDataset[MaybeRSEQ, MaybeRTRK]:
         """Open a dataset from a path. If no reference genome is provided, the dataset cannot yield sequences.
         Will initialize the dataset such that it will return tracks and haplotypes (reference sequences if no genotypes) if possible.
@@ -130,6 +145,14 @@ def open(
             shifting of longer-than-requested haplotypes.
         rc_neg
             Whether to reverse-complement sequences and reverse tracks on negative strands.
+        splice_info
+            A string or tuple of strings representing the splice information to use.
+            If a string, it will be used as the transcript ID and the exons are expected to be in order.
+            If a tuple of strings, the first string will be used as the transcript ID and the second string will be used as the exon number.
+            If a dictionary, the keys will be used as the transcript ID and the values should be the row number for each exon, in order.
+            If False, splicing will be disabled.
+        var_filter
+            Whether to filter variants. If set to :code:`"exonic"`, only exonic variants will be applied.
         """
         path = Path(path)
         if not path.exists():
@@ -203,6 +226,7 @@ def open(
                     samples=samples,
                     ploidy=ploidy,
                 )
+                seqs.filter = var_filter
                 tracks = None
                 reconstructor = seqs
             case reference, True, True:
@@ -221,6 +245,7 @@ def open(
                     samples=samples,
                     ploidy=ploidy,
                 )
+                seqs.filter = var_filter
                 tracks = Tracks.from_path(path, len(regions), len(samples))
                 tracks = tracks.with_tracks(list(tracks.intervals))
                 reconstructor = HapsTracks(haps=seqs, tracks=tracks)
@@ -229,6 +254,12 @@ def open(
                 assert_never(has_genotypes)
                 assert_never(has_intervals)
 
+        if splice_info is not None:
+            splice_idxer, spliced_bed = _parse_splice_info(splice_info, bed, idxer)
+        else:
+            splice_idxer = None
+            spliced_bed = None
+
         if seqs is not None:
             cnorm = ContigNormalizer(seqs.reference.contigs)
             contig_lengths = dict(
@@ -266,7 +297,9 @@ def open(
             transform=None,
             deterministic=deterministic,
             _idxer=idxer,
+            _sp_idxer=splice_idxer,
             _full_bed=bed,
+            _spliced_bed=spliced_bed,
             _full_regions=regions,
             _seqs=seqs,
             _tracks=tracks,
@@ -284,6 +317,8 @@ def with_settings(
         rng: int | np.random.Generator | None = None,
         deterministic: bool | None = None,
         rc_neg: bool | None = None,
+        splice_info: str | tuple[str, str] | Literal[False] | None = None,
+        var_filter: Literal[False, "exonic"] | None = None,
     ) -> Self:
         """Modify settings of the dataset, returning a new dataset without modifying the old one.
 
@@ -300,17 +335,18 @@ def with_settings(
             can be returned.
         rc_neg
             Whether to reverse-complement sequences and reverse tracks on negative strands.
+        splice_info
+            A string or tuple of strings representing the splice information to use.
+            If a string, it will be used as the transcript ID and the exons are expected to be in order.
+            If a tuple of strings, the first string will be used as the transcript ID and the second string will be used as the exon number.
+            If a dictionary, the keys will be used as the transcript ID and the values should be the row number for each exon, in order.
+            If False, splicing will be disabled.
+        var_filter
+            Whether to filter variants. If set to :code:`"exonic"`, only exonic variants will be applied.
         """
         to_evolve = {}
 
         if jitter is not None:
-            if jitter < 0:
-                raise ValueError(f"Jitter ({jitter}) must be a non-negative integer.")
-            elif jitter > self.max_jitter:
-                raise ValueError(
-                    f"Jitter ({jitter}) must be less than or equal to the maximum jitter of the dataset ({self.max_jitter})."
-                )
-
             if jitter != self.jitter:
                 if isinstance(self.output_length, int):
                     min_r_len: int = (
@@ -337,7 +373,80 @@ def with_settings(
         if rc_neg is not None:
             to_evolve["rc_neg"] = rc_neg
 
-        return evolve(self, **to_evolve)
+        if splice_info is not None:
+            if splice_info is False:
+                splice_idxer = None
+                spliced_bed = None
+            else:
+                splice_idxer, spliced_bed = _parse_splice_info(
+                    splice_info, self._full_bed, self._idxer
+                )
+            to_evolve["_sp_idxer"] = splice_idxer
+            to_evolve["_spliced_bed"] = spliced_bed
+
+        if var_filter is not None:
+            if not isinstance(self._seqs, Haps):
+                raise ValueError(
+                    "Filtering variants can only be done when the dataset has variants."
+                )
+
+            if var_filter is False:
+                var_filter = None
+
+            if var_filter != self._seqs.filter:
+                to_evolve["_seqs"] = evolve(self._seqs, filter=var_filter)
+
+        self = evolve(self, **to_evolve)
+        self._check_valid_state()
+
+        return self
+
+    def _check_valid_state(self):
+        if self.is_spliced:
+            if self.jitter > 0:
+                raise RuntimeError(
+                    "Jitter is not supported with splicing. Please set jitter to 0."
+                )
+
+            if not self.deterministic:
+                raise RuntimeError(
+                    "Non-deterministic algorithms are not supported with splicing. Please set deterministic to True."
+                )
+
+            if self.sequence_type == "variants":
+                raise ValueError("Splicing is not supported with variants.")
+
+        if self.jitter < 0:
+            raise ValueError(f"Jitter ({self.jitter}) must be a non-negative integer.")
+        elif self.jitter > self.max_jitter:
+            raise ValueError(
+                f"Jitter ({self.jitter}) must be less than or equal to the maximum jitter of the dataset ({self.max_jitter})."
+            )
+
+        if isinstance(self.output_length, int):
+            if self.sequence_type == "variants":
+                raise ValueError(
+                    "Output length must be ragged when the sequence type is variants."
+                )
+
+            if self.output_length < 1:
+                raise ValueError(
+                    f"Output length ({self.output_length}) must be a positive integer."
+                )
+
+            min_r_len: int = (self._full_regions[:, 2] - self._full_regions[:, 1]).min()
+            max_output_length = min_r_len + 2 * self.max_jitter
+            eff_length = self.output_length + 2 * self.jitter
+            if eff_length > max_output_length:
+                raise ValueError(
+                    f"Effective length (out_len={self.output_length}) + 2 * ({self.jitter=}) = {eff_length} must be less"
+                    f" than or equal to the maximum output length of the dataset ({max_output_length})."
+                    f" The maximum output length is the minimum region length ({min_r_len}) + 2 * (max_jitter={self.max_jitter})."
+                )
+        elif self.output_length == "variable" and self.sequence_type == "variants":
+            raise ValueError(
+                "Output length must be ragged when the sequence type is variants."
+            )
 
     def with_len(
         self, output_length: Literal["ragged", "variable"] | int
@@ -352,22 +461,7 @@ def with_len(
             `online documentation <https://genvarloader.readthedocs.io/en/latest/dataset.html>`_ for more information.
         """
         if isinstance(output_length, int):
-            if output_length < 1:
-                raise ValueError(
-                    f"Output length ({output_length}) must be a positive integer."
-                )
-            min_r_len: int = (self._full_regions[:, 2] - self._full_regions[:, 1]).min()
-            max_output_length = min_r_len + 2 * self.max_jitter
-            eff_length = output_length + 2 * self.jitter
-
-            if eff_length > max_output_length:
-                raise ValueError(
-                    f"Jitter-expanded output length (out_len={self.output_length}) + 2 * ({self.jitter=}) = {eff_length} must be less"
-                    f" than or equal to the maximum output length of the dataset ({max_output_length})."
-                    f" The maximum output length is the minimum region length ({min_r_len}) + 2 * (max_jitter={self.max_jitter})."
-                )
-
-            return ArrayDataset(
+            out = ArrayDataset(
                 path=self.path,
                 output_length=output_length,
                 max_jitter=self.max_jitter,
@@ -378,7 +472,9 @@ def with_len(
                 transform=self.transform,
                 deterministic=self.deterministic,
                 _idxer=self._idxer,
+                _sp_idxer=self._sp_idxer,
                 _full_bed=self._full_bed,
+                _spliced_bed=self._spliced_bed,
                 _full_regions=self._full_regions,
                 _seqs=self._seqs,
                 _tracks=self._tracks,
@@ -386,7 +482,7 @@ def with_len(
                 _rng=self._rng,
             )
         else:
-            return RaggedDataset(
+            out = RaggedDataset(
                 path=self.path,
                 output_length=output_length,
                 max_jitter=self.max_jitter,
@@ -397,7 +493,9 @@ def with_len(
                 transform=self.transform,
                 deterministic=self.deterministic,
                 _idxer=self._idxer,
+                _sp_idxer=self._sp_idxer,
                 _full_bed=self._full_bed,
+                _spliced_bed=self._spliced_bed,
                 _full_regions=self._full_regions,
                 _seqs=self._seqs,
                 _tracks=self._tracks,
@@ -405,6 +503,10 @@ def with_len(
                 _rng=self._rng,
             )
 
+        out._check_valid_state()
+
+        return out
+
     def with_seqs(
         self, kind: Literal["reference", "haplotypes", "annotated", "variants"] | None
     ):
@@ -468,10 +570,6 @@ class AnnotatedHaps:
                     "Dataset is set to only return sequences, so setting sequence_type to None would"
                     " result in a Dataset that cannot return anything."
                 )
-            case None, _, _, (Tracks() as t) | RefTracks(tracks=t) | HapsTracks(
-                tracks=t
-            ):
-                return evolve(self, _recon=t)
             case kind, None, _, _:
                 raise ValueError(
                     "Dataset has no reference genome to reconstruct sequences from."
@@ -481,51 +579,60 @@ class AnnotatedHaps:
                     "Dataset has no genotypes to reconstruct haplotypes from."
                 )
 
+            case None, _, _, (Tracks() as t) | RefTracks(tracks=t) | HapsTracks(
+                tracks=t
+            ):
+                self = evolve(self, _recon=t)
+
             case "reference", _, _, Ref(reference=r) | Haps(reference=r):
                 seqs = Ref(reference=r)
-                return evolve(self, _recon=seqs)
+                self = evolve(self, _recon=seqs)
             case "reference", Ref(reference=ref) | Haps(reference=ref), _, (
                 (Tracks() as tracks)
                 | RefTracks(tracks=tracks)
                 | HapsTracks(tracks=tracks)
             ):
                 seqs = Ref(reference=ref)
-                return evolve(self, _recon=RefTracks(seqs=seqs, tracks=tracks))
+                self = evolve(self, _recon=RefTracks(seqs=seqs, tracks=tracks))
 
             case "haplotypes", Haps() as haps, _, Ref() | Haps():
-                return evolve(self, _recon=haps.to_kind(RaggedSeqs))
+                self = evolve(self, _recon=haps.to_kind(RaggedSeqs))
             case "haplotypes", Haps() as haps, _, (
                 (Tracks() as tracks)
                 | RefTracks(tracks=tracks)
                 | HapsTracks(tracks=tracks)
             ):
-                return evolve(self, _recon=HapsTracks(haps.to_kind(RaggedSeqs), tracks))
+                self = evolve(self, _recon=HapsTracks(haps.to_kind(RaggedSeqs), tracks))
 
             case "annotated", Haps() as haps, _, Ref() | Haps():
-                return evolve(self, _recon=haps.to_kind(RaggedAnnotatedHaps))
+                self = evolve(self, _recon=haps.to_kind(RaggedAnnotatedHaps))
             case "annotated", Haps() as haps, _, (
                 (Tracks() as tracks)
                 | RefTracks(tracks=tracks)
                 | HapsTracks(tracks=tracks)
             ):
-                return evolve(
+                self = evolve(
                     self, _recon=HapsTracks(haps.to_kind(RaggedAnnotatedHaps), tracks)
                 )
 
             case "variants", Haps() as haps, _, Ref() | Haps():
-                return evolve(self, _recon=haps.to_kind(RaggedVariants))
+                self = evolve(self, _recon=haps.to_kind(RaggedVariants))
             case "variants", Haps() as haps, _, (
                 (Tracks() as tracks)
                 | RefTracks(tracks=tracks)
                 | HapsTracks(tracks=tracks)
             ):
-                return evolve(
+                self = evolve(
                     self, _recon=HapsTracks(haps.to_kind(RaggedVariants), tracks)
                 )
 
             case k, s, t, r:
                 assert_never(k), assert_never(s), assert_never(t), assert_never(r)
 
+        self._check_valid_state()
+
+        return self
+
     def with_tracks(self, tracks: str | list[str] | None):
         """Modify which tracks to return, returning a new dataset without modifying the old one.
 
@@ -595,9 +702,11 @@ def with_tracks(self, tracks: str | list[str] | None):
     transform: Callable | None
     """Tranform to apply to what the dataset would otherwise return on its own."""
     _full_bed: pl.DataFrame = field(alias="_full_bed")
+    _spliced_bed: pl.DataFrame | None = field(alias="_spliced_bed")
     _full_regions: NDArray[np.int32] = field(alias="_full_regions")
     """Unjittered, sorted regions matching order on-disk."""
     _idxer: DatasetIndexer = field(alias="_idxer")
+    _sp_idxer: SpliceIndexer | None = field(alias="_sp_idxer")
     _seqs: (
         Ref | Haps[RaggedSeqs] | Haps[RaggedAnnotatedHaps] | Haps[RaggedVariants] | None
     ) = field(alias="_seqs")
@@ -620,6 +729,11 @@ def is_subset(self) -> bool:
         """Whether the dataset is a subset."""
         return self._idxer.is_subset
 
+    @property
+    def is_spliced(self) -> bool:
+        """Whether the dataset is spliced."""
+        return self._sp_idxer is not None
+
     @property
     def has_reference(self) -> bool:
         """Whether the dataset was provided a reference genome."""
@@ -657,8 +771,18 @@ def regions(self) -> pl.DataFrame:
 
     @property
     def n_regions(self) -> int:
-        """The number of regions in the dataset."""
-        return self._idxer.n_regions
+        """The number of (spliced) regions in the dataset."""
+        return self.shape[0]
+
+    @property
+    def spliced_regions(self) -> pl.DataFrame | None:
+        """The spliced regions in the dataset."""
+        if self._spliced_bed is None or self._sp_idxer is None:
+            raise ValueError("Dataset does not have splice information.")
+        if self._sp_idxer.row_subset_idxs is None:
+            return self._spliced_bed
+        else:
+            return self._spliced_bed[self._sp_idxer.row_subset_idxs]
 
     @property
     def n_samples(self) -> int:
@@ -673,13 +797,19 @@ def ploidy(self) -> int | None:
 
     @property
     def shape(self) -> tuple[int, int]:
-        """Return the shape of the dataset. :code:`(n_samples, n_regions)`"""
-        return self.n_regions, self.n_samples
+        """Return the shape of the dataset. :code:`(n_rows, n_samples)`"""
+        if self._sp_idxer is None:
+            return self._idxer.shape
+        else:
+            return self._sp_idxer.shape
 
     @property
     def full_shape(self) -> tuple[int, int]:
-        """Return the full shape of the dataset, ignoring any subsetting. :code:`(n_samples, n_regions)`"""
-        return self._idxer.full_shape
+        """Return the full shape of the dataset, ignoring any subsetting. :code:`(n_rows, n_samples)`"""
+        if self._sp_idxer is None:
+            return self._idxer.full_shape
+        else:
+            return self._sp_idxer.full_shape
 
     @property
     def available_tracks(self) -> list[str] | None:
@@ -734,11 +864,14 @@ def __len__(self):
         return self.n_regions * self.n_samples
 
     def __str__(self) -> str:
-        if self._available_sequences is None or self.sequence_type is None:
-            seq_type = "None"
+        splice_status = "Spliced" if self.is_spliced else "Unspliced"
+
+        if self._available_sequences is None:
+            seq_type = None
         else:
             seqs = self._available_sequences
-            seqs[seqs.index(self.sequence_type)] = f"[{self.sequence_type}]"
+            if self.sequence_type is not None:
+                seqs[seqs.index(self.sequence_type)] = f"[{self.sequence_type}]"
             seq_type = " ".join(seqs)
 
         if self.available_tracks is None:
@@ -755,7 +888,7 @@ def __str__(self) -> str:
             if len(self.active_tracks) > 5:
                 act_tracks += f" + {len(self.active_tracks) - 5} more"
         return (
-            f"GVL store at {self.path}\n"
+            splice_status + f" GVL dataset at {self.path}\n"
             f"Is subset: {self.is_subset}\n"
             f"# of regions: {self.n_regions}\n"
             f"# of samples: {self.n_samples}\n"
@@ -832,9 +965,7 @@ def subset_to(
             return self
 
         if samples is not None:
-            if isinstance(samples, np.ndarray) and np.issubdtype(
-                samples.dtype, np.bool_
-            ):
+            if isinstance(samples, np.ndarray) and is_dtype(samples, np.bool_):
                 sample_idx = np.nonzero(samples)[0]
             elif isinstance(
                 samples, (int, np.integer, slice, np.ndarray)
@@ -860,22 +991,35 @@ def subset_to(
         if regions is not None:
             if isinstance(regions, pl.Series):
                 region_idxs = regions.to_numpy()
-                if np.issubdtype(region_idxs.dtype, np.bool_):
+                if is_dtype(region_idxs, np.bool_):
                     region_idxs = np.nonzero(region_idxs)[0]
-                elif not np.issubdtype(region_idxs.dtype, np.integer):
+                elif not is_dtype(region_idxs, np.integer):
                     raise ValueError("`regions` must be index-like or a boolean mask.")
             else:
                 region_idxs = idx_like_to_array(regions, self.n_regions)
         else:
             region_idxs = None
 
-        idxer = self._idxer.subset_to(regions=region_idxs, samples=sample_idx)
-
-        return evolve(self, _idxer=idxer)
+        if self._sp_idxer is None:
+            idxer = self._idxer.subset_to(regions=region_idxs, samples=sample_idx)
+            return evolve(self, _idxer=idxer)
+        else:
+            row_idxs = region_idxs
+            sp_idxer, sub_dsi = self._sp_idxer.subset_to(
+                rows=row_idxs, samples=sample_idx
+            )
+            return evolve(self, _idxer=sub_dsi, _sp_idxer=sp_idxer)
 
     def to_full_dataset(self) -> Self:
         """Return a full sized dataset, undoing any subsetting."""
-        return evolve(self, _idxer=self._idxer.to_full_dataset())
+        if self._sp_idxer is None:
+            return evolve(self, _idxer=self._idxer.to_full_dataset())
+        else:
+            return evolve(
+                self,
+                _idxer=self._idxer.to_full_dataset(),
+                _sp_idxer=self._sp_idxer.to_full_dataset(),
+            )
 
     def haplotype_lengths(
         self,
@@ -886,6 +1030,10 @@ def haplotype_lengths(
         not phased or not deterministic, this will return :code:`None` because the haplotypes are not guaranteed to be
         a consistent length due to randomness in what variants are used.
 
+        .. note::
+
+            Currently not implemented for spliced datasets.
+
         Parameters
         ----------
         regions
@@ -893,6 +1041,11 @@ def haplotype_lengths(
         samples
             Samples to compute haplotype lengths for.
         """
+        if self._sp_idxer is not None:
+            raise NotImplementedError(
+                "Haplotype lengths are not yet implemented for spliced datasets."
+            )
+
         if (
             not isinstance(self._seqs, Haps)
             or not isinstance(self._seqs.genotypes, SparseGenotypes)
@@ -1164,7 +1317,7 @@ def to_dataloader(
         )
 
     def __getitem__(
-        self, idx: Idx | tuple[Idx] | tuple[Idx, Idx | str | Sequence[str]]
+        self, idx: StrIdx | tuple[StrIdx] | tuple[StrIdx, StrIdx]
     ) -> (
         Ragged[np.bytes_ | np.float32]
         | RaggedAnnotatedHaps
@@ -1180,6 +1333,57 @@ def __getitem__(
             ...,
         ]
     ):
+        if self._sp_idxer is not None:
+            recon, squeeze, out_reshape = self._getitem_spliced(idx, self._sp_idxer)
+        else:
+            if isinstance(idx, tuple):
+                r_idx = idx[0]
+            else:
+                r_idx = idx
+
+            if isinstance(r_idx, str) or (
+                isinstance(r_idx, Sequence) and isinstance(next(collapse(r_idx)), str)
+            ):
+                raise ValueError(
+                    "Unspliced datasets do not support string indexing over regions. Please use integer indexing."
+                )
+
+            idx = cast(Idx | tuple[Idx] | tuple[Idx, StrIdx], idx)
+
+            recon, squeeze, out_reshape = self._getitem_unspliced(idx)
+
+        if self.output_length == "variable":
+            recon = cast(
+                tuple[Ragged[np.bytes_ | np.float32] | RaggedAnnotatedHaps, ...], recon
+            )
+            recon = tuple(_pad(r) for r in recon)
+        elif isinstance(self.output_length, int):
+            recon = cast(
+                tuple[Ragged[np.bytes_ | np.float32] | RaggedAnnotatedHaps, ...], recon
+            )
+            recon = tuple(_fix_len(r, self.output_length) for r in recon)
+
+        if squeeze:
+            # (1 [p] l) -> ([p] l)
+            recon = tuple(o.squeeze(0) for o in recon)
+
+        if out_reshape is not None:
+            recon = tuple(o.reshape(out_reshape + o.shape[1:]) for o in recon)
+
+        if len(recon) == 1:
+            recon = recon[0]
+
+        return recon
+
+    def _getitem_unspliced(
+        self, idx: Idx | tuple[Idx] | tuple[Idx, Idx | str | Sequence[str]]
+    ) -> tuple[
+        tuple[
+            Ragged[np.bytes_ | np.float32] | RaggedAnnotatedHaps | RaggedVariants, ...
+        ],
+        bool,
+        tuple[int, ...] | None,
+    ]:
         # (b)
         ds_idx, squeeze, out_reshape = self._idxer.parse_idx(idx)
         r_idx, _ = np.unravel_index(ds_idx, self.full_shape)
@@ -1203,10 +1407,7 @@ def __getitem__(
             deterministic=self.deterministic,
         )
 
-        if isinstance(recon, tuple):
-            unlist = False
-        else:
-            unlist = True
+        if not isinstance(recon, tuple):
             recon = (recon,)
 
         ragv = None
@@ -1225,81 +1426,250 @@ def __getitem__(
                 )
             # (b)
             to_rc: NDArray[np.bool_] = self._full_regions[r_idx, 3] == -1
-            recon = tuple(self._rc(r, to_rc) for r in recon)
-
-        if self.output_length == "variable":
-            recon = tuple(self._pad(r) for r in recon)
-        elif isinstance(self.output_length, int):
-            recon = tuple(self._fix_len(r) for r in recon)
+            recon = tuple(_rc(r, to_rc) for r in recon)
 
         if ragv is not None:
             recon = (ragv,) + recon
 
-        if out_reshape is not None:
-            recon = tuple(o.reshape(out_reshape + o.shape[1:]) for o in recon)
+        return (recon, squeeze, out_reshape)
 
-        if squeeze:
-            # (1 [p] l) -> ([p] l)
-            recon = tuple(o.squeeze(0) for o in recon)
+    def _getitem_spliced(
+        self,
+        idx: StrIdx | tuple[StrIdx] | tuple[StrIdx, StrIdx],
+        splice_idxer: SpliceIndexer,
+    ) -> tuple[
+        tuple[Ragged[np.bytes_ | np.float32] | RaggedAnnotatedHaps, ...],
+        bool,
+        tuple[int, ...] | None,
+    ]:
+        if isinstance(self.output_length, int):
+            raise RuntimeError(
+                "In general, splicing cannot be done with fixed length data because even if the length of each region's data"
+                " is fixed/constant, the number of elements in each spliced element is not. Thus, the final length of the"
+                " spliced elements will be variable."
+            )
 
-        if unlist:
-            recon = recon[0]
+        assert self.sequence_type != "variants"
+        assert not isinstance(self.output_length, int)
+        assert self.jitter == 0
+        assert self.deterministic
+
+        # TODO: really need to assert no jitter and deterministic?
+        # * In theory, this still "works" with jitter or non-determinism, but why would anyone want this? Would they want a different alg here?
+        # * Potential issues:
+        # * Each each component of the spliced output will have different jitter
+        # * For non-determinism, each component will have different shifts & different unphased haplotypes chosen
+        if self.jitter > 0:
+            raise RuntimeError(
+                "Jitter is not supported with splicing. Please set jitter to 0."
+            )
 
-        return recon
+        if not self.deterministic:
+            raise RuntimeError(
+                "Non-deterministic algorithms are not supported with splicing. Please set deterministic to True."
+            )
 
-    @overload
-    def _rc(self, rag: Ragged[DTYPE], to_rc: NDArray[np.bool_]) -> Ragged[DTYPE]: ...
-    @overload
-    def _rc(
-        self, rag: RaggedAnnotatedHaps, to_rc: NDArray[np.bool_]
-    ) -> RaggedAnnotatedHaps: ...
-    def _rc(
-        self, rag: Ragged | RaggedAnnotatedHaps, to_rc: NDArray[np.bool_]
-    ) -> Ragged | RaggedAnnotatedHaps:
-        if isinstance(rag, Ragged):
-            if is_rag_dtype(rag, np.bytes_):
-                rag = reverse_complement(rag, to_rc)
-            elif is_rag_dtype(rag, np.float32):
-                reverse(rag, to_rc)
-        elif isinstance(rag, RaggedAnnotatedHaps):
-            rag.haps = reverse_complement(rag.haps, to_rc)
-            reverse(rag.var_idxs, to_rc)
-            reverse(rag.ref_coords, to_rc)
-        else:
-            assert_never(rag)
-        return rag
+        inner_ds = self.with_len("ragged")
+        ds_idx, squeeze, out_reshape, offsets = splice_idxer.parse_idx(idx)
+        r_idx, _ = np.unravel_index(ds_idx, self._idxer.full_shape)
+        regions = self._full_regions[r_idx]
 
-    @overload
-    def _pad(self, rag: Ragged[DTYPE]) -> NDArray[DTYPE]: ...
-    @overload
-    def _pad(self, rag: RaggedAnnotatedHaps) -> AnnotatedHaps: ...
-    def _pad(self, rag: Ragged | RaggedAnnotatedHaps) -> NDArray | AnnotatedHaps:
-        if isinstance(rag, Ragged):
-            if is_rag_dtype(rag, np.bytes_):
-                return to_padded(rag, b"N")
-            elif is_rag_dtype(rag, np.float32):
-                return to_padded(rag, 0)
-            else:
-                raise ValueError(f"Unsupported pad dtype: {rag.data.dtype}")
-        elif isinstance(rag, RaggedAnnotatedHaps):
-            return rag.to_padded()
-        else:
-            assert_never(rag)
+        recon = inner_ds._recon(
+            idx=ds_idx,
+            r_idx=r_idx,
+            regions=regions,
+            output_length="ragged",
+            jitter=self.jitter,
+            rng=self._rng,
+            deterministic=self.deterministic,
+        )
 
-    @overload
-    def _fix_len(self, rag: Ragged[DTYPE]) -> NDArray[DTYPE]: ...
-    @overload
-    def _fix_len(self, rag: RaggedAnnotatedHaps) -> AnnotatedHaps: ...
-    def _fix_len(self, rag: Ragged | RaggedAnnotatedHaps) -> NDArray | AnnotatedHaps:
-        assert isinstance(self.output_length, int)
-        if isinstance(rag, Ragged):
-            # (b p) or (b)
-            return rag.data.reshape((*rag.shape, self.output_length))
-        elif isinstance(rag, RaggedAnnotatedHaps):
-            assert isinstance(self._seqs, Haps)
-            return rag.to_fixed_shape((*rag.shape, self.output_length))
+        if not isinstance(recon, tuple):
+            recon = (recon,)
+
+        recon = cast(
+            tuple[Ragged[np.bytes_ | np.float32] | RaggedAnnotatedHaps, ...], recon
+        )
+
+        if self.rc_neg:
+            # (b)
+            to_rc: NDArray[np.bool_] = regions[:, 3] == -1
+            recon = tuple(_rc(r, to_rc) for r in recon)
+
+        recon = tuple(_cat_length(r, offsets) for r in recon)
+
+        return recon, squeeze, out_reshape
+
+
+def _parse_splice_info(
+    splice_info: str | tuple[str, str],
+    full_bed: pl.DataFrame,
+    idxer: DatasetIndexer,
+):
+    """Parse splice info into a SpliceIndexer.
+
+    Parameters
+    ----------
+    splice_info
+        The splice info to parse. Can be a string, a tuple of strings, or a dictionary.
+    regions
+        The regions to parse the splice info from.
+    idxer
+        The idxer to use to parse the splice info.
+    """
+    if isinstance(splice_info, str):
+        sp_bed = (
+            full_bed.rename({splice_info: "splice_id"})
+            .with_row_index()
+            .group_by("splice_id", maintain_order=True)
+            .agg(pl.all())
+        )
+        names = sp_bed["splice_id"].to_list()
+        lengths = sp_bed["index"].list.len().to_numpy()
+        splice_map = Ragged.from_lengths(
+            sp_bed["index"].explode().to_numpy(), lengths
+        ).to_awkward()
+    elif isinstance(splice_info, tuple):
+        if len(splice_info) != 2:
+            raise ValueError(
+                "Splice info tuple must be of length 2, corresponding to columns names for splice IDs and element ordering."
+            )
+        sp_bed = (
+            full_bed.rename({splice_info[0]: "splice_id"})
+            .with_row_index()
+            .group_by("splice_id", maintain_order=True)
+            .agg(pl.all().sort_by(splice_info[1]))
+        )
+        names = sp_bed["splice_id"].to_list()
+        lengths = sp_bed["index"].list.len().to_numpy()
+        splice_map = Ragged.from_lengths(
+            sp_bed["index"].explode().to_numpy(), lengths
+        ).to_awkward()
+    else:
+        assert_never(splice_info)
+
+    splice_map = cast(ak.Array, splice_map)
+    sp_idxer = SpliceIndexer._init(names, splice_map, idxer)
+    return sp_idxer, sp_bed
+
+
+@overload
+def _rc(rag: Ragged[DTYPE], to_rc: NDArray[np.bool_]) -> Ragged[DTYPE]: ...
+@overload
+def _rc(rag: RaggedAnnotatedHaps, to_rc: NDArray[np.bool_]) -> RaggedAnnotatedHaps: ...
+def _rc(
+    rag: Ragged | RaggedAnnotatedHaps, to_rc: NDArray[np.bool_]
+) -> Ragged | RaggedAnnotatedHaps:
+    """Reverse or reverse-complement stuff.
+
+    Parameters
+    ----------
+    rag
+        Ragged data, could be reference, haplotypes, annotated haplotypes, or tracks.
+        Ref shape: (batch, ~length)
+        Hap shape: (batch, ploidy, ~length)
+        Track shape: (batch, tracks, [ploidy], ~length)
+    to_rc
+        Mask of which regions to reverse-complement. Shape: (batch)
+    """
+    if isinstance(rag, Ragged):
+        if is_rag_dtype(rag, np.bytes_):
+            rag = reverse_complement(rag, to_rc)
+        elif is_rag_dtype(rag, np.float32):
+            reverse(rag, to_rc)
+    elif isinstance(rag, RaggedAnnotatedHaps):
+        rag.haps = reverse_complement(rag.haps, to_rc)
+        reverse(rag.var_idxs, to_rc)
+        reverse(rag.ref_coords, to_rc)
+    else:
+        assert_never(rag)
+    return rag
+
+
+@overload
+def _pad(rag: Ragged[DTYPE]) -> NDArray[DTYPE]: ...
+@overload
+def _pad(rag: RaggedAnnotatedHaps) -> AnnotatedHaps: ...
+def _pad(rag: Ragged | RaggedAnnotatedHaps) -> NDArray | AnnotatedHaps:
+    if isinstance(rag, Ragged):
+        if is_rag_dtype(rag, np.bytes_):
+            return to_padded(rag, b"N")
+        elif is_rag_dtype(rag, np.float32):
+            return to_padded(rag, 0)
         else:
-            assert_never(rag)
+            raise ValueError(f"Unsupported pad dtype: {rag.data.dtype}")
+    elif isinstance(rag, RaggedAnnotatedHaps):
+        return rag.to_padded()
+    else:
+        assert_never(rag)
+
+
+@overload
+def _fix_len(
+    rag: Ragged[DTYPE], output_length: Literal["ragged", "variable"] | int
+) -> NDArray[DTYPE]: ...
+@overload
+def _fix_len(
+    rag: RaggedAnnotatedHaps, output_length: Literal["ragged", "variable"] | int
+) -> AnnotatedHaps: ...
+def _fix_len(
+    rag: Ragged | RaggedAnnotatedHaps,
+    output_length: Literal["ragged", "variable"] | int,
+) -> NDArray | AnnotatedHaps:
+    assert isinstance(output_length, int)
+    if isinstance(rag, Ragged):
+        # (b p) or (b)
+        return rag.data.reshape((*rag.shape, output_length))
+    elif isinstance(rag, RaggedAnnotatedHaps):
+        return rag.to_fixed_shape((*rag.shape, output_length))
+    else:
+        assert_never(rag)
+
+
+@overload
+def _cat_length(rag: Ragged[DTYPE], offsets: NDArray[np.integer]) -> Ragged[DTYPE]: ...
+@overload
+def _cat_length(
+    rag: RaggedAnnotatedHaps, offsets: NDArray[np.integer]
+) -> RaggedAnnotatedHaps: ...
+def _cat_length(
+    rag: Ragged | RaggedAnnotatedHaps, offsets: NDArray[np.integer]
+) -> Ragged | RaggedAnnotatedHaps:
+    """Concatenate the lengths of the ragged data."""
+    if isinstance(rag, Ragged):
+        if rag.ndim == 1 or rag.shape[1:] == (1,) * (
+            rag.ndim - 1
+        ):  # (b [1] [1] ~l) => layout is correct
+            new_lengths = np.add.reduceat(rag.lengths, offsets[:-1], 0)
+            cat = Ragged.from_lengths(rag.data, new_lengths)
+        elif rag.ndim == 2:  # (b p ~l) or (b t ~l)
+            grouped = ak.Array(
+                ListOffsetArray(Index64(offsets), rag.to_awkward().layout)
+            )
+            cat = Ragged.from_awkward(
+                ak.concatenate(
+                    [
+                        ak.flatten(grouped[:, :, i], -1)[:, None]  # (g 1 ~l)
+                        for i in range(rag.shape[1])
+                    ],
+                    1,
+                )
+            )
+        elif rag.ndim == 3:  # hap tracks: (b t p ~l)
+            raise NotImplementedError("Splicing haplotype tracks.")
+        else:
+            raise RuntimeError("Should never see a 4+ dim ragged array.")
+
+        if is_rag_dtype(rag, np.bytes_):
+            cat = cat.view("S1")  # type: ignore
+        return cat
+    elif isinstance(rag, RaggedAnnotatedHaps):
+        haps = _cat_length(rag.haps, offsets)
+        var_idxs = _cat_length(rag.var_idxs, offsets)
+        ref_coords = _cat_length(rag.ref_coords, offsets)
+        return RaggedAnnotatedHaps(haps, var_idxs, ref_coords)
+    else:
+        assert_never(rag)
 
 
 def _annot_to_intervals(regions: pl.DataFrame, annot: pl.DataFrame) -> RaggedIntervals:
@@ -1442,7 +1812,7 @@ def __getitem__(
         self: ArrayDataset[MaybeSEQ, MaybeTRK],
         idx: Idx | tuple[Idx] | tuple[Idx, Idx | str | Sequence[str]],
     ) -> SEQ | NDArray[np.float32] | tuple[SEQ, NDArray[np.float32]]: ...
-    def __getitem__(
+    def __getitem__(  # type: ignore
         self, idx: Idx | tuple[Idx] | tuple[Idx, Idx | str | Sequence[str]]
     ) -> SEQ | NDArray[np.float32] | tuple[SEQ, NDArray[np.float32]]:
         return super().__getitem__(idx)  # type: ignore
@@ -1560,7 +1930,7 @@ def __getitem__(
         self: RaggedDataset[MaybeRSEQ, MaybeRTRK],
         idx: Idx | tuple[Idx] | tuple[Idx, Idx | str | Sequence[str]],
     ) -> RSEQ | Ragged[np.float32] | tuple[RSEQ, Ragged[np.float32]]: ...
-    def __getitem__(
+    def __getitem__(  # type: ignore
         self, idx: Idx | tuple[Idx] | tuple[Idx, Idx | str | Sequence[str]]
     ) -> RSEQ | Ragged[np.float32] | tuple[RSEQ, Ragged[np.float32]]:
         return super().__getitem__(idx)  # type: ignore
diff --git a/python/genvarloader/_dataset/_indexing.py b/python/genvarloader/_dataset/_indexing.py
index 4a6d4c7a..7e9252f1 100644
--- a/python/genvarloader/_dataset/_indexing.py
+++ b/python/genvarloader/_dataset/_indexing.py
@@ -1,14 +1,18 @@
-from typing import List, Literal, Optional, Sequence, cast
+from __future__ import annotations
 
+from collections.abc import Sequence
+from typing import Literal, cast
+
+import awkward as ak
 import numpy as np
 from attrs import define, evolve
 from hirola import HashTable
 from more_itertools import collapse
 from numpy.typing import NDArray
-from typing_extensions import assert_never
+from typing_extensions import Self, assert_never
 
 from .._types import Idx, StrIdx
-from .._utils import idx_like_to_array, is_dtype
+from .._utils import idx_like_to_array, is_dtype, lengths_to_offsets
 
 
 @define
@@ -19,9 +23,9 @@ class DatasetIndexer:
     """Full map from input sample indices to on-disk sample indices."""
     s2i_map: HashTable
     """Map from input sample names to on-disk sample indices."""
-    region_subset_idxs: Optional[NDArray[np.integer]] = None
+    region_subset_idxs: NDArray[np.integer] | None = None
     """Which input regions are included in the subset."""
-    sample_subset_idxs: Optional[NDArray[np.integer]] = None
+    sample_subset_idxs: NDArray[np.integer] | None = None
     """Which input samples are included in the subset."""
 
     @classmethod
@@ -29,7 +33,7 @@ def from_region_and_sample_idxs(
         cls,
         r_idxs: NDArray[np.integer],
         s_idxs: NDArray[np.integer],
-        samples: List[str],
+        samples: list[str],
     ):
         _samples = np.array(samples)
         s2i_map = HashTable(
@@ -66,10 +70,10 @@ def n_samples(self) -> int:
         return len(self.sample_subset_idxs)
 
     @property
-    def samples(self) -> List[str]:
+    def samples(self) -> list[str]:
         if self.sample_subset_idxs is None:
-            return self.full_samples.tolist()  # type: ignore
-        return self.full_samples[self.sample_subset_idxs].tolist()  # type: ignore
+            return self.full_samples.tolist()
+        return self.full_samples[self.sample_subset_idxs].tolist()
 
     @property
     def shape(self) -> tuple[int, int]:
@@ -84,9 +88,9 @@ def __len__(self):
 
     def subset_to(
         self,
-        regions: Optional[Idx] = None,
-        samples: Optional[Idx] = None,
-    ) -> "DatasetIndexer":
+        regions: Idx | None = None,
+        samples: Idx | None = None,
+    ) -> Self:
         """Subset the dataset to specific regions and/or samples."""
         if regions is None and samples is None:
             return self
@@ -105,7 +109,7 @@ def subset_to(
             self, region_subset_idxs=region_idxs, sample_subset_idxs=sample_idxs
         )
 
-    def to_full_dataset(self) -> "DatasetIndexer":
+    def to_full_dataset(self) -> Self:
         """Return a full sized dataset, undoing any subsettting."""
         return evolve(self, region_subset_idxs=None, sample_subset_idxs=None)
 
@@ -143,12 +147,7 @@ def parse_idx(
             idx = np.ravel_multi_index(
                 np.ix_(r_idx.ravel(), s_idx.ravel()), self.full_shape
             )
-            if (
-                isinstance(r_idx, np.ndarray)
-                and r_idx.ndim > 1
-                or isinstance(s_idx, np.ndarray)
-                and s_idx.ndim > 1
-            ):
+            if r_idx.ndim > 1 or s_idx.ndim > 1:
                 out_reshape = (*r_idx.shape, *s_idx.shape)
             elif idx.ndim > 1:
                 out_reshape = idx.shape
@@ -178,6 +177,201 @@ def s2i(self, samples: StrIdx) -> Idx:
         return s2i(samples, self.s2i_map)
 
 
+@define
+class SpliceIndexer:
+    rows: HashTable
+    """Map from splice element names to row indices."""
+    splice_map: ak.Array
+    """Map from splice indices to region indices in splicing order."""
+    full_splice_map: ak.Array
+    """Non-subset map from splice indices to region indices."""
+    dsi: DatasetIndexer
+    row_subset_idxs: NDArray[np.intp] | None = None
+    """Subset of row indices."""
+
+    @classmethod
+    def _init(
+        cls,
+        names: Sequence[str] | NDArray[np.str_],
+        splice_map: ak.Array,
+        dsi: DatasetIndexer,
+    ) -> "SpliceIndexer":
+        _names = np.array(names)
+        rows = HashTable(
+            max=len(names) * 2,  # type: ignore | 2x size for perf > mem
+            dtype=_names.dtype,
+        )
+        rows.add(_names)
+
+        if (
+            ak.max(splice_map, None) >= dsi.n_regions
+            or ak.min(splice_map, None) < -dsi.n_regions
+        ):
+            raise ValueError(
+                "Found indices in the splice map that are out of bounds for the dataset."
+            )
+
+        return cls(
+            rows=rows,
+            splice_map=splice_map,
+            full_splice_map=splice_map,
+            dsi=dsi,
+            row_subset_idxs=None,
+        )
+
+    @property
+    def n_rows(self) -> int:
+        return len(self.splice_map)
+
+    @property
+    def n_samples(self) -> int:
+        return self.dsi.n_samples
+
+    @property
+    def shape(self) -> tuple[int, int]:
+        return self.n_rows, self.n_samples
+
+    @property
+    def full_shape(self) -> tuple[int, int]:
+        return len(self.full_splice_map), len(self.dsi.full_samples)
+
+    def __len__(self):
+        return self.n_rows * self.n_samples
+
+    def subset_to(
+        self,
+        rows: Idx | None = None,
+        samples: Idx | None = None,
+    ) -> tuple[Self, DatasetIndexer]:
+        """Subset to specific regions and/or samples."""
+        if rows is None and samples is None:
+            return self, self.dsi
+
+        if rows is not None:
+            row_idxs = idx_like_to_array(rows, self.n_rows)
+        else:
+            row_idxs = np.arange(self.n_rows, dtype=np.intp)
+
+        splice_map = cast(ak.Array, self.splice_map[row_idxs])
+        # splice_map is to absolute indices so don't subset dsi regions
+        sub_dsi = self.dsi.subset_to(samples=samples)
+        region_idxs = ak.flatten(splice_map, None).to_numpy()
+        eff_dsi = self.dsi.subset_to(regions=region_idxs, samples=samples)
+
+        return evolve(
+            self,
+            splice_map=splice_map,
+            dsi=sub_dsi,
+            row_subset_idxs=row_idxs,
+        ), eff_dsi
+
+    def to_full_dataset(self) -> Self:
+        """Return a full sized dataset, undoing any subsettting."""
+        return evolve(
+            self,
+            splice_map=self.full_splice_map,
+            dsi=self.dsi.to_full_dataset(),
+            row_subset_idxs=None,
+        )
+
+    def parse_idx(self, idx: StrIdx | tuple[StrIdx] | tuple[StrIdx, StrIdx]):
+        """Parse the index into a format suitable for indexing.
+
+        Parameters
+        ----------
+        idx
+            The index to parse. This can be a single index, a tuple of indices,
+            or a tuple of indices and a list of sample names.
+
+        Returns
+        -------
+        idx
+            1-D raveled dataset indices.
+        squeeze
+            Whether to squeeze the output.
+        out_reshape
+            The intended shape of the output, ready to be passed to reshape().
+        reducer
+            Indices for np.add.reduceat() to get the correct lengths for each splice element. Example:
+            spliced_lengths = np.add.reduceat(ragged.lengths, reduce_indices, axis=0)
+        rows
+            Indices of the splice elements.
+        s_idx
+            Indices of the samples.
+        """
+        out_reshape = None
+        squeeze = False
+
+        if not isinstance(idx, tuple):
+            rows = idx
+            samples = slice(None)
+        elif len(idx) == 1:
+            rows = idx[0]
+            samples = slice(None)
+        else:
+            rows, samples = idx
+
+        rows = self.r2i(rows)
+        samples = self.s2i(samples)
+
+        if isinstance(rows, (int, np.integer)) and isinstance(
+            samples, (int, np.integer)
+        ):
+            squeeze = True
+
+        r_idx = idx_like_to_array(rows, self.n_rows)
+        s_idx = idx_like_to_array(samples, self.n_samples)
+
+        idx_t = idx_type((r_idx, s_idx))
+        if idx_t == "basic":
+            # * FYI this will never execute because idx type is guaranteed to be adv or combo by casting
+            # basic indices to arrays above
+            idx = np.ravel_multi_index(np.ix_(r_idx, s_idx), self.full_shape)
+        elif idx_t == "adv":
+            idx = np.ravel_multi_index((r_idx, s_idx), self.full_shape)
+        elif idx_t == "combo":
+            idx = np.ravel_multi_index(
+                np.ix_(r_idx.ravel(), s_idx.ravel()), self.full_shape
+            )
+            if squeeze:
+                pass
+            elif r_idx.ndim > 1 or s_idx.ndim > 1:
+                out_reshape = (*r_idx.shape, *s_idx.shape)
+            elif idx.ndim > 1:
+                out_reshape = idx.shape
+        else:
+            assert_never(idx_t)
+
+        if idx_t != "combo" and idx.ndim > 1:
+            out_reshape = idx.shape
+        idx = idx.ravel()
+        (
+            r_idx,
+            s_idx,
+        ) = np.unravel_index(idx, self.full_shape)
+
+        r_idx = self.splice_map[r_idx]
+        lengths = ak.count(r_idx, -1)
+        if not isinstance(lengths, np.integer):
+            lengths = lengths.to_numpy()
+        lengths = cast(NDArray[np.int64], lengths)
+        offsets = lengths_to_offsets(lengths)
+        r_idx = ak.flatten(r_idx, -1).to_numpy()
+        s_idx = s_idx.repeat(lengths)
+
+        ds_idx, *_ = self.dsi.parse_idx((r_idx, s_idx))
+
+        return ds_idx, squeeze, out_reshape, offsets
+
+    def r2i(self, regions: StrIdx) -> Idx:
+        """Convert region names to region indices."""
+        return s2i(regions, self.rows)
+
+    def s2i(self, samples: StrIdx) -> Idx:
+        """Convert sample names to sample indices."""
+        return s2i(samples, self.dsi.s2i_map)
+
+
 def s2i(str_idx: StrIdx, map: HashTable) -> Idx:
     """Convert a string index to an integer index using a hirola.HashTable."""
     if (
diff --git a/python/genvarloader/_dataset/_reconstruct.py b/python/genvarloader/_dataset/_reconstruct.py
index a349ed2e..b3a75281 100644
--- a/python/genvarloader/_dataset/_reconstruct.py
+++ b/python/genvarloader/_dataset/_reconstruct.py
@@ -30,7 +30,11 @@
 from .._ragged import INTERVAL_DTYPE, RaggedAnnotatedHaps, RaggedIntervals, RaggedSeqs
 from .._utils import lengths_to_offsets
 from .._variants._records import RaggedAlleles
-from ._genotypes import get_diffs_sparse, reconstruct_haplotypes_from_sparse
+from ._genotypes import (
+    choose_exonic_variants,
+    get_diffs_sparse,
+    reconstruct_haplotypes_from_sparse,
+)
 from ._indexing import DatasetIndexer
 from ._intervals import intervals_to_tracks, tracks_to_intervals
 from ._rag_variants import RaggedVariants
@@ -130,6 +134,7 @@ class Haps(Reconstructor[_H]):
     """Shape: (regions, samples, ploidy). The genotypes in the dataset. This is memory mapped."""
     dosages: SparseDosages | None
     kind: type[_H]
+    filter: Literal["exonic"] | None
 
     @classmethod
     def from_path(
@@ -199,20 +204,34 @@ def from_path(
             genotypes=genotypes,
             dosages=dosages,
             kind=RaggedSeqs,
+            filter=None,
         )
 
     def _haplotype_ilens(
         self,
         idx: NDArray[np.integer],
-        regions: NDArray[np.int32],
+        regions: NDArray[np.integer],
         deterministic: bool,
         keep: NDArray[np.bool_] | None = None,
-        keep_offsets: NDArray[np.int64] | None = None,
+        keep_offsets: NDArray[np.integer] | None = None,
     ) -> NDArray[np.int32]:
         """`idx` must be 1D."""
         # (b p)
         geno_offset_idxs = self.get_geno_offset_idx(idx, self.genotypes)
 
+        if self.filter == "exonic":
+            keep, keep_offsets = choose_exonic_variants(
+                starts=regions[:, 1],
+                ends=regions[:, 2],
+                geno_offset_idxs=geno_offset_idxs,
+                geno_v_idxs=self.genotypes.data,
+                geno_offsets=self.genotypes.offsets,
+                v_starts=self.variants.v_starts,
+                ilens=self.variants.ilens,
+            )
+        else:
+            keep, keep_offsets = None, None
+
         # (r s p)
         hap_ilens = get_diffs_sparse(
             geno_offset_idxs=geno_offset_idxs,
@@ -222,6 +241,8 @@ def _haplotype_ilens(
             q_starts=regions[:, 1],
             q_ends=regions[:, 2],
             v_starts=self.variants.v_starts,
+            keep=keep,
+            keep_offsets=keep_offsets,
         )
 
         return hap_ilens.reshape(-1, self.genotypes.shape[-1])
@@ -251,7 +272,7 @@ def __call__(
     def get_haps_and_shifts(
         self,
         idx: NDArray[np.integer],
-        regions: NDArray[np.int32],
+        regions: NDArray[np.integer],
         output_length: Literal["ragged", "variable"] | int,
         rng: np.random.Generator,
         deterministic: bool,
@@ -271,8 +292,24 @@ def get_haps_and_shifts(
 
         geno_offset_idx = self.get_geno_offset_idx(idx, self.genotypes)
 
+        if self.filter == "exonic":
+            keep, keep_offsets = choose_exonic_variants(
+                starts=regions[:, 1],
+                ends=regions[:, 2],
+                geno_offset_idxs=geno_offset_idx,
+                geno_v_idxs=self.genotypes.data,
+                geno_offsets=self.genotypes.offsets,
+                v_starts=self.variants.v_starts,
+                ilens=self.variants.ilens,
+            )
+        else:
+            keep = None
+            keep_offsets = None
+
         # (b p)
-        diffs = self._haplotype_ilens(idx, regions, deterministic)
+        diffs = self._haplotype_ilens(
+            idx, regions, deterministic, keep=keep, keep_offsets=keep_offsets
+        )
         hap_lengths = lengths[:, None] + diffs
 
         if deterministic or isinstance(output_length, str):
@@ -310,8 +347,8 @@ def get_haps_and_shifts(
                 regions=regions,
                 out_offsets=out_offsets,
                 shifts=shifts,
-                keep=None,
-                keep_offsets=None,
+                keep=keep,
+                keep_offsets=keep_offsets,
                 annotate=False,
             )
         elif issubclass(self.kind, RaggedAnnotatedHaps):
@@ -320,8 +357,8 @@ def get_haps_and_shifts(
                 regions=regions,
                 out_offsets=out_offsets,
                 shifts=shifts,
-                keep=None,
-                keep_offsets=None,
+                keep=keep,
+                keep_offsets=keep_offsets,
                 annotate=True,
             )
             out = RaggedAnnotatedHaps(haps, maybe_annot_v_idx, maybe_annot_pos)
@@ -330,8 +367,8 @@ def get_haps_and_shifts(
                 idx=idx,
                 regions=regions,
                 shifts=shifts,
-                keep=None,
-                keep_offsets=None,
+                keep=keep,
+                keep_offsets=keep_offsets,
             )
         else:
             assert_never(self.kind)
@@ -342,8 +379,8 @@ def get_haps_and_shifts(
             shifts,
             diffs,
             hap_lengths,
-            None,
-            None,
+            keep,
+            keep_offsets,
         )
 
     @staticmethod
@@ -360,10 +397,10 @@ def get_geno_offset_idx(
     def _get_variants(
         self,
         idx: NDArray[np.integer],
-        regions: NDArray[np.int32],
-        shifts: NDArray[np.int32],
+        regions: NDArray[np.integer],
+        shifts: NDArray[np.integer],
         keep: NDArray[np.bool_] | None,
-        keep_offsets: NDArray[np.int64] | None,
+        keep_offsets: NDArray[np.integer] | None,
     ) -> RaggedVariants:
         # TODO: maybe filter variants for region, shifts, and keep?
         r, s = np.unravel_index(idx, self.genotypes.shape[:2])
@@ -408,34 +445,34 @@ def _get_variants(
     @overload
     def _get_haplotypes(
         self,
-        geno_offset_idx: NDArray[np.intp],
-        regions: NDArray[np.int32],
-        out_offsets: NDArray[np.int64],
-        shifts: NDArray[np.int32],
+        geno_offset_idx: NDArray[np.integer],
+        regions: NDArray[np.integer],
+        out_offsets: NDArray[np.integer],
+        shifts: NDArray[np.integer],
         keep: NDArray[np.bool_] | None,
-        keep_offsets: NDArray[np.int64] | None,
+        keep_offsets: NDArray[np.integer] | None,
         annotate: Literal[False],
     ) -> Ragged[np.bytes_]: ...
     @overload
     def _get_haplotypes(
         self,
-        geno_offset_idx: NDArray[np.intp],
-        regions: NDArray[np.int32],
-        out_offsets: NDArray[np.int64],
-        shifts: NDArray[np.int32],
+        geno_offset_idx: NDArray[np.integer],
+        regions: NDArray[np.integer],
+        out_offsets: NDArray[np.integer],
+        shifts: NDArray[np.integer],
         keep: NDArray[np.bool_] | None,
-        keep_offsets: NDArray[np.int64] | None,
+        keep_offsets: NDArray[np.integer] | None,
         annotate: Literal[True],
     ) -> tuple[Ragged[np.bytes_], Ragged[np.int32], Ragged[np.int32]]: ...
 
     def _get_haplotypes(
         self,
-        geno_offset_idx: NDArray[np.intp],
-        regions: NDArray[np.int32],
-        out_offsets: NDArray[np.int64],
-        shifts: NDArray[np.int32],
+        geno_offset_idx: NDArray[np.integer],
+        regions: NDArray[np.integer],
+        out_offsets: NDArray[np.integer],
+        shifts: NDArray[np.integer],
         keep: NDArray[np.bool_] | None,
-        keep_offsets: NDArray[np.int64] | None,
+        keep_offsets: NDArray[np.integer] | None,
         annotate: bool,
     ) -> (
         Ragged[np.bytes_]
diff --git a/python/genvarloader/_dataset/_write.py b/python/genvarloader/_dataset/_write.py
index f66bcaf9..62277ea4 100644
--- a/python/genvarloader/_dataset/_write.py
+++ b/python/genvarloader/_dataset/_write.py
@@ -241,7 +241,7 @@ def _write_from_vcf(path: Path, bed: pl.DataFrame, vcf: VCF, max_mem: int):
 
     pl.DataFrame(
         {
-            "POS": vcf._index.gr.df["Start"],
+            "POS": vcf._index.df["POS"] - 1,
             "ALT": vcf._index.df["ALT"].list.first(),
             "ILEN": vcf._index.df.select(
                 pl.col("ALT").list.first().str.len_bytes().cast(pl.Int32)
diff --git a/python/genvarloader/_dummy.py b/python/genvarloader/_dummy.py
index 244ce4d9..942ef908 100644
--- a/python/genvarloader/_dummy.py
+++ b/python/genvarloader/_dummy.py
@@ -7,7 +7,7 @@
 from genoray._svar import POS_TYPE, SparseGenotypes
 from natsort import natsorted
 
-from ._dataset._impl import RaggedDataset
+from ._dataset._impl import RaggedDataset, _parse_splice_info
 from ._dataset._indexing import DatasetIndexer
 from ._dataset._intervals import tracks_to_intervals
 from ._dataset._reconstruct import Haps, HapsTracks, Tracks, TrackType, _Variants
@@ -18,10 +18,24 @@
 from ._variants._records import RaggedAlleles
 
 
-def get_dummy_dataset():
+def get_dummy_dataset(spliced: bool = False):
     """Return a dummy :class:`Dataset <genvarloader.Dataset>`  with 4 regions, 4 samples, max jitter of 2, a reference genome of all :code:`"N"`, genotypes, and
     1 track "read-depth" where each track is :code:`[1, 2, 3, 4, 5, 6]` in the reference coordinate system, where :code:`3` is aligned
     with each region's start coordinate. Is initialized to return ragged haplotypes and tracks with no jitter and deterministic reconstruction algorithms.
+
+    Parameters
+    ----------
+    spliced
+        If :code:`True`, the dataset will be setup for splicing with all regions moved to chromosome 1 and
+        a splice indexer with 2 genes, "tp53" and "shh", corresponding to regions:
+
+        .. code-block:: python
+
+            {
+                "tp53": [3, 0, 2],
+                "shh": [1],
+            }
+
     """
     max_jitter = 2
 
@@ -35,6 +49,8 @@ def get_dummy_dataset():
             "chromStart": [5, 13, 8, 2],
             "chromEnd": [8, 16, 11, 5],
             "strand": ["+", "-", "+", "+"],
+            "gene": ["tp53", "shh", "tp53", "tp53"],
+            "exon": [3, 1, 1, 2],
         }
     )
     n_regions = len(dummy_bed)
@@ -98,6 +114,7 @@ def get_dummy_dataset():
         genotypes=dummy_genos,
         dosages=None,
         kind=RaggedSeqs,
+        filter=None,
     )
 
     # (r s), want tracks of [1, 2, 3, 4, 5] for each region so that pad values of 0 are obvious
@@ -146,6 +163,13 @@ def get_dummy_dataset():
 
     dummy_recon = HapsTracks(dummy_haps, dummy_tracks)
 
+    if spliced:
+        dummy_bed = dummy_bed.with_columns(chrom=pl.lit("chr1"))
+        dummy_spi, sp_bed = _parse_splice_info(("gene", "exon"), dummy_bed, dummy_idxer)
+    else:
+        dummy_spi = None
+        sp_bed = None
+
     dummy_dataset: RaggedDataset[RaggedSeqs, Ragged[np.float32]] = RaggedDataset(
         path=Path("dummy"),
         output_length="ragged",
@@ -157,8 +181,10 @@ def get_dummy_dataset():
         rc_neg=True,
         transform=None,
         _full_bed=dummy_bed,
+        _spliced_bed=sp_bed,
         _full_regions=dummy_regions,
         _idxer=dummy_idxer,
+        _sp_idxer=dummy_spi,
         _seqs=dummy_haps,
         _tracks=dummy_tracks,
         _recon=dummy_recon,
diff --git a/python/genvarloader/_ragged.py b/python/genvarloader/_ragged.py
index 2213b18b..8a021dfd 100644
--- a/python/genvarloader/_ragged.py
+++ b/python/genvarloader/_ragged.py
@@ -9,7 +9,7 @@
 from einops import repeat
 from numpy.typing import NDArray
 from phantom import Phantom
-from seqpro._ragged import Ragged
+from seqpro._ragged import OFFSET_TYPE, Ragged
 
 from ._types import DTYPE, AnnotatedHaps
 
@@ -168,11 +168,16 @@ def pad_ragged(
 #! for whatever reason, this causes data corruption with parallel=True?!
 @nb.njit(nogil=True, cache=True)
 def _rc_helper(
-    data: NDArray[np.uint8], offsets: NDArray[np.int64], mask: NDArray[np.bool_]
+    data: NDArray[np.uint8], offsets: NDArray[OFFSET_TYPE], mask: NDArray[np.bool_]
 ) -> NDArray[np.uint8]:
     out = data.copy()
     for i in nb.prange(len(offsets) - 1):
-        start, end = offsets[i], offsets[i + 1]
+        if offsets.ndim == 1:
+            if i == len(offsets) - 1:
+                continue
+            start, end = offsets[i], offsets[i + 1]
+        else:
+            start, end = offsets[i]
         _data = data[start:end]
         _out = out[start:end]
         if mask[i]:
@@ -195,10 +200,17 @@ def reverse_complement(
 
 #! for whatever reason, this causes data corruption with parallel=True?!
 @nb.njit(nogil=True, cache=True)
-def _reverse_helper(data: NDArray, offsets: NDArray[np.int64], mask: NDArray[np.bool_]):
-    for i in nb.prange(len(offsets) - 1):
+def _reverse_helper(
+    data: NDArray, offsets: NDArray[OFFSET_TYPE], mask: NDArray[np.bool_]
+):
+    for i in nb.prange(len(offsets)):
         if mask[i]:
-            start, end = offsets[i], offsets[i + 1]
+            if offsets.ndim == 1:
+                if i == len(offsets) - 1:
+                    continue
+                start, end = offsets[i], offsets[i + 1]
+            else:
+                start, end = offsets[i]
             data[start:end] = np.flip(data[start:end])