diff --git a/.github/workflows/build_deploy_main.yaml b/.github/workflows/build_deploy_main.yaml
index be8a4fb9..6f1ab59a 100644
--- a/.github/workflows/build_deploy_main.yaml
+++ b/.github/workflows/build_deploy_main.yaml
@@ -3,6 +3,7 @@ on:
   push:
     branches:
       - main
+      - dev
 
 permissions:
   id-token: write
@@ -18,7 +19,7 @@ jobs:
     runs-on: ubuntu-latest
     environment: production
     outputs:
-      matrix: ${{ steps.get_next_version.outputs.next_version }}
+      next_version: ${{ steps.get_next_version.outputs.next_version }}
     steps:
       - name: 'Checkout repo'
         uses: actions/checkout@v4
@@ -55,13 +56,12 @@ jobs:
     environment: production
     needs:
       - get_next_version
-    if: ${{ needs.get_next_version.outputs.matrix != '{"include":[]}' && needs.get_next_version.outputs.matrix != '' }}
-    strategy:
-      matrix: ${{ fromJson(needs.get_next_version.outputs.matrix) }}
+    if: ${{ needs.get_next_version.outputs.next_version != '' && github.event.pull_request.draft == false }}
     env:
       DOCKER_BUILDKIT: 1
       BUILDKIT_PROGRESS: plain
       CLOUDSDK_CORE_DISABLE_PROMPTS: 1
+      TAG: ${{ needs.get_next_version.outputs.next_version }}${{ github.ref == 'refs/heads/dev' && '_dev' || '' }}
     steps:
       - uses: actions/checkout@v4
         with:
@@ -84,9 +84,8 @@ jobs:
       - name: 'build image'
         run: |
           docker build . -f src/talos/cpg_internal_scripts/CPG_Dockerfile \
-          --build-arg VERSION=${{ matrix.tag }} \
-          --tag ${{ env.IMAGES_PREFIX }}/${{ matrix.name }}:${{ matrix.tag }}
+          --tag ${{ env.IMAGES_PREFIX }}/talos:${{ env.TAG }}
 
       - name: 'push image'
         run: |
-          docker push "${{ env.IMAGES_PREFIX }}/${{ matrix.name }}:${{ matrix.tag }}"
+          docker push "${{ env.IMAGES_PREFIX }}/talos:${{ env.TAG }}"
diff --git a/.github/workflows/dev_auto_build.yaml b/.github/workflows/dev_auto_build.yaml
index a8ff6619..d5e5f1ea 100644
--- a/.github/workflows/dev_auto_build.yaml
+++ b/.github/workflows/dev_auto_build.yaml
@@ -24,7 +24,7 @@ jobs:
     runs-on: ubuntu-latest
     environment: production
     outputs:
-      matrix: ${{ steps.get_next_version.outputs.next_version }}
+      next_version: ${{ steps.get_next_version.outputs.next_version }}
     steps:
       - name: 'Checkout repo'
         uses: actions/checkout@v4
@@ -61,9 +61,7 @@ jobs:
     environment: production
     needs:
       - get_next_version
-    if: ${{ needs.get_next_version.outputs.matrix != '{"include":[]}' && needs.get_next_version.outputs.matrix != '' && github.event.pull_request.draft == false }}
-    strategy:
-      matrix: ${{ fromJson(needs.get_next_version.outputs.matrix) }}
+    if: ${{ needs.get_next_version.outputs.next_version != '' && github.event.pull_request.draft == false }}
     steps:
       - name: 'checkout repo'
         uses: actions/checkout@v4
@@ -88,12 +86,11 @@ jobs:
       - name: 'build image with get_next_version'
         run: |
           docker build . -f src/talos/cpg_internal_scripts/CPG_Dockerfile \
-          --build-arg VERSION=${{ matrix.tag }} \
-          --tag ${{ env.IMAGES_PREFIX }}/${{ matrix.name }}:${{ matrix.tag }}
+          --tag ${{ env.IMAGES_PREFIX }}/talos:${{ needs.get_next_version.outputs.next_version }}
 
       - name: 'Push to dev artifactory'
         run: |
-          docker push "${{ env.IMAGES_PREFIX }}/${{ matrix.name }}:${{ matrix.tag }}" | tee push.log
+          docker push "${{ env.IMAGES_PREFIX }}/talos:${{ needs.get_next_version.outputs.next_version }}" | tee push.log
           digest=$(grep 'digest:' push.log | grep -o 'sha256:[0-9a-f]*')
           echo ":package: Link to image:" >> $GITHUB_STEP_SUMMARY
-          echo "https://console.cloud.google.com/artifacts/docker/cpg-common/australia-southeast1/images-dev/${{ matrix.name }}/$digest?project=cpg-common" >> $GITHUB_STEP_SUMMARY
+          echo "https://console.cloud.google.com/artifacts/docker/cpg-common/australia-southeast1/images-dev/talos/$digest?project=cpg-common" >> $GITHUB_STEP_SUMMARY
diff --git a/.github/workflows/get_version.py b/.github/workflows/get_version.py
index b46b5341..e880bbd3 100644
--- a/.github/workflows/get_version.py
+++ b/.github/workflows/get_version.py
@@ -1,5 +1,4 @@
 import json
-import logging
 import os
 import re
 import subprocess
@@ -9,11 +8,11 @@
 def extract_version_from_file(file_path: str) -> str | None:
     """
     Extract the version from a Dockerfile by searching for a line like:
-    ARG VERSION=1.0.0
+    ENV VERSION=1.0.0
     """
     with open(file_path) as f:
         content = f.read()
-    pattern = re.compile(r'^\s*ARG\s+VERSION\s*=\s*([^\s]+)', re.MULTILINE)
+    pattern = re.compile(r'^\s*ENV\s+VERSION\s*=\s*([^\s]+)', re.MULTILINE)
     match = pattern.search(content)
     return match.group(1) if match else None
 
@@ -27,48 +26,31 @@ def get_next_version_tag(folder: str, version: str) -> str:
         'GCP_BASE_IMAGE',
         'australia-southeast1-docker.pkg.dev/cpg-common/images',
     )
-    base_image_path_archive = os.environ.get(
-        'GCP_BASE_ARCHIVE_IMAGE',
-        'australia-southeast1-docker.pkg.dev/cpg-common/images-archive',
-    )
-    full_image_name_prod = f'{base_image_path_prod}/{folder}'
-    full_image_name_archive = f'{base_image_path_archive}/{folder}'
-
-    tags_list = []
-
-    logging.basicConfig(level=logging.ERROR)
-
-    for full_image_name in [full_image_name_prod, full_image_name_archive]:
-        cmd = [
-            'gcloud',
-            'container',
-            'images',
-            'list-tags',
-            full_image_name,
-            '--format=json',
-        ]
-        try:
-            result = subprocess.run(cmd, capture_output=True, text=True, check=True)  # noqa: S603
-        except subprocess.CalledProcessError:
-            logging.error(f'Failed to list tags for {full_image_name}')
-            continue
-
-        # If existing tags are found, proceed to determine the next version.
-        tags_list += json.loads(result.stdout)
+    cmd = [
+        'gcloud',
+        'container',
+        'images',
+        'list-tags',
+        f'{base_image_path_prod}/{folder}',
+        '--format=json',
+    ]
 
     max_suffix = 0
     pattern = re.compile(rf'^{re.escape(version)}-(\d+)$')
 
-    # If no tags are found, it returns the next version as -1.
-    for entry in tags_list:
-        tags = entry.get('tags', [])
-        for tag in tags:
-            match = pattern.match(tag)
+    try:
+        result = subprocess.run(cmd, capture_output=True, text=True, check=True)  # noqa: S603
+        for block in json.loads(result.stdout):
+            if not block.get('tags', []):
+                continue
+            match = pattern.match(block['tags'][0])
             if match:
                 num = int(match.group(1))
                 max_suffix = max(max_suffix, num)
-    new_suffix = max_suffix + 1
-    return f'{version}-{new_suffix}'
+    except subprocess.CalledProcessError as err:
+        raise RuntimeError('Failed to list tags for the given image') from err
+
+    return f'{version}-{max_suffix + 1}'
 
 
 def main():
@@ -77,17 +59,13 @@ def main():
     current_version = extract_version_from_file(dockerfile_name)
     if current_version is None:
         # Throw an error here
-        raise NotImplementedError('The Dockerfile needs to contain a version string in the format "ARG VERSION=x.x.x"')
+        raise NotImplementedError('The Dockerfile needs to contain a version string in the format "ENV VERSION=x.x.x"')
 
     # Determine the next available tag based on current_version.
     new_tag = get_next_version_tag(container_name, current_version)
 
-    include_entries = [{'name': container_name, 'tag': new_tag}]
-
-    # Build the final matrix structure.
-    matrix = {'include': include_entries}
-    print(json.dumps(matrix, separators=(',', ':')), file=sys.stderr)
-    print(json.dumps(matrix, separators=(',', ':')), end='')
+    print(new_tag, file=sys.stderr)
+    print(new_tag, end='')
 
 
 main()
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 9dfc4ddd..f5c3b2af 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -28,7 +28,7 @@ repos:
 
   # Static type analysis (as much as it's possible in python using type hints)
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.20.2
+    rev: v2.1.0
     hooks:
       - id: mypy
         args: [--pretty, --show-error-codes, --install-types, --non-interactive]
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 73c78679..77821964 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -17,6 +17,19 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 <!--changelog-start-->
 <!--latest-start-->
 
+[11.1.0] - 2026-07
+
+### Removed
+
+  * All references to the private build of BCFtools. the `--greedy` behaviour is now released as standard in BCFtools 1.24
+
+### Added
+
+  * STR functionality! This is currently specific to STRipy data, with a script to translate STRipy JSON data into a pseudo-joint called VCF
+  * If there is appetite for STR functionality, this could be expanded to other STR callers.
+
+<!--latest-end-->
+
 [11.0.0] - 2026-05-15
 
 ### Added
@@ -48,8 +61,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 > NOTE! For existing Talos users, obtaining this extra annotation will require the re-run of the annotation workflow. One example gene which is known to be obscured by this default behaviour was RNU2-2, but there may be others.
 
-<!--latest-end-->
-
 [10.0.1] - 2026-03-23
 
 ### Fixed
diff --git a/Dockerfile b/Dockerfile
index e46962c3..51641f03 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -19,49 +19,35 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 
 FROM base AS bcftools_compiler
 
-ARG BCFTOOLS_VERSION=1.23.1
-
-# AS OF 11.0.0, Talos is building BCFtools from a private fork. This fork contains a single change - csq applies annotations
-# to both coding and non-coding genes in the event of overlapping genes. By default BCFtools skips non-coding gene annotation
-# if a coding transcript consequence was detected, but in practice this is masking clinically relevant non-coding gene variation
-# in cases where the non-coding gene overlaps with a non-clinically relevant coding gene.
-# The change made is to always search for non-coding variation, but to mask non-coding consequences in otherwise coding transcripts
-# if a coding change was already detected.
+ARG BCFTOOLS_VERSION=1.24
+
 RUN apt-get update && apt-get install --no-install-recommends -y \
-    autoconf \
     gcc \
-    git \
     libbz2-dev \
     libcurl4-openssl-dev \
     liblzma-dev \
     libssl-dev \
     make \
     zlib1g-dev && \
-    wget https://github.com/samtools/htslib/releases/download/${BCFTOOLS_VERSION}/htslib-${BCFTOOLS_VERSION}.tar.bz2 && \
-    tar -xf htslib-${BCFTOOLS_VERSION}.tar.bz2 && \
-    cd htslib-${BCFTOOLS_VERSION} && \
-    ./configure --enable-libcurl && \
-    make && \
-    make DESTDIR=/bcftools_install install && \
-    cd .. && \
-    git clone https://github.com/populationgenomics/bcftools.git && \
-    cd bcftools && \
-    autoheader && \
-    autoconf && \
+    rm -rf /var/lib/apt/lists/* && \
+    wget https://github.com/samtools/bcftools/releases/download/${BCFTOOLS_VERSION}/bcftools-${BCFTOOLS_VERSION}.tar.bz2 && \
+    tar -xf bcftools-${BCFTOOLS_VERSION}.tar.bz2 && \
+    cd bcftools-${BCFTOOLS_VERSION} && \
     ./configure --enable-libcurl --enable-s3 --enable-gcs && \
     make && \
     strip bcftools plugins/*.so && \
+    make DESTDIR=/bcftools_install install && \
+    cd htslib-${BCFTOOLS_VERSION} && \
+    make && \
     make DESTDIR=/bcftools_install install
 
 FROM base AS talos
 
 COPY --from=bcftools_compiler /bcftools_install/usr/local/bin/* /usr/local/bin/
 COPY --from=bcftools_compiler /bcftools_install/usr/local/libexec/bcftools/* /usr/local/libexec/bcftools/
-COPY --from=bcftools_compiler /bcftools_install/usr/local/lib/ /usr/local/lib/
-RUN ldconfig
 
 ARG ECHTVAR_VERSION=v0.2.2
-ARG VERSION=11.0.3
+ENV VERSION=11.1.0
 
 RUN wget -q -O /bin/echtvar "https://github.com/brentp/echtvar/releases/download/${ECHTVAR_VERSION}/echtvar" && \
     chmod +x /bin/echtvar
diff --git a/README.md b/README.md
index 50076a0d..d25b6568 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ There are two primary workflows:
 To build the Docker image:
 
 ```
-docker build -t talos:11.0.3 .
+docker build -t talos:11.1.0 .
 ```
 
 ### **2. Download Annotation Resources**
diff --git a/docs/getting-started.md b/docs/getting-started.md
index 7e29b012..9a57da10 100644
--- a/docs/getting-started.md
+++ b/docs/getting-started.md
@@ -25,7 +25,7 @@ You will need:
 Build the Talos Docker image locally:
 
 ```bash
-docker build -t talos:11.0.3 .
+docker build -t talos:11.1.0 .
 ```
 
 ---
@@ -81,7 +81,10 @@ The optional columns (history, ext_ids, seqr_map, mito) can be omitted completel
 | `seqr_map` | optional | ID mapping for Seqr hyperlinks in the HTML report.                             |
 | `mito`     | optional | Path to a joint-called mitochondrial VCF.                                      |
 
-### Input types
+STR can be handled by Talos, but only data called by STRipy, and aggregated into a joint VCF format by the script `talos/scripts/stripy_json_to_vcf.py`.
+This is not yet exposed in the nextflow implementation, but may be in future.
+
+### Small-variant VCF input types
 
 <table>
     <colgroup>
diff --git a/nextflow.config b/nextflow.config
index 434e4a32..26faa58e 100644
--- a/nextflow.config
+++ b/nextflow.config
@@ -16,8 +16,8 @@ params {
     large_files = "large_files"
     ref_genome = "${params.large_files}/ref.fa"
 
-    // Docker container - "docker build -t talos:11.0.3 ."
-    container = 'talos:11.0.3'
+    // Docker container - "docker build -t talos:11.1.0 ."
+    container = 'talos:11.1.0'
 
     // MANE transcript resource
     mane = "${params.large_files}/MANE.GRCh38.v1.5.summary.txt.gz"
@@ -31,6 +31,7 @@ params {
     // Result files from the CreateRoiFromGff3 nextflow module
     ensembl_bed = "${params.processed_annotations}/GRCh38.bed"
     ensembl_merged_bed = "${params.processed_annotations}/GRCh38_merged.bed"
+    ensembl_symbol_lookup = "${params.processed_annotations}/GRCh38_symbol_to_ensg.json"
 
     // AlphaMissense raw data, and reformatted as an echtvar-compatible zip
     alphamissense_tsv = "${params.large_files}/AlphaMissense_hg38.tsv.gz"
diff --git a/nextflow/inputs/config.toml b/nextflow/inputs/config.toml
index f01bb6c4..e2554b4a 100644
--- a/nextflow/inputs/config.toml
+++ b/nextflow/inputs/config.toml
@@ -10,7 +10,7 @@ forced_panels = [ 144, 239]
 # 3 = Green only
 # 2 = Amber or Green only
 # 1 = Red, Amber, or Green
-confidence_level = 1
+confidence_level = 3
 
 [[GeneratePanelData.manual_overrides]]
 # this section permits the manual addition of genes to the panel data
diff --git a/nextflow/modules/annotation/AnnotateCsqWithBcftools/main.nf b/nextflow/modules/annotation/AnnotateCsqWithBcftools/main.nf
index 25712ed2..3c725b5a 100644
--- a/nextflow/modules/annotation/AnnotateCsqWithBcftools/main.nf
+++ b/nextflow/modules/annotation/AnnotateCsqWithBcftools/main.nf
@@ -14,6 +14,7 @@ process AnnotateCsqWithBcftools {
     set -euo pipefail
 
     bcftools csq --force -f "${reference}" \
+        --greedy 1 \
         --local-csq \
         -g ${gff3} \
         --unify-chr-names 'chr,-,chr' \
diff --git a/nextflow/modules/prep/CreateRoiFromGff3/main.nf b/nextflow/modules/prep/CreateRoiFromGff3/main.nf
index 3465b69b..78970be2 100644
--- a/nextflow/modules/prep/CreateRoiFromGff3/main.nf
+++ b/nextflow/modules/prep/CreateRoiFromGff3/main.nf
@@ -8,9 +8,11 @@ process CreateRoiFromGff3 {
     // generates a BED file one-gene-per-line, for per-gene annotations
     // generates an BED with overlapping intervals merged for filtering
     // sorts both
+    // also generates a JSON file mapping symbols to ENSG IDs
     output:
         path "GRCh38.bed", emit: bed
         path "GRCh38_merged.bed", emit: merged_bed
+        path "GRCh38_symbol_to_ensg.json", emit: json_lookup
 
     script:
         """
@@ -19,7 +21,8 @@ process CreateRoiFromGff3 {
         python -m talos.annotation_scripts.create_roi_from_gff3 \
             --gff3 ${gff} \
             --unmerged_output unsorted_GRCh38.bed \
-            --merged_output unsorted_merged_GRCh38.bed
+            --merged_output unsorted_merged_GRCh38.bed \
+            --json_output GRCh38_symbol_to_ensg.json
 
         sort -k1,1V -k2,2n unsorted_GRCh38.bed > GRCh38.bed
         sort -k1,1V -k2,2n unsorted_merged_GRCh38.bed > GRCh38_merged.bed
diff --git a/nextflow_schema.json b/nextflow_schema.json
index 2cf64099..098d47c4 100644
--- a/nextflow_schema.json
+++ b/nextflow_schema.json
@@ -33,7 +33,7 @@
         },
         "container": {
           "type": "string",
-          "default": "talos:11.0.3",
+          "default": "talos:11.1.0",
           "description": "Docker container used by processes."
         },
         "mane": {
diff --git a/preparation.nf b/preparation.nf
index e4a67977..4f6f78a3 100644
--- a/preparation.nf
+++ b/preparation.nf
@@ -121,13 +121,15 @@ workflow {
     // generate the Region-of-interest BED file from Ensembl GFF3
     // generates a per-gene BED file with ID annotations
     // and a overlap-merged version of the same for more efficient region filtering
-    if (!file(params.ensembl_bed).exists() || !file(params.ensembl_merged_bed).exists()) {
+    if (!file(params.ensembl_bed).exists() || !file(params.ensembl_merged_bed).exists() || !file(params.ensembl_symbol_lookup).exists()) {
         CreateRoiFromGff3(ch_gff)
         ch_bed = CreateRoiFromGff3.out.bed
         ch_merged_bed = CreateRoiFromGff3.out.merged_bed
+        ch_symbol_lookup = CreateRoiFromGff3.out.json_lookup
     } else {
         ch_merged_bed = channel.fromPath(params.ensembl_merged_bed, checkIfExists: true)
         ch_bed = channel.fromPath(params.ensembl_bed, checkIfExists: true)
+        ch_symbol_lookup = channel.fromPath(params.ensembl_symbol_lookup, checkIfExists: true)
     }
 
     // pull and parse the MANE data into a Hail Table
@@ -156,6 +158,7 @@ workflow {
         alphamissense = ch_alphamissense_zip
         bed = ch_bed
         merged_bed = ch_merged_bed
+        symbol_lookup = ch_symbol_lookup
         clinvar_all = ch_clinvar_all
         clinvar_pm5 = ch_clinvar_pm5
         mitimpact = ch_mitimpact_zip
@@ -172,6 +175,8 @@ output {
     }
     merged_bed {
     }
+    symbol_lookup {
+    }
     clinvar_all {
     }
     clinvar_pm5 {
diff --git a/pyproject.toml b/pyproject.toml
index a469abea..b48172d2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "hatchling.build"
 name='talos'
 description='Centre for Population Genomics Variant Prioritisation'
 readme = "README.md"
-version="11.0.3"
+version="11.1.0"
 requires-python = ">=3.10,<3.12"
 license-files = ["LICENSE"]
 classifiers=[
@@ -23,7 +23,7 @@ dependencies=[
     'hail~=0.2.137',
     'clinvarbitration~=2.2.7',
     'cloudpathlib[all]>=0.16.0',
-    'cpg-utils>=5.4.1',
+    'cpg-utils~=5.7',
     'cyvcf2>=0.30.18',
     'grpcio-status>=1.48,<1.50',
     'httpx>=0.27.0',
@@ -57,6 +57,9 @@ docs = [
     'mkdocs-material>=9.5',
     'mkdocs-include-markdown-plugin>=6.2',
 ]
+stripy = [
+    'pysam'
+]
 
 [project.urls]
 Repository = "https://github.com/populationgenomics/talos"
@@ -102,7 +105,8 @@ ignore = [
     "PLR0913", # Too many arguments in function (> 5)
     "C901", # method is too complex (> 10 conditions)
     "S311", # Standard pseudo-random generators are not suitable for cryptographic purposes
-    "PLR2004" #  Magic value used in comparison, consider replacing `X` with a constant variable. I don't rate this
+    "PLR2004", # Magic value used in comparison, consider replacing `X` with a constant variable. I don't rate this
+    "PLW0108", # Lambda may be unnecessary; consider inlining inner function
 ]
 
 [tool.ruff.format]
@@ -127,7 +131,7 @@ hail = ["hail", "hailtop"]
 "test/test_cpg_flow_utils.py" = ['ANN202', 'ARG001']
 
 [tool.bumpversion]
-current_version = "11.0.3"
+current_version = "11.1.0"
 parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
 serialize = ["{major}.{minor}.{patch}"]
 commit = true
diff --git a/src/talos/annotation_scripts/annotated_vcf_into_matrixtable.py b/src/talos/annotation_scripts/annotated_vcf_into_matrixtable.py
index 037a9ef5..fca46bfa 100755
--- a/src/talos/annotation_scripts/annotated_vcf_into_matrixtable.py
+++ b/src/talos/annotation_scripts/annotated_vcf_into_matrixtable.py
@@ -191,7 +191,11 @@ def annotate_all_transcript_consequences(
                     mane[x.transcript]['mane_id'],
                     MISSING_STRING,
                 ),
-                gene_id=ensgs[mt.locus.contig].get(x.gene, x.gene),
+                gene_id=hl.if_else(
+                    ensgs.contains(mt.locus.contig),
+                    ensgs[mt.locus.contig].get(x.gene, x.gene),
+                    x.gene,
+                ),
             ),
             mt.transcript_consequences,
         ),
diff --git a/src/talos/annotation_scripts/create_roi_from_gff3.py b/src/talos/annotation_scripts/create_roi_from_gff3.py
index 69a6d6ea..84115786 100755
--- a/src/talos/annotation_scripts/create_roi_from_gff3.py
+++ b/src/talos/annotation_scripts/create_roi_from_gff3.py
@@ -9,6 +9,7 @@
 """
 
 import gzip
+import json
 import re
 from argparse import ArgumentParser
 
@@ -31,7 +32,13 @@
 CANONICAL_CONTIGS = [f'chr{x}' for x in list(range(1, 23))] + ['chrX', 'chrY', 'chrM']
 
 
-def main(gff3_file: str, unmerged_output: str, merged_output: str, flanking: int = 2000):
+def main(
+    gff3_file: str,
+    unmerged_output: str,
+    merged_output: str,
+    flanking: int = 2000,
+    json_output: str | None = None,
+):
     """
     Read the GFF3 file, and generate a BED file of gene regions, plus padding
     Args:
@@ -39,21 +46,29 @@ def main(gff3_file: str, unmerged_output: str, merged_output: str, flanking: int
         unmerged_output (str): path to the intended BED output file
         merged_output (str): path to generate a BED file with merged overlapping rows
         flanking (int): number of bases to add before and after each gene
+        json_output (str | None): optional, path to write a Symbol: ENSG dictionary
     """
 
-    unmerged_lines = generate_bed_lines(gff3_file, unmerged_output, flanking)
+    unmerged_lines, as_dict = generate_bed_lines(gff3_file, unmerged_output, flanking)
     merge_output(unmerged_lines, merged_output)
 
+    # if a json version was requested, write that out
+    if json_output is not None:
+        with open(json_output, 'w') as write_handle:
+            json.dump(as_dict, write_handle, indent=4)
+
 
 def generate_bed_lines(
     gff3_file: str,
     output: str,
     flanking: int = FLANKING_REGION,
-) -> list[tuple[str, int, int]]:
+) -> tuple[list[tuple[str, int, int]], dict[str, str]]:
     """
     Generate the new BED file, and return the lines as a list of lists for merging.
     """
     output_lines: list[tuple[str, int, int]] = []
+    output_as_dict: dict[str, str] = {}
+
     # open and iterate over the GFF3 file
     with gzip.open(gff3_file, 'rt') as handle, open(output, 'w') as write_handle:
         for line in handle:
@@ -85,6 +100,8 @@ def generate_bed_lines(
                 print(f'Failed to extract gene name from {line_as_list[DETAILS_INDEX]}')
                 continue
 
+            output_as_dict[gene_name] = gene_id
+
             # write the line to the output
             output_list = [
                 f'chr{line_as_list[CHROM_INDEX]}',
@@ -100,7 +117,7 @@ def generate_bed_lines(
                     int(line_as_list[END_INDEX]) + flanking,
                 ),
             )
-    return output_lines
+    return output_lines, output_as_dict
 
 
 def merge_output(
@@ -159,19 +176,25 @@ def cli_main():
     parser.add_argument(
         '--merged_output',
         help='Path to output file, regions merged',
-        required=False,
+        required=True,
     )
     parser.add_argument(
         '--flanking',
         help='Regions to add to each gene',
         default=FLANKING_REGION,
     )
+    parser.add_argument(
+        '--json_output',
+        help='Write a {Gene Symbol: ENSG, } dictionary in JSON format.',
+        required=False,
+    )
     args = parser.parse_args()
     main(
         gff3_file=args.gff3,
         unmerged_output=args.unmerged_output,
         merged_output=args.merged_output,
         flanking=args.flanking,
+        json_output=args.json_output,
     )
 
 
diff --git a/src/talos/cpg_internal_scripts/CPG_Dockerfile b/src/talos/cpg_internal_scripts/CPG_Dockerfile
index e01bd3ff..093a7e37 100644
--- a/src/talos/cpg_internal_scripts/CPG_Dockerfile
+++ b/src/talos/cpg_internal_scripts/CPG_Dockerfile
@@ -14,46 +14,32 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 
 FROM base AS bcftools_compiler
 
-# BCFtools 1.22+ doesn't do any inference on chr-prefixes during CSQ, so the mismatch between fasta/vcf, and the gff3 (no chr-prefix) needs to be handled explicitly
-# BCFtools >=1.22 is required internally as it's the first version with a built-in Mitochondrial lookup table.
-# AS OF 11.0.3, Talos is building BCFtools from a private fork. This fork contains a single change - csq applies annotations
-# to both coding and non-coding genes in the event of overlapping genes. By default BCFtools skips non-coding gene annotation
-# if a coding transcript consequence was detected, but in practice this is masking clinically relevant non-coding gene variation
-# in cases where the non-coding gene overlaps with a non-clinically relevant coding gene.
-# The change made is to always search for non-coding variation, but to mask non-coding consequences in otherwise coding transcripts
-# if a coding change was already detected.
-ARG BCFTOOLS_VERSION=1.23.1
+ARG BCFTOOLS_VERSION=1.24
 
 RUN apt-get update && apt-get install --no-install-recommends -y \
-    autoconf \
     gcc \
-    git \
     libbz2-dev \
     libcurl4-openssl-dev \
     liblzma-dev \
     libssl-dev \
     make \
     zlib1g-dev && \
-    wget https://github.com/samtools/htslib/releases/download/${BCFTOOLS_VERSION}/htslib-${BCFTOOLS_VERSION}.tar.bz2 && \
-    tar -xf htslib-${BCFTOOLS_VERSION}.tar.bz2 && \
-    cd htslib-${BCFTOOLS_VERSION} && \
-    ./configure --enable-libcurl && \
-    make && \
-    make DESTDIR=/bcftools_install install && \
-    cd .. && \
-    git clone https://github.com/populationgenomics/bcftools.git && \
-    cd bcftools && \
-    autoheader && \
-    autoconf && \
+    rm -rf /var/lib/apt/lists/* && \
+    wget https://github.com/samtools/bcftools/releases/download/${BCFTOOLS_VERSION}/bcftools-${BCFTOOLS_VERSION}.tar.bz2 && \
+    tar -xf bcftools-${BCFTOOLS_VERSION}.tar.bz2 && \
+    cd bcftools-${BCFTOOLS_VERSION} && \
     ./configure --enable-libcurl --enable-s3 --enable-gcs && \
     make && \
     strip bcftools plugins/*.so && \
+    make DESTDIR=/bcftools_install install && \
+    cd htslib-${BCFTOOLS_VERSION} && \
+    make && \
     make DESTDIR=/bcftools_install install
 
 FROM base AS talos
 
 ARG ECHTVAR_VERSION=v0.2.2
-ARG VERSION=11.0.3
+ENV VERSION=11.1.0
 
 COPY --from=bcftools_compiler /bcftools_install/usr/local/bin/* /usr/local/bin/
 COPY --from=bcftools_compiler /bcftools_install/usr/local/libexec/bcftools/* /usr/local/libexec/bcftools/
diff --git a/src/talos/cpg_internal_scripts/cpgflow_jobs/annotate_csq_with_bcftools.py b/src/talos/cpg_internal_scripts/cpgflow_jobs/annotate_csq_with_bcftools.py
index 9c518ebd..1c042b34 100644
--- a/src/talos/cpg_internal_scripts/cpgflow_jobs/annotate_csq_with_bcftools.py
+++ b/src/talos/cpg_internal_scripts/cpgflow_jobs/annotate_csq_with_bcftools.py
@@ -44,6 +44,7 @@ def make_bcftools_anno_jobs(
             f"""
             bcftools index -t {local_vcf}
             bcftools csq --force -f {fasta} \\
+                --greedy 1 \\
                 --local-csq \\
                 --threads 4 \\
                 -g {gff3_file} \\
diff --git a/src/talos/cpg_internal_scripts/extract_fragmented_vcf_from_mt.py b/src/talos/cpg_internal_scripts/extract_fragmented_vcf_from_mt.py
index b39a2a01..a72d34fd 100755
--- a/src/talos/cpg_internal_scripts/extract_fragmented_vcf_from_mt.py
+++ b/src/talos/cpg_internal_scripts/extract_fragmented_vcf_from_mt.py
@@ -54,6 +54,8 @@
     },
 }
 
+CANONICAL_CONTIGS = {f'chr{x}' for x in list(range(1, 23))} | {'chrX', 'chrY'}
+
 
 def filter_mt_to_sgids(
     mt: hl.MatrixTable,
@@ -84,6 +86,12 @@ def filter_mt_to_sgids(
     return mt.filter_rows(hl.agg.any(mt.GT.is_non_ref()))
 
 
+def filter_mt_to_contigs(mt: hl.MatrixTable) -> hl.MatrixTable:
+    """Strict filter to just the non-Mito contigs."""
+    contig_literal = hl.literal(CANONICAL_CONTIGS)
+    return mt.filter_rows(contig_literal.contains(mt.locus.contig))
+
+
 def main(
     mt_path: str,
     sg_id_file: str,
@@ -109,6 +117,9 @@ def main(
     # filter the MT to a specific set of SG IDs
     mt = filter_mt_to_sgids(mt, sg_id_file)
 
+    # filter out chrM in this context
+    mt = filter_mt_to_contigs(mt)
+
     # replace the existing INFO block to just have AC/AN/AF - no other carry-over. Allow for this to be missing.
     if 'AF' not in mt.info:
         mt = hl.variant_qc(mt)
diff --git a/src/talos/cpg_internal_scripts/talos_stages.py b/src/talos/cpg_internal_scripts/talos_stages.py
index 544aeb7b..60b7cfab 100644
--- a/src/talos/cpg_internal_scripts/talos_stages.py
+++ b/src/talos/cpg_internal_scripts/talos_stages.py
@@ -628,6 +628,26 @@ def queue_jobs(self, cohort: targets.Cohort, inputs: stage.StageInput) -> stage.
             )['vcf.bgz']
             mito_vcf_arg = f'--labelled_mito {mito_vcf} '
 
+        # is this run going to include STR data?
+        str_vcf_arg = ''
+        # screen out cohorts/datasets which we don't want to run STRs for
+        str_cohort = cohort.dataset.name in config.config_retrieve(['workflow', 'str_cohorts'], [])
+        if str_cohort and (
+            str_vcf := query_for_latest_analysis(
+                dataset=cohort.dataset.name,
+                analysis_type='vcf',
+                long_read=config.config_retrieve(['workflow', 'long_read'], False),
+                stage_name='MakeStripyJointCall',
+            )
+        ):
+            stripy_vcf = hail_batch.get_batch().read_input_group(
+                **{
+                    'vcf.bgz': str_vcf,
+                    'vcf.bgz.tbi': f'{str_vcf}.tbi',
+                },
+            )['vcf.bgz']
+            str_vcf_arg = f'--str {stripy_vcf} '
+
         labelled_vcf = hail_batch.get_batch().read_input_group(
             **{
                 'vcf.bgz': hail_inputs,
@@ -657,7 +677,7 @@ def queue_jobs(self, cohort: targets.Cohort, inputs: stage.StageInput) -> stage.
                 --labelled_vcf {labelled_vcf} \\
                 --output {job.output} \\
                 --panelapp {panelapp_data} \\
-                --pedigree {pedigree} {sv_vcf_arg} {history_string} {mito_vcf_arg}
+                --pedigree {pedigree} {sv_vcf_arg} {history_string} {mito_vcf_arg} {str_vcf_arg}
             """,
         )
         expected_out = self.expected_outputs(cohort)
diff --git a/src/talos/create_talos_html.py b/src/talos/create_talos_html.py
index 51eb4cad..4a630bcc 100644
--- a/src/talos/create_talos_html.py
+++ b/src/talos/create_talos_html.py
@@ -23,7 +23,16 @@
 from loguru import logger
 
 from talos.config import config_retrieve
-from talos.models import PanelApp, PanelDetail, PanelShort, ReportVariant, ResultData, SmallVariant, StructuralVariant
+from talos.models import (
+    PanelApp,
+    PanelDetail,
+    PanelShort,
+    ReportVariant,
+    ResultData,
+    ShortTandemRepeat,
+    SmallVariant,
+    StructuralVariant,
+)
 from talos.utils import read_json_from_path
 
 JINJA_TEMPLATE_DIR = Path(__file__).absolute().parent / 'templates'
@@ -157,7 +166,7 @@ def variant_in_forbidden_gene(variant_obj: ReportVariant, forbidden_genes):
         if gene_id in forbidden_genes:
             return True
 
-    if isinstance(variant_obj.var_data, StructuralVariant):
+    if isinstance(variant_obj.var_data, (ShortTandemRepeat, StructuralVariant)):
         return False
 
     # Allow for exclusion by Symbol too
@@ -568,6 +577,11 @@ def get_var_change(self) -> str:
         if isinstance(self.var_data, StructuralVariant):
             return f'{self.var_data.info["svtype"]} {self.var_data.info["svlen"]}bp'
 
+        if isinstance(self.var_data, ShortTandemRepeat):
+            locus = self.var_data.locus or self.var_data.info.get('locus')
+            repeats = ', '.join(map(str, self.var_data.info['sample_repeats']))
+            return f'STR {locus}, Repeats: {repeats}'
+
         raise ValueError(f'Unknown variant type: {self.var_data.__class__.__name__}')
 
     def __init__(self, report_variant: ReportVariant, sample: Sample, ext_labels: list, html_builder: HTMLBuilder):  # noqa: PLR0915
diff --git a/src/talos/download_panelapp.py b/src/talos/download_panelapp.py
index 939936ae..f356cbca 100644
--- a/src/talos/download_panelapp.py
+++ b/src/talos/download_panelapp.py
@@ -25,6 +25,8 @@
 - attempt to find alternative gene symbols for the ENSG ID
 - attempt to find alternative Ensembl IDs for the gene symbol
 - record all variations
+
+STR-specific feature - parses the PanelApp Repeat Disorders panel
 """
 
 import asyncio
@@ -66,6 +68,7 @@
 # if this is a massive result, it returns over a number of pages
 PANEL_TEMPLATE_URL = f'{PANELS_ENDPOINT}/{{id}}'
 ACTIVITY_TEMPLATE = f'{PANELS_ENDPOINT}/{{id}}/activities'
+STR_TEMPLATE = f'{PANELS_ENDPOINT}/3597/strs?confidence_level=3'
 MITO_BAD = 'MT'
 MITO_GOOD = 'M'
 
@@ -311,6 +314,24 @@ def reorganise_mane_data(mane_path: str) -> tuple[dict[str, str], dict[str, str]
     return ensg_as_primary, symbol_as_primary
 
 
+def parse_repeat_disorders() -> tuple[set[str], set[str]]:
+    """Parse panel 3597 - find all genes with a green association to a STR disorder."""
+    str_genes: set[str] = set()
+    str_symbols: set[str] = set()
+    for each_result in get_json_response(STR_TEMPLATE)['results']:
+        str_association = each_result['gene_data']
+        str_symbols.add(str_association['gene_symbol'])
+
+        for build, content in str_association['ensembl_genes'].items():
+            if build.lower() == 'grch38':
+                # the ensembl version may alter over time, but will be singular
+                ensembl_data = content[next(iter(content.keys()))]
+                ensg = ensembl_data['ensembl_id']
+                str_genes.add(ensg)
+
+    return str_genes, str_symbols
+
+
 def cli_main():
     logger.info('Starting PanelApp parsing')
     parser = ArgumentParser()
@@ -408,6 +429,13 @@ async def _fetch_all() -> tuple[dict, dict]:
         logger.info(f'Removing panel {panel_id} from hpo matching - no green genes')
         del collected_panel_data.hpos[panel_id]
 
+    # query panelapp for the repeat disorders panel
+    str_genes, str_symbols = parse_repeat_disorders()
+
+    # populate the panelapp object
+    collected_panel_data.str_genes = str_genes
+    collected_panel_data.str_symbols = str_symbols
+
     with open(output, 'w') as output_file:
         output_file.write(collected_panel_data.model_dump_json(indent=4))
 
diff --git a/src/talos/example_config.toml b/src/talos/example_config.toml
index 38f21fcf..679683dd 100644
--- a/src/talos/example_config.toml
+++ b/src/talos/example_config.toml
@@ -112,6 +112,9 @@ support_categories = ['alphamissense', 'clinvar0star']
 #super_logging = true
 #super_logging_path = "exclusions.jsonl.gz"
 
+# any STR loci with unacceptably low signal-to-noise ratios - this is the 'locus' name from STRipy, not the gene ID
+noisy_strs = ['ARX_1', 'ARX_2', 'HOXA13_1', 'HOXA13_2',  'HOXA13_3', 'TCF4', 'RUNX2']
+
 [RunHailFiltering]
 # variables affecting how the VCF variants are parsed, and AnalysisVariant objects are populated
 csq_string = [ "consequence", "gene_id", "gene", "transcript", "mane_id", "mane", "biotype", "dna_change", "amino_acid_change", "codon", "ensp", "am_class", "am_pathogenicity",]
diff --git a/src/talos/liftover/lift_1_1_0_to_1_2_0.py b/src/talos/liftover/lift_1_1_0_to_1_2_0.py
index 51e44e62..e37da122 100644
--- a/src/talos/liftover/lift_1_1_0_to_1_2_0.py
+++ b/src/talos/liftover/lift_1_1_0_to_1_2_0.py
@@ -2,8 +2,6 @@
 code for lifting over models from 1.1.0 to 1.2.0
 """
 
-from talos.config import config_retrieve
-
 
 def resultdata(data_dict: dict) -> dict:
     """
@@ -22,9 +20,9 @@ def resultdata(data_dict: dict) -> dict:
                 _ = variant['var_data'].pop('sample_support')
 
             # the list of categories which are being treated as support for this run
-            variant['var_data']['support_categories'] = config_retrieve(['ValidateMOI', 'support_categories'], [])
+            variant['var_data']['support_categories'] = []
             # the list of categories being ignored for this run
-            variant['var_data']['ignored_categories'] = config_retrieve(['ValidateMOI', 'ignore_categories'], [])
+            variant['var_data']['ignored_categories'] = []
 
     data_dict['version'] = '1.2.0'
     return data_dict
diff --git a/src/talos/liftover/lift_2_2_0_to_2_3_0.py b/src/talos/liftover/lift_2_2_0_to_2_3_0.py
index a2362a0e..3bfd602c 100644
--- a/src/talos/liftover/lift_2_2_0_to_2_3_0.py
+++ b/src/talos/liftover/lift_2_2_0_to_2_3_0.py
@@ -3,7 +3,14 @@
 """
 
 
+def panelapp(data_dict: dict) -> dict:
+    data_dict |= {'str_genes': set(), 'str_symbols': set()}
+    data_dict['version'] = '2.3.0'
+    return data_dict
+
+
 def dl_panelapp(data_dict: dict) -> dict:
+    data_dict |= {'str_genes': set(), 'str_symbols': set()}
     for _, gene_data in data_dict['genes'].items():
         gene_data['panel_confidences'] = {}
         for _, panel_data in gene_data['panels'].items():
diff --git a/src/talos/models.py b/src/talos/models.py
index bfdca7e5..24062209 100644
--- a/src/talos/models.py
+++ b/src/talos/models.py
@@ -21,6 +21,7 @@
 from talos.liftover.lift_2_1_0_to_2_2_0 import dl_panelapp as dl_pa_210_to_220
 from talos.liftover.lift_2_1_0_to_2_2_0 import resultdata as rd_210_to_220
 from talos.liftover.lift_2_2_0_to_2_3_0 import dl_panelapp as dl_pa_220_to_230
+from talos.liftover.lift_2_2_0_to_2_3_0 import panelapp as pa_220_to_230
 from talos.liftover.lift_none_to_1_0_0 import resultdata as rd_none_to_1_0_0
 from talos.static_values import get_granular_date
 
@@ -330,13 +331,20 @@ def min_alt_ratio(self, sample: str, threshold: float = 0.2) -> bool:
 
 
 class StructuralVariant(VariantCommon):
-    """
-    placeholder for any methods/data specific to Structural Variants
-    """
+    """Placeholder for any methods/data specific to Structural Variants."""
+
+
+class ShortTandemRepeat(VariantCommon):
+    """Sub-class designed to parse STR VCFs into a suitable common format."""
+
+    locus: str
+    sample_filter: dict[str, str] = Field(default_factory=dict, exclude=True)
+    sample_repeats: dict[str, tuple[int, int] | tuple[int]] = Field(default_factory=dict, exclude=True)
+    sample_repeat_details: dict[str, dict] = Field(default_factory=dict, exclude=True)
 
 
 # register all interchangeable models here
-VARIANT_MODELS = SmallVariant | StructuralVariant
+VARIANT_MODELS = SmallVariant | StructuralVariant | ShortTandemRepeat
 
 
 class ReportPanel(BaseModel):
@@ -438,6 +446,8 @@ class PanelApp(BaseModel):
     participants: dict[str, ParticipantHPOPanels] = Field(default_factory=dict)
     version: str = CURRENT_VERSION
     creation_date: str = Field(default=get_granular_date())
+    str_genes: set[str] = Field(default_factory=set)
+    str_symbols: set[str] = Field(default_factory=set)
 
 
 class DownloadedPanelAppGenePanelDetail(BaseModel):
@@ -468,6 +478,8 @@ class DownloadedPanelApp(BaseModel):
     hpos: dict[int, list[HpoTerm]] = Field(default_factory=dict)
     version: str = CURRENT_VERSION
     date: str = Field(default=get_granular_date())
+    str_genes: set[str] = Field(default_factory=set)
+    str_symbols: set[str] = Field(default_factory=set)
 
 
 class ResultMeta(BaseModel):
@@ -567,6 +579,7 @@ def __str__(self):
     PanelApp: {
         '1.2.0_2.0.0': pa_120_to_200,
         '2.0.0_2.1.0': pa_200_to_210,
+        '2.2.0_2.3.0': pa_220_to_230,
     },
     ResultData: {
         'None_1.0.0': rd_none_to_1_0_0,
diff --git a/src/talos/moi_tests.py b/src/talos/moi_tests.py
index e488a3e7..78acd4bf 100644
--- a/src/talos/moi_tests.py
+++ b/src/talos/moi_tests.py
@@ -14,7 +14,7 @@
 
 from talos.config import config_retrieve
 from talos.exclusion_log import get_exclusion_logger
-from talos.models import VARIANT_MODELS, ReportVariant, SmallVariant, StructuralVariant
+from talos.models import VARIANT_MODELS, ReportVariant, ShortTandemRepeat, SmallVariant, StructuralVariant
 from talos.static_values import get_granular_date
 from talos.utils import X_CHROMOSOME, CompHetDict
 
@@ -23,6 +23,19 @@
 SV_HOMS = {'male_n_homalt', 'female_n_homalt'}
 
 
+def get_str_var_data(var: VARIANT_MODELS, sample_id: str) -> VARIANT_MODELS:
+    """For STRs, return a shallow copy with a sample-scoped info dict. Other variant types pass through unchanged."""
+    if isinstance(var, ShortTandemRepeat):
+        copy = var.model_copy(deep=False)
+        copy.info = {
+            **var.info,
+            'sample_repeats': var.sample_repeats[sample_id],
+            'sample_repeat_details': var.sample_repeat_details[sample_id],
+        }
+        return copy
+    return var
+
+
 @dataclass
 class GlobalFilter:
     """
@@ -46,16 +59,19 @@ class GlobalFilter:
     small_gnomad_hemi: ClassVar[int] = config_retrieve(['ValidateMOI', 'gnomad_max_hemizygotes'])
 
     # filters specific to SVs
+    # todo the field could have a different name
     sv_dict: ClassVar[dict[str, float]] = {
         'gnomad_v2.1_sv_AF': config_retrieve(['ValidateMOI', 'gnomad_sv_max_af']),
     }
 
-    def too_common(self, variant: SmallVariant | StructuralVariant, applied_moi: str | None = None) -> bool:  # noqa: PLR0911
+    def too_common(  # noqa: PLR0911
+        self, variant: SmallVariant | ShortTandemRepeat | StructuralVariant, applied_moi: str | None = None
+    ) -> bool:
         """
         Check if a variant is too common in the population
 
         Args:
-            variant (SmallVariant | StructuralVariant): the variant to check
+            variant (SmallVariant | ShortTandemRepeat | StructuralVariant): the variant to check
             applied_moi (str | None): MOI under which this filter is being applied (for exclusion logging only)
 
         Returns:
@@ -149,6 +165,9 @@ def too_common(self, variant: SmallVariant | StructuralVariant, applied_moi: str
                 )
                 return True
 
+        elif isinstance(variant, ShortTandemRepeat):
+            return False
+
         else:
             raise ValueError('Variant type not recognised')
 
@@ -180,12 +199,14 @@ class DominantFilter:
         'gnomad_v2.1_sv_AF': config_retrieve(['ValidateMOI', 'dominant_gnomad_sv_max_af']),
     }
 
-    def too_common(self, variant: SmallVariant | StructuralVariant, applied_moi: str | None = None) -> bool:
+    def too_common(
+        self, variant: SmallVariant | ShortTandemRepeat | StructuralVariant, applied_moi: str | None = None
+    ) -> bool:
         """
         Check if a variant is too common in the population
 
         Args:
-            variant (SmallVariant | StructuralVariant): the variant to check
+            variant (SmallVariant | ShortTandemRepeat | StructuralVariant): the variant to check
             applied_moi (str | None): MOI under which this filter is being applied (for exclusion logging only)
 
         Returns:
@@ -261,6 +282,9 @@ def too_common(self, variant: SmallVariant | StructuralVariant, applied_moi: str
                 )
                 return True
 
+        elif isinstance(variant, ShortTandemRepeat):
+            return False
+
         else:
             raise ValueError('Variant type not recognised')
 
@@ -559,6 +583,15 @@ def sample_passes_basic_checks(
         Returns True if the sample passes; otherwise logs the first failing reason and returns False.
         Caller is responsible for any additional MOI-specific gates (sex, zygosity).
         """
+        if sample_id not in self.pedigree.participants:
+            self._log_sample_exclusion(
+                principal,
+                sample_id,
+                'participant_not_in_pedigree',
+                details={'allow_support': allow_support},
+            )
+            return False
+
         if self.pedigree.participants[sample_id].is_not_affected:
             # not logging, self-evident
             # self._log_sample_exclusion(principal, sample_id, 'sample_not_affected')
@@ -802,7 +835,7 @@ def run(
                     sample=sample_id,
                     family=self.pedigree.participants[sample_id].family_id,
                     gene=principal.info.get('gene_id'),
-                    var_data=principal,
+                    var_data=get_str_var_data(principal, sample_id),
                     categories={key: get_granular_date() for key in principal.category_values(sample_id)},
                     reasons=self.applied_moi,
                     genotypes=self.get_family_genotypes(variant=principal, sample_id=sample_id),
@@ -877,7 +910,7 @@ def run(
                         sample=sample_id,
                         family=self.pedigree.participants[sample_id].family_id,
                         gene=principal.info.get('gene_id'),
-                        var_data=principal,
+                        var_data=get_str_var_data(principal, sample_id),
                         categories={key: get_granular_date() for key in principal.category_values(sample_id)},
                         reasons=self.applied_moi,
                         genotypes=self.get_family_genotypes(variant=principal, sample_id=sample_id),
@@ -952,7 +985,7 @@ def run(
                     sample=sample_id,
                     family=self.pedigree.participants[sample_id].family_id,
                     gene=principal.info.get('gene_id'),
-                    var_data=principal,
+                    var_data=get_str_var_data(principal, sample_id),
                     categories={key: get_granular_date() for key in principal.category_values(sample_id)},
                     genotypes=self.get_family_genotypes(variant=principal, sample_id=sample_id),
                     reasons=self.applied_moi,
@@ -1017,7 +1050,7 @@ def run(
                     sample=sample_id,
                     family=self.pedigree.participants[sample_id].family_id,
                     gene=principal.info.get('gene_id'),
-                    var_data=principal,
+                    var_data=get_str_var_data(principal, sample_id),
                     categories={key: get_granular_date() for key in principal.category_values(sample_id)},
                     reasons=self.applied_moi,
                     genotypes=self.get_family_genotypes(variant=principal, sample_id=sample_id),
@@ -1105,7 +1138,7 @@ def run(
                     sample=sample_id,
                     family=self.pedigree.participants[sample_id].family_id,
                     gene=principal.info.get('gene_id'),
-                    var_data=principal,
+                    var_data=get_str_var_data(principal, sample_id),
                     categories={key: get_granular_date() for key in principal.category_values(sample_id)},
                     reasons=self.applied_moi,
                     genotypes=self.get_family_genotypes(variant=principal, sample_id=sample_id),
@@ -1172,7 +1205,7 @@ def run(
                     sample=sample_id,
                     family=self.pedigree.participants[sample_id].family_id,
                     gene=principal.info.get('gene_id'),
-                    var_data=principal,
+                    var_data=get_str_var_data(principal, sample_id),
                     categories={key: get_granular_date() for key in principal.category_values(sample_id)},
                     genotypes=self.get_family_genotypes(variant=principal, sample_id=sample_id),
                     reasons=self.applied_moi,
@@ -1238,7 +1271,7 @@ def run(
                     sample=sample_id,
                     family=self.pedigree.participants[sample_id].family_id,
                     gene=principal.info.get('gene_id'),
-                    var_data=principal,
+                    var_data=get_str_var_data(principal, sample_id),
                     categories={key: get_granular_date() for key in principal.category_values(sample_id)},
                     genotypes=self.get_family_genotypes(variant=principal, sample_id=sample_id),
                     reasons=self.applied_moi,
@@ -1315,7 +1348,7 @@ def run(
                         sample=sample_id,
                         family=self.pedigree.participants[sample_id].family_id,
                         gene=principal.info.get('gene_id'),
-                        var_data=principal,
+                        var_data=get_str_var_data(principal, sample_id),
                         categories={key: get_granular_date() for key in principal.category_values(sample_id)},
                         reasons=self.applied_moi,
                         genotypes=self.get_family_genotypes(variant=principal, sample_id=sample_id),
@@ -1387,7 +1420,7 @@ def run(
                     sample=sample_id,
                     family=self.pedigree.participants[sample_id].family_id,
                     gene=principal.info.get('gene_id'),
-                    var_data=principal,
+                    var_data=get_str_var_data(principal, sample_id),
                     categories={key: get_granular_date() for key in principal.category_values(sample_id)},
                     reasons=self.applied_moi,
                     genotypes=self.get_family_genotypes(variant=principal, sample_id=sample_id),
diff --git a/src/talos/run_hail_filtering.py b/src/talos/run_hail_filtering.py
index 04d731cb..3018d94a 100644
--- a/src/talos/run_hail_filtering.py
+++ b/src/talos/run_hail_filtering.py
@@ -778,7 +778,7 @@ def get_csq_from_struct(element: hl.expr.StructExpression) -> hl.expr.StringExpr
         return hl.delimit([hl.or_else(hl.str(fields.get(f, '')), '') for f in csq_fields], '|')
 
     csq = hl.empty_array(hl.tstr)
-    csq = csq.extend(hl.or_else(tx_expr.map(lambda x: get_csq_from_struct(x)), hl.empty_array(hl.tstr)))  # noqa: PLW0108
+    csq = csq.extend(hl.or_else(tx_expr.map(lambda x: get_csq_from_struct(x)), hl.empty_array(hl.tstr)))
 
     # previous consequence filters may make this caution unnecessary
     return hl.or_missing(hl.len(csq) > 0, csq)
diff --git a/src/talos/scripts/stripy_json_to_vcf.py b/src/talos/scripts/stripy_json_to_vcf.py
new file mode 100755
index 00000000..c4ffecff
--- /dev/null
+++ b/src/talos/scripts/stripy_json_to_vcf.py
@@ -0,0 +1,386 @@
+#!/usr/bin/env python3
+
+"""
+Borrowing _heavily_ from https://github.com/broadinstitute/gatk-sv/blob/7eb2af1feea9f81a390caff211f6832c6e7f7ac5/src/stripy/stripy_to_vcf.py
+Has been extended to take multiple single-sample STRipy reports as input, and to characterise the GT field as:
+- '0/0' 2 called alleles, no pathogenic
+- '1/0' a1 pathogenic, a2 non-pathogenic
+- '1/.' a1 pathogenic, a2 missing
+
+Uses fixed contigs (GRCh38) for the header, instead of relying on a reference input
+
+This script creates VCFs in the format expected by Talos. Specific fields used:
+  - INFO/GENE              - used to detect the appropriate MOI for this STR
+  - INFO/SYMBOL            - used in variant display
+  - FORMAT/REPCN           - counts detected at each allele
+  - FORMAT/STR_FILTER      - rule out quality-failed STR calls
+  - FORMAT/DISEASE_DETAILS - dense formatted String, presenting each disease and corresponding thresholds
+
+This implementation of the STRipy VCF generator requires a symbol: ensg lookup dictionary
+This is now provided as an additional input from the prep script/module CreateRoiFromGff3
+"""
+
+import argparse
+import json
+import logging
+import re
+from pathlib import Path
+
+import pysam
+
+from cpg_utils import config, to_path
+
+CONTIG_ORDER = [f'chr{x}' for x in list(range(1, 23))] + ['chrX', 'chrY', 'chrM']
+HEADER_LINES = [
+    ('chr1', 248956422),
+    ('chr2', 242193529),
+    ('chr3', 198295559),
+    ('chr4', 190214555),
+    ('chr5', 181538259),
+    ('chr6', 170805979),
+    ('chr7', 159345973),
+    ('chr8', 145138636),
+    ('chr9', 138394717),
+    ('chr10', 133797422),
+    ('chr11', 135086622),
+    ('chr12', 133275309),
+    ('chr13', 114364328),
+    ('chr14', 107043718),
+    ('chr15', 101991189),
+    ('chr16', 90338345),
+    ('chr17', 83257441),
+    ('chr18', 80373285),
+    ('chr19', 58617616),
+    ('chr20', 64444167),
+    ('chr21', 46709983),
+    ('chr22', 50818468),
+    ('chrX', 156040895),
+    ('chrY', 57227415),
+    ('chrM', 16569),
+]
+
+VCF_HEADER = {
+    'INFO': [
+        {'ID': 'END', 'Number': '1', 'Type': 'Integer', 'Description': 'Stop position of the interval'},
+        {'ID': 'SVTYPE', 'Number': '1', 'Type': 'String', 'Description': 'Type of structural variant'},
+        {'ID': 'RU', 'Number': '1', 'Type': 'String', 'Description': 'Repeat unit in the reference orientation'},
+        {'ID': 'PERIOD', 'Number': '1', 'Type': 'Integer', 'Description': 'Length of the repeat unit'},
+        {
+            'ID': 'DISEASES',
+            'Number': '.',
+            'Type': 'String',
+            'Description': 'Associated disease symbols for this locus (| separated)',
+        },
+        {'ID': 'LOCUS', 'Number': '1', 'Type': 'String', 'Description': 'Gene/locus identifier from STRipy'},
+        {'ID': 'SYMBOL', 'Number': '1', 'Type': 'String', 'Description': 'Gene inferred from STRipy locus identifier'},
+        {'ID': 'GENE', 'Number': '1', 'Type': 'String', 'Description': 'ENSG ID inferred from Gene Symbol'},
+    ],
+    'FORMAT': [
+        {'ID': 'GT', 'Number': '1', 'Type': 'String', 'Description': 'Unphased genotype'},
+        {'ID': 'REPCN', 'Number': '2', 'Type': 'Float', 'Description': 'Number of repeat units spanned by each allele'},
+        {
+            'ID': 'DISEASE_DETAILS',
+            'Number': '1',
+            'Type': 'String',
+            'Description': '|-delimited details for each disease, in the form diseaseSymbol__normal__intermediate__pathogenic',  # noqa: E501
+        },
+        {
+            'ID': 'REPCI1',
+            'Number': '2',
+            'Type': 'Integer',
+            'Description': '95% CI min,max on repeat counts of first allele',
+        },
+        {
+            'ID': 'REPCI2',
+            'Number': '2',
+            'Type': 'Integer',
+            'Description': '95% CI min,max on repeat counts of second allele',
+        },
+        {
+            'ID': 'OUTLIER',
+            'Number': '2',
+            'Type': 'Integer',
+            'Description': 'Allelic population outlier flags (0/1) assigned by STRipy',
+        },
+        {
+            'ID': 'ZSCORE',
+            'Number': '2',
+            'Type': 'Float',
+            'Description': 'Allelic population Z-scores assigned by STRipy',
+        },
+        {'ID': 'DP', 'Number': '1', 'Type': 'Integer', 'Description': 'Total Depth'},
+        {'ID': 'STR_FILTER', 'Number': '.', 'Type': 'String', 'Description': 'Filter status assigned by STRipy'},
+    ],
+}
+
+
+def get_limited_locus_list() -> list[str] | None:
+    """Try to get the finite list of loci from config, if available."""
+    try:
+        return config.config_retrieve(['stripy', 'loci_lists', 'default_with_exclusions'])
+    except config.ConfigError:
+        print('No CPG config detected, using all loci')
+        return None
+
+
+def parse_coords(coord: str) -> tuple[str, int, int] | None:
+    m = re.match(r'(chr)?(?P<chrom>[^:]+):(?P<start>\d+)-(?P<end>\d+)', str(coord))
+    if not m:
+        return None
+    chrom = m.group('chrom')
+    if not chrom.startswith('chr'):
+        chrom = 'chr' + chrom
+    return chrom, int(m.group('start')), int(m.group('end'))
+
+
+def _to_float(x: str | float) -> float:
+    try:
+        return float(x)
+    except (TypeError, ValueError):
+        return float('nan')
+
+
+def _to_outlier(x: str | int) -> int | None:
+    try:
+        return int(x)
+    except (TypeError, ValueError):
+        return None
+
+
+def _ci_tuple(allele: dict[str, dict[str, int]]) -> tuple[int, int]:
+    return allele['CI'].get('Min', 0), allele['CI'].get('Max', 0)
+
+
+def parse_disease_ranges(content: dict[str, str | dict[str, int]]) -> str:
+    """
+    Read the CorrespondingDisease content of the locus' JSON dictionary.
+    This creates a hybrid String of Gene, MOI, normal and intermediate ranges, and pathogenic threshold.
+    These occur per-disease, and there can be multiple per allele.
+
+    example results: "HD__AD__min9max26__min27max35__36" or "DMD__XLR__min11max33__.__59"
+
+    Args:
+        content: the dictionary from STRipy's CorrespondingDisease data block
+
+    Returns:
+        A single aggregated String for all the details
+    """
+    # get the symbol for this disease
+    disease = content['DiseaseSymbol']
+
+    # the inheritance pattern
+    inheritance = content['Inheritance']
+
+    # parse the normal range, or '.'
+    normal_range: dict[str, int] | str = content['NormalRange']
+    normal = '.'
+    if isinstance(normal_range, dict):
+        normal = f'min{normal_range["Min"]}max{normal_range["Max"]}'
+
+    # parse the intermediate range, or '.'
+    inter_range: dict[str, int] | str = content['IntermediateRange']
+    intermediate = '.'
+    if isinstance(inter_range, dict):
+        intermediate = f'min{inter_range["Min"]}max{inter_range["Max"]}'
+
+    # add the pathogenic cutoff used for this disease, pull it all together
+    return f'{disease}__{inheritance}__{normal}__{intermediate}__{content["PathogenicCutoff"]}'
+
+
+def load_sample(json_path: str) -> tuple[str, dict]:
+    """Load a STRipy JSON and return (sample_name, loci_dict keyed by locus_id)."""
+    with to_path(json_path).open() as f:
+        data = json.load(f)
+
+    # hard-wired to pull the SampleID from the input filename - consider reverting
+    input_file = data.get('JobDetails', {}).get('InputFile', '')
+    if input_file:
+        stem = Path(input_file).stem
+        sample_name = stem.split('__')[0]
+    else:
+        sample_name = Path(json_path).stem.split('.')[0]
+
+    loci = {}
+    for entry in data.get('GenotypingResults', []):
+        for locus_name, locus in entry.items():
+            tl = locus['TargetedLocus']
+
+            # create a |-delimited string for all ranges for all relevant disease for this locus
+            disease_deets = '|'.join(parse_disease_ranges(corr) for corr in tl['CorrespondingDisease'].values())
+
+            # add flexibility if the Alleles aren't populated at all
+            if 'Alleles' not in locus:
+                explain_string = f'Parsing {sample_name}, locus {tl["LocusID"]}, no Alleles present - skipping. '
+                if 'Filter' in locus:
+                    explain_string += f'Filter: {locus["Filter"]} - '
+                logging.debug(explain_string)
+                continue
+
+            parsed = parse_coords(tl['Coordinates'])
+            if not parsed:
+                continue
+            chrom, start, end = parsed
+            alleles = locus['Alleles']
+            a1 = alleles[0]
+            a2 = alleles[1] if len(alleles) > 1 else None
+            motif = tl['Motif']
+            loci[locus_name] = {
+                'chrom': chrom,
+                'pos': start,
+                'end': end,
+                'id': locus_name,
+                'motif': motif,
+                'period': len(motif),
+                'a1_rep': _to_float(a1['Repeats']),
+                'a2_rep': _to_float(a2['Repeats']) if a2 is not None else None,
+                'a1_ci': _ci_tuple(a1),
+                'a2_ci': _ci_tuple(a2) if a2 is not None else (None, None),
+                'a1_range': a1['Range'],
+                'a2_range': a2['Range'] if a2 else None,
+                'a1_out': _to_outlier(a1['IsPopulationOutlier']),
+                'a2_out': _to_outlier(a2['IsPopulationOutlier']) if a2 is not None else None,
+                'a1_z': _to_float(a1['PopulationZscore']),
+                'a2_z': _to_float(a2['PopulationZscore']) if a2 is not None else None,
+                'coverage': locus['Metadata']['Coverage'],
+                'filter': locus['Filter'],
+                'diseases': '|'.join(sorted({meta['DiseaseSymbol'] for meta in tl['CorrespondingDisease'].values()})),
+                'disease_details': disease_deets,
+            }
+
+    return sample_name, loci
+
+
+def get_header(sample_names: list[str]) -> pysam.VariantHeader:
+    """Fill the header."""
+    header = pysam.VariantHeader()
+    header.add_meta('source', value='STRipy2VCF')
+    header.add_meta('ALT', items=[('ID', 'STR'), ('Description', 'Short tandem repeat')])
+    for info in VCF_HEADER['INFO']:
+        header.add_meta('INFO', items=list(info.items()))
+    for fmt in VCF_HEADER['FORMAT']:
+        header.add_meta('FORMAT', items=list(fmt.items()))
+
+    # generate the contig header lines
+    for contig, length in HEADER_LINES:
+        header.contigs.add(contig, length=length)
+
+    for sample_name in sample_names:
+        header.add_sample(sample_name)
+
+    return header
+
+
+def convert_range_to_gt(range_val: str | None) -> int | None:
+    """Converts the per-allele range to an integer, for embedding in GT."""
+    if range_val is None:
+        return None
+    if range_val.lower() == 'pathogenic':
+        return 1
+    return 0
+
+
+def get_gene_lookup(map_path: str | None = None) -> dict[str, str]:
+    """Pull out the dictionary reading logic."""
+    lookup: dict[str, str] = {}
+    if map_path is not None:
+        with to_path(map_path).open() as handle:
+            lookup = json.load(handle)
+    return lookup
+
+
+def write_multisample_vcf(  # noqa: PLR0915
+    samples: list[tuple[str, dict[str, dict]]],
+    out_path: str,
+    gene_map: str | None = None,
+) -> None:
+    """
+    Integrate the per-sample data into a union VCF
+
+    Args:
+        samples: list of (sample_name, loci_dict) tuples
+        out_path: vcf path to write output to. PySam will write compressed if ending is gz/bgz
+        gene_map: optional, file path to a JSON dict, mapping gene symbols to gene IDs
+    """
+
+    gene_lookup = get_gene_lookup(gene_map)
+
+    header = get_header(sample_names=[sam_bit[0] for sam_bit in samples])
+
+    only_loci = get_limited_locus_list()
+
+    canonical: dict[str, dict] = {}
+
+    for _, loci in samples:
+        for locus_id, loc in loci.items():
+            # if we got a list of specific loci, only use that finite list - ignore everything else
+            if (only_loci is not None) and (locus_id not in only_loci):
+                continue
+            canonical.setdefault(locus_id, loc)
+
+    # double sort contigs by both chrom and position
+    sorted_loci = sorted(canonical.values(), key=lambda x: (CONTIG_ORDER.index(x['chrom']), x['pos']))
+
+    with pysam.VariantFile(out_path, mode='w', header=header) as vf:
+        for loc in sorted_loci:
+            rec = header.new_record()
+            rec.contig = loc['chrom']
+            rec.start = loc['pos'] - 1
+            rec.stop = loc['end']
+            rec.id = str(loc['id'])
+            rec.ref = 'N'
+            rec.alts = ('<STR>',)
+            rec.filter.add('PASS')
+            rec.info['SVTYPE'] = 'STR'
+            if loc['motif']:
+                rec.info['RU'] = loc['motif']
+            if loc['period']:
+                rec.info['PERIOD'] = int(loc['period'])
+            rec.info['DISEASES'] = loc['diseases']
+            rec.info['LOCUS'] = loc['id']
+
+            # extract the gene symbol from STRipy annotations
+            symbol = loc['id'].split('_')[0]
+            rec.info['SYMBOL'] = symbol
+            rec.info['GENE'] = gene_lookup.get(symbol, symbol)
+
+            for sample_name, loci in samples:
+                s_loc = loci.get(loc['id'])
+                s = rec.samples[sample_name]
+                if s_loc is None:
+                    s['GT'] = (None, None)
+                    s['REPCN'] = (None, None)
+                    s['REPCI1'] = (None, None)
+                    s['REPCI2'] = (None, None)
+                    s['OUTLIER'] = (None, None)
+                    s['ZSCORE'] = (None, None)
+                    s['DP'] = None
+                    s['STR_FILTER'] = ['.']
+                    s['DISEASE_DETAILS'] = '.'
+                else:
+                    s['GT'] = (convert_range_to_gt(s_loc['a1_range']), convert_range_to_gt(s_loc['a2_range']))
+                    s['REPCN'] = (s_loc['a1_rep'], s_loc['a2_rep'])
+                    s['REPCI1'] = s_loc['a1_ci']
+                    s['REPCI2'] = s_loc['a2_ci']
+                    s['OUTLIER'] = (s_loc['a1_out'], s_loc['a2_out'])
+                    s['ZSCORE'] = (s_loc['a1_z'], s_loc['a2_z'])
+                    s['DP'] = int(s_loc['coverage'])
+                    s['STR_FILTER'] = [str(s_loc['filter'])] if s_loc['filter'] else ['PASS']
+                    s['DISEASE_DETAILS'] = s_loc['disease_details']
+
+            vf.write(rec)
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description='Convert STRipy JSON output(s) into a multi-sample VCF.')
+    ap.add_argument('--json', required=True, nargs='+', help='STRipy JSON report(s); one per sample')
+    ap.add_argument('--output', required=True, help='Output VCF')
+    ap.add_argument('--mapping', default=None, help='A JSON dict of Gene Symbol:Gene ID to update annotation')
+    args = ap.parse_args()
+
+    samples = [load_sample(p) for p in args.json]
+
+    write_multisample_vcf(samples=samples, out_path=args.output, gene_map=args.mapping)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/src/talos/templates/index.html.jinja b/src/talos/templates/index.html.jinja
index c6b7400d..62a4e5f1 100644
--- a/src/talos/templates/index.html.jinja
+++ b/src/talos/templates/index.html.jinja
@@ -453,7 +453,7 @@
 
         var html = '<div class="child-row-content"><div class="row">';
 
-        html += '<div class="col-md-6"><div class="card"><div class="card-body">';
+        html += '<div class="col-md-9"><div class="card"><div class="card-body">';
         html += '<h5 class="card-title">Variant Details</h5><dl class="row">';
 
         html += '<dt class="col-sm-4">Coordinates</dt><dd class="col-sm-8">';
@@ -608,6 +608,65 @@
                     }
                 }
                 html += '</dd>';
+            } else if (rowData.variantType === 'ShortTandemRepeat' && variantData.info) {
+                var strRepeats = variantData.info.sample_repeats;
+                var strDetails = variantData.info.sample_repeat_details;
+
+                if (strRepeats && strRepeats.length > 0) {
+                    html += '<dt class="col-sm-4">Repeat Counts</dt><dd class="col-sm-8">';
+                    strRepeats.forEach(function(count, i) {
+                        html += 'Allele ' + (i + 1) + ': ' + escapeHtml(String(count)) + '<br>';
+                    });
+                    html += '</dd>';
+                }
+
+                if (strDetails && Object.keys(strDetails).length > 0) {
+                    var maxRepeats = strRepeats && strRepeats.length > 0 ? Math.max.apply(null, strRepeats) : null;
+                    html += '<dt class="col-sm-4">Disease Classification</dt><dd class="col-sm-8">';
+                    html += '<table class="table table-sm table-bordered mb-0"><thead><tr>';
+                    html += '<th>Disease</th><th>MOI</th><th>Normal</th><th>Intermediate</th><th>Pathogenic</th><th>Classification</th>';
+                    html += '</tr></thead><tbody>';
+
+                    Object.keys(strDetails).forEach(function(disease) {
+                        var d = strDetails[disease];
+                        var norm = d.norm ? d.norm[0] + '–' + d.norm[1] : '—';
+                        var inter = d.inter ? d.inter[0] + '–' + d.inter[1] : '—';
+                        var pathThresh = d.pathogenic && d.pathogenic !== '.' ? '≥' + d.pathogenic : '—';
+
+                        var badge = '<span class="badge bg-secondary">UNCLASSIFIED</span>';
+                        if (maxRepeats !== null) {
+                            var pathNum = d.pathogenic && d.pathogenic !== '.' ? parseInt(d.pathogenic, 10) : null;
+                            if (pathNum !== null && maxRepeats >= pathNum) {
+                                badge = '<span class="badge bg-danger">PATHOGENIC</span>';
+                            } else if (d.inter && maxRepeats >= d.inter[0] && maxRepeats <= d.inter[1]) {
+                                badge = '<span class="badge bg-warning text-dark">INTERMEDIATE</span>';
+                            } else if (d.norm && maxRepeats >= d.norm[0] && maxRepeats <= d.norm[1]) {
+                                badge = '<span class="badge bg-success">NORMAL</span>';
+                            }
+                        }
+
+                        html += '<tr>';
+                        html += '<td>' + escapeHtml(disease) + '</td>';
+                        html += '<td>' + escapeHtml(d.moi || '—') + '</td>';
+                        html += '<td>' + escapeHtml(norm) + '</td>';
+                        html += '<td>' + escapeHtml(inter) + '</td>';
+                        html += '<td>' + escapeHtml(pathThresh) + '</td>';
+                        html += '<td>' + badge + '</td>';
+                        html += '</tr>';
+                    });
+
+                    html += '</tbody></table></dd>';
+                }
+
+                if (rowData.genes) {
+                    var strGenes = rowData.genes.split(',');
+                    var strFirst = strGenes[0] ? strGenes[0].split(':') : ['', ''];
+                    if (strFirst[1]) {
+                        html += '<dt class="col-sm-4">External Links</dt><dd class="col-sm-8">';
+                        html += '<a href="' + omimGeneUrl(strFirst[1]) + '" target="_blank">OMIM</a>';
+                        html += '</dd>';
+                    }
+                }
             }
 
             html += '</dl></div></div></div>';
diff --git a/src/talos/unified_panelapp_parser.py b/src/talos/unified_panelapp_parser.py
index cfbd8c87..cad57359 100644
--- a/src/talos/unified_panelapp_parser.py
+++ b/src/talos/unified_panelapp_parser.py
@@ -430,6 +430,10 @@ def main(panel_data: str, output_file: str, pedigree_path: str, hpo_file: str |
 
         panelapp_data.genes = {key: value for key, value in panelapp_data.genes.items() if key not in genes_to_remove}
 
+    # add in the STR content
+    panelapp_data.str_genes = cached_panelapp.str_genes
+    panelapp_data.str_symbols = cached_panelapp.str_symbols
+
     # validate and write using pydantic
     valid_cohort_details = PanelApp.model_validate(panelapp_data)
     with open(output_file, 'w', encoding='utf-8') as handle:
diff --git a/src/talos/utils.py b/src/talos/utils.py
index 99f12f85..f353946e 100644
--- a/src/talos/utils.py
+++ b/src/talos/utils.py
@@ -12,6 +12,7 @@
 import zoneinfo
 from collections import defaultdict
 from datetime import datetime
+from functools import cache
 from itertools import chain, combinations, combinations_with_replacement, islice
 from random import choices
 from typing import TYPE_CHECKING, Any
@@ -21,13 +22,16 @@
 from loguru import logger
 from mendelbrot.bcftools_interpreter import TYPES_RE, classify_change
 from mendelbrot.pedigree_parser import PedigreeParser
+from numpy import isnan
 from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential_jitter
 
 from talos.config import config_retrieve
 from talos.models import (
     VARIANT_MODELS,
     Coordinates,
+    PanelApp,
     ResultData,
+    ShortTandemRepeat,
     SmallVariant,
     StructuralVariant,
     lift_up_model_version,
@@ -75,6 +79,8 @@
 # not in use yet, if we want to reduce the number of TranscriptConsequence entries we store, this is a good start
 BORING_CONSEQUENCES = ['downstream_gene_variant', 'intron_variant', 'upstream_gene_variant']
 
+STR_RANGE = re.compile(r'min(?P<min>[0-9]+)max(?P<max>[0-9]+)$')
+
 
 def parse_mane_json_to_dict(mane_json: str) -> dict:
     """
@@ -134,6 +140,14 @@ def generator_chunks(generator, size):
         yield list(chain([first], islice(iterator, size - 1)))
 
 
+@cache
+def get_categories_translated(conf_string: str = 'support_categories') -> set[str]:
+    """One central method to parse categories from config."""
+    support_categories = {word.lower() for word in config_retrieve(['ValidateMOI', conf_string], [])}
+    support_categories.update({translate_category(x) for x in support_categories})
+    return support_categories
+
+
 @retry(
     stop=stop_after_attempt(3),
     wait=wait_exponential_jitter(initial=1, max=5, exp_base=2),
@@ -399,6 +413,51 @@ def organise_svdb_doi(info_dict: dict[str, Any]):
     info_dict['svdb_doi'] = doi_urls
 
 
+def parse_str_disease_details(detail_string: str) -> dict[str, dict[str, str | tuple[int, int] | None]]:
+    """
+    Parse the pipe-delimited disease details for this sample & locus.
+    Each block is in the format "Disease__MOI__NormalRange__IntermediateRange__PathogenicThreshold".
+    Ranges can be missing: "."
+    Other values can be missing: "NA"
+
+    Args:
+        detail_string: the single pipe-delimited String
+
+    Returns:
+        dict of each disease name and its related thresholds and ranges
+    """
+
+    results: dict[str, dict] = {}
+
+    # missing value = no details, return an empty dict
+    if detail_string == '.':
+        return results
+
+    # parse each disease's data separately
+    for disease_block in detail_string.split('|'):
+        # split up the formatted string
+        gene, moi, norm, inter, pathogenic = disease_block.split('__')
+
+        # try and detect the normal and intermediate ranges
+        normal_range: None | tuple[int, int] = None
+        if matchy := re.match(STR_RANGE, norm):
+            normal_range = (int(matchy.group('min')), int(matchy.group('max')))
+
+        inter_range: None | tuple[int, int] = None
+        if matchy := re.match(STR_RANGE, inter):
+            inter_range = (int(matchy.group('min')), int(matchy.group('max')))
+
+        # build the
+        results[gene] = {
+            'moi': moi,
+            'norm': normal_range,
+            'inter': inter_range,
+            'pathogenic': pathogenic,
+        }
+
+    return results
+
+
 def organise_de_novo(info_dict: dict[str, Any], alt_depths: dict[str, int], ab_ratios: dict[str, float]) -> None:
     """
     apply some late checking on de novo attributes
@@ -424,10 +483,65 @@ def organise_de_novo(info_dict: dict[str, Any], alt_depths: dict[str, int], ab_r
 
     # if we detected any failing samples against these rules, fish them out
     if to_pop:
-        logger.info(f'Removing de novo status for {len(to_pop)} samples: {", ".join(to_pop)}')
+        logger.debug(f'Removing de novo status for {len(to_pop)} samples: {", ".join(to_pop)}')
         info_dict['categorysampledenovo'] = [sam for sam in info_dict['categorysampledenovo'] if sam not in to_pop]
 
 
+def create_str_variant(
+    var: 'cyvcf2.Variant',
+    samples: list[str],
+) -> ShortTandemRepeat | None:
+    """Takes a variant row from an STR 'joint-call', and parses into a Variant object."""
+
+    ignored_categories = get_categories_translated('ignore_categories')
+    if 'categorybooleanstr' in ignored_categories:
+        return None
+
+    coordinates = Coordinates(chrom=var.CHROM.replace('chr', ''), pos=var.POS, ref='STR', alt=var.ID)
+
+    # parse the info dict, and shove in a recognisable category label
+    boolean_categories = ['categorybooleanstr']
+    info: dict[str, Any] = {x.lower(): y for x, y in var.INFO} | {'categorybooleanstr': 1}
+
+    if info.get('locus') in config_retrieve(['ValidateMOI', 'noisy_strs'], []):
+        return None
+
+    # shuffle the gene ID (ENSG) to match the location Hail classifiers put it
+    info['gene_id'] = info.pop('gene')
+
+    het_samples, hom_samples = get_non_ref_samples(variant=var, samples=samples)
+
+    # no samples with an STR call, reject the whole variant row
+    if not (het_samples or hom_samples):
+        return None
+
+    # get repeat counts for each sample with a variant call
+    vcf_repcn = dict(zip(samples, var.format('REPCN'), strict=True))
+
+    disease_details = dict(zip(samples, var.format('DISEASE_DETAILS'), strict=True))
+
+    # dictionary to record repeat counts for called variants - tuple of length 1 | 2
+    repeat_counts: dict[str, tuple[int, int] | tuple[int]] = {}
+    sample_disease_details: dict = {}
+    for sample_id in het_samples | hom_samples:
+        # strip out numpy.nan(float64) values, cast the real values as floats
+        repeat_counts[sample_id] = tuple(int(value) for value in vcf_repcn[sample_id] if ~isnan(value))  # type: ignore[assignment]
+        sample_disease_details[sample_id] = parse_str_disease_details(disease_details[sample_id])
+
+    return ShortTandemRepeat(
+        coordinates=coordinates,
+        locus=var.ID,
+        info=info,
+        het_samples=het_samples,
+        hom_samples=hom_samples,
+        boolean_categories=boolean_categories,
+        ignored_categories=ignored_categories,
+        support_categories=get_categories_translated(),
+        sample_repeats=repeat_counts,
+        sample_repeat_details=sample_disease_details,
+    )
+
+
 def create_small_variant(
     var: 'cyvcf2.Variant',
     samples: list[str],
@@ -454,10 +568,8 @@ def create_small_variant(
         ),
     )
 
-    # optionally - ignore some categories from this analysis
-    # keep it super flexible, lower case and a translated version
-    ignored_categories = {word.lower() for word in config_retrieve(['ValidateMOI', 'ignore_categories'], [])}
-    ignored_categories.update({translate_category(x) for x in ignored_categories})
+    # optionally - ignore some categories from this analysis. Super flexible, lower case and a translated version.
+    ignored_categories = get_categories_translated('ignore_categories')
 
     # set the class attributes - skipping over categories we've chosen to ignore
     boolean_categories = [
@@ -471,10 +583,6 @@ def create_small_variant(
         if key.startswith('categorysample') and key.replace('categorysample', '') not in ignored_categories
     ]
 
-    # the categories to be treated as support-only for this runtime - make it a set
-    support_categories = {word.lower() for word in config_retrieve(['ValidateMOI', 'support_categories'], [])}
-    support_categories.update({translate_category(x) for x in support_categories})
-
     # overwrite with true booleans
     for cat in boolean_categories:
         info[cat] = info.get(cat, 0) == 1
@@ -522,7 +630,7 @@ def create_small_variant(
         boolean_categories=boolean_categories,
         sample_categories=sample_categories,
         ignored_categories=ignored_categories,
-        support_categories=support_categories,
+        support_categories=get_categories_translated(),
         phased=phased,
         alt_depths=alt_depths,
         depths=depths,
@@ -595,8 +703,8 @@ def canonical_contigs_from_vcf(reader) -> set[str]:
 
 def gather_gene_dict_from_contig(
     contig: str,
-    variant_source: 'cyvcf2.VCFReader',
-    sv_source: 'cyvcf2.VCFReader | None' = None,
+    variant_sources: dict[str, 'cyvcf2.VCFReader'],
+    panelapp: PanelApp,
 ) -> GeneDict:
     """
     takes a cyvcf2.VCFReader instance, and a specified chromosome
@@ -606,8 +714,8 @@ def gather_gene_dict_from_contig(
 
     Args:
         contig (): contig name from VCF header
-        variant_source (): the VCF reader instance
-        sv_source (): an optional list of SV VCFs
+        variant_sources (): dict mapping each variant type to a VCF reader instance
+                            known types: small, sv, mito (chrM only), str
 
     Returns:
         A lookup in the form
@@ -631,36 +739,55 @@ def gather_gene_dict_from_contig(
     contig_dict = defaultdict(list)
 
     # iterate over all variants on this contig and store by unique key
-    # if contig has no variants, prints an error and returns []
-    for variant in variant_source(contig):
-        if (small_variant := create_small_variant(var=variant, samples=variant_source.samples)) is None:
+    small_samples = variant_sources['small'].samples
+    for variant in variant_sources['small'](contig):
+        if (small_variant := create_small_variant(var=variant, samples=small_samples)) is None:
             continue
 
         if small_variant.coordinates.string_format in blacklist:
             logger.info(f'Skipping blacklisted variant: {small_variant.coordinates.string_format}')
             continue
-
-        # update the variant count
         contig_variants += 1
-
-        # update the gene index dictionary
         contig_dict[small_variant.info['gene_id']].append(small_variant)
 
     # parse the SV VCF if provided, but not a necessary part of processing
-    if sv_source:
+    if variant_sources.get('sv'):
+        sv_samples = variant_sources['sv'].samples
         structural_variants = 0
-        for variant in sv_source(contig):
-            # create an abstract SV variant
-            structural_variant = create_structural_variant(var=variant, samples=sv_source.samples)
-
-            # update the variant count
+        for variant in variant_sources['sv'](contig):
+            structural_variant = create_structural_variant(var=variant, samples=sv_samples)
             structural_variants += 1
-
-            # update the gene index dictionary
             contig_dict[structural_variant.info['gene_id']].append(structural_variant)
-
         logger.info(f'Contig {contig} contained {structural_variants} SVs')
 
+    # parse STR VCF if provided
+    if variant_sources.get('str'):
+        # limit STRs to PanelApp green-evidence accepted repeat disorder genes
+        repeat_disorder_genes = panelapp.str_genes
+        str_samples = variant_sources['str'].samples
+        str_variants = 0
+        for variant in variant_sources['str'](contig):
+            if str_var := create_str_variant(var=variant, samples=str_samples):
+                # skip any which aren't accepted in PanelApp's repeat disorders panel
+                if str_var.info['gene_id'] not in repeat_disorder_genes:
+                    continue
+
+                str_variants += 1
+                contig_dict[str_var.info['gene_id']].append(str_var)
+
+        logger.info(f'Contig {contig} contained {str_variants} STRs')
+
+    # special segment for mito data - if Mito is provided and chrM is being searched, provide variants
+    if contig == 'chrM' and variant_sources.get('mito'):
+        mito_samples = variant_sources['mito'].samples
+        mito_variants = 0
+        for variant in variant_sources['mito'](contig):
+            if mito_var := create_small_variant(var=variant, samples=mito_samples):
+                mito_variants += 1
+                contig_dict[mito_var.info['gene_id']].append(mito_var)
+
+        logger.info(f'Mito VCF contained {mito_variants} variants')
+
     logger.info(f'Contig {contig} contained {contig_variants} variants, in {len(contig_dict)} genes')
 
     return contig_dict
diff --git a/src/talos/validate_moi.py b/src/talos/validate_moi.py
index 87a68792..c62a7ef0 100644
--- a/src/talos/validate_moi.py
+++ b/src/talos/validate_moi.py
@@ -279,8 +279,7 @@ def count_families(pedigree: PedigreeParser) -> dict:
 
 def prepare_results_shell(
     results_meta: ResultMeta,
-    small_samples: set[str],
-    sv_samples: set[str],
+    source_samples: dict[str, set[str]],
     pedigree: PedigreeParser,
     panelapp: PanelApp,
 ) -> ResultData:
@@ -289,8 +288,7 @@ def prepare_results_shell(
 
     Args:
         results_meta (): metadata for the results
-        small_samples (): samples in the Small VCF
-        sv_samples (): samples in the SV VCFs
+        source_samples (): samples indexed by variant type
         pedigree (): the Pedigree object, already reduced to samples in the callset
         panelapp (): dictionary of gene data
 
@@ -328,8 +326,8 @@ def prepare_results_shell(
                     if panel_id in panelapp.metadata
                 },
                 solved=bool(participant.sample_id in solved_cases or participant.family_id in solved_cases),
-                present_in_small=participant.sample_id in small_samples,
-                present_in_sv=participant.sample_id in sv_samples,
+                present_in_small=participant.sample_id in source_samples['small'],
+                present_in_sv=participant.sample_id in source_samples.get('sv', set()),
             ),
         )
 
@@ -339,8 +337,9 @@ def prepare_results_shell(
 def cli_main():
     parser = ArgumentParser(description='Startup commands for the MOI testing phase of Talos')
     parser.add_argument('--labelled_vcf', help='Category-labelled VCF')
-    parser.add_argument('--labelled_sv', help='Category-labelled SV VCF', default=None)
-    parser.add_argument('--labelled_mito', help='Category-labelled Mito VCF', default=None)
+    parser.add_argument('--labelled_sv', help='Optional, Category-labelled SV VCF', default=None)
+    parser.add_argument('--labelled_mito', help='Optional, Category-labelled Mito VCF', default=None)
+    parser.add_argument('--str', help='Optional, VCF containing STR data', default=None)
     parser.add_argument('--output', help='Prefix to write JSON results to', required=True)
     parser.add_argument('--panelapp', help='QueryPanelApp JSON', required=True)
     parser.add_argument('--pedigree', help='Path to PED file', required=True)
@@ -354,6 +353,7 @@ def cli_main():
         pedigree=args.pedigree,
         labelled_sv=args.labelled_sv,
         labelled_mito=args.labelled_mito,
+        str_vcf=args.str,
         previous=args.previous,
     )
 
@@ -365,6 +365,7 @@ def main(
     pedigree: str,
     labelled_sv: str | None = None,
     labelled_mito: str | None = None,
+    str_vcf: str | None = None,
     previous: str | None = None,
 ):
     """
@@ -378,6 +379,7 @@ def main(
         labelled_vcf (str): VCF output from Hail Labelling stage
         labelled_sv (str | None): optional second VCF (SV)
         labelled_mito (str | None): optional Mitochondrial VCF
+        str_vcf (str | None): optional STR 'joint-call' VCF
         output (str): location to write output file
         panelapp_path (str): location of PanelApp data JSON
         pedigree (str): location of PED file
@@ -398,6 +400,7 @@ def main(
     # initialise the optional MOI-stage exclusion logger; no-op when disabled in config
     exclusion_logger = get_exclusion_logger()
 
+    # shove everything in a try-except to make sure the global logger is closed down
     try:
         panelapp: PanelApp = read_json_from_path(
             panelapp_path,
@@ -413,26 +416,31 @@ def main(
 
         result_list: list[ReportVariant] = []
 
-        # collect all sample IDs from each VCF type
-        small_vcf_samples: set[str] = set()
-        sv_vcf_samples: set[str] = set()
-
-        # open the small variant VCF using a cyvcf2 reader
+        # open the small variant VCF using a cyvcf2 reader - mandatory data source
         vcf_opened = VCFReader(labelled_vcf)
-        small_vcf_samples.update(set(vcf_opened.samples))
+        source_samples: dict[str, set[str]] = {'small': set(vcf_opened.samples)}
+        source_vcfs: dict[str, VCFReader] = {'small': vcf_opened}
 
         # optional SV behaviour
-        sv_opened = None
         if labelled_sv:
             sv_opened = VCFReader(labelled_sv)
-            sv_vcf_samples = set(sv_opened.samples)
-
-        all_samples = small_vcf_samples.union(sv_vcf_samples)
+            source_samples['sv'] = set(sv_opened.samples)
+            source_vcfs['sv'] = sv_opened
 
-        mito_opened = None
+        # optional mito behaviour
         if labelled_mito:
             mito_opened = VCFReader(labelled_mito)
-            all_samples |= set(mito_opened.samples)
+            source_vcfs['mito'] = mito_opened
+            source_samples['mito'] = set(mito_opened.samples)
+
+        # optional str behaviour
+        if str_vcf:
+            str_opened = VCFReader(str_vcf)
+            source_vcfs['str'] = str_opened
+            source_samples['str'] = set(str_opened.samples)
+
+        # collect all unique samples across the various sources
+        all_samples = set.union(*source_samples.values())
 
         # parse the pedigree from the file
         ped = PedigreeParser(pedigree)
@@ -454,21 +462,10 @@ def main(
             # assemble {gene: [var1, var2, ..]}
             contig_dict = gather_gene_dict_from_contig(
                 contig=contig,
-                variant_source=vcf_opened,
-                sv_source=sv_opened,
-            )
-
-            result_list.extend(
-                apply_moi_to_variants(
-                    variant_dict=contig_dict,
-                    moi_lookup=moi_lookup,
-                    panelapp_data=panelapp.genes,
-                    pedigree=ped,
-                ),
+                variant_sources=source_vcfs,
+                panelapp=panelapp,
             )
 
-        if mito_opened:
-            contig_dict = gather_gene_dict_from_contig('chrM', variant_source=mito_opened)
             result_list.extend(
                 apply_moi_to_variants(
                     variant_dict=contig_dict,
@@ -488,8 +485,7 @@ def main(
         # create a shell to store results in, adds participant metadata
         results_model = prepare_results_shell(
             results_meta=results_meta,
-            small_samples=small_vcf_samples,
-            sv_samples=sv_vcf_samples,
+            source_samples=source_samples,
             pedigree=ped,
             panelapp=panelapp,
         )
diff --git a/src/talos/version.py b/src/talos/version.py
index d1bb7002..77f1fe77 100644
--- a/src/talos/version.py
+++ b/src/talos/version.py
@@ -1 +1 @@
-__version__ = '11.0.3'
+__version__ = '11.1.0'
diff --git a/test/test_unified_panelapp_parser.py b/test/test_unified_panelapp_parser.py
index a6c7cd61..d2a3e4de 100644
--- a/test/test_unified_panelapp_parser.py
+++ b/test/test_unified_panelapp_parser.py
@@ -101,7 +101,6 @@ def test_match_participants_to_panels():
         'genes': {},
         'participants': {
             'sam1': ParticipantHPOPanels(
-                external_id='',
                 family_id='',
                 hpo_terms=[HpoTerm(id='HP:1', label='1'), HpoTerm(id='HP:2', label='2')],
                 panels={99, 1, 3, 5},
@@ -109,7 +108,6 @@ def test_match_participants_to_panels():
                 matched_phenotypes=set(),
             ),
             'sam2': ParticipantHPOPanels(
-                external_id='',
                 family_id='',
                 hpo_terms=[HpoTerm(id='HP:1', label='1')],
                 panels={99, 1, 3},
@@ -118,6 +116,8 @@ def test_match_participants_to_panels():
             ),
         },
         'version': CURRENT_VERSION,
+        'str_genes': set(),
+        'str_symbols': set(),
     }
 
 
diff --git a/test/test_utils.py b/test/test_utils.py
index 74ab45a4..3caccb53 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -26,6 +26,11 @@
 FIVE_EXPECTED = 5
 
 
+class FakePanelApp:
+    def __init__(self):
+        self.str_genes: set[str] = set()
+
+
 def test_coord_sorting():
     """
     check that coord sorting methods work
@@ -132,8 +137,7 @@ def test_gene_dict(two_trio_variants_vcf):
     gene = ENSG00000075043
     """
     reader = VCFReader(two_trio_variants_vcf)
-    contig = 'chr20'
-    var_dict = gather_gene_dict_from_contig(contig=contig, variant_source=reader)
+    var_dict = gather_gene_dict_from_contig(contig='chr20', variant_sources={'small': reader}, panelapp=FakePanelApp())
     assert len(var_dict) == 1
     assert 'ENSG00000075043' in var_dict
     assert len(var_dict['ENSG00000075043']) == TWO_EXPECTED
@@ -163,7 +167,7 @@ def test_phased_dict(phased_vcf_path):
     gene = ENSG00000075043
     """
     reader = VCFReader(phased_vcf_path)
-    var_dict = gather_gene_dict_from_contig(contig='chr20', variant_source=reader)
+    var_dict = gather_gene_dict_from_contig(contig='chr20', variant_sources={'small': reader}, panelapp=FakePanelApp())
     assert len(var_dict) == ONE_EXPECTED
     assert 'ENSG00000075043' in var_dict
     assert len(var_dict['ENSG00000075043']) == TWO_EXPECTED
diff --git a/test/test_validate_methods.py b/test/test_validate_methods.py
index c13cb224..d4887ebc 100644
--- a/test/test_validate_methods.py
+++ b/test/test_validate_methods.py
@@ -49,10 +49,10 @@ def test_results_shell(pedigree_path: str):
             'mother_2': {'external_id': 'mother_2', 'hpo_terms': []},
         },
     )
+    source_samples = {'small': {'male'}, 'sv': {'female'}}
     shell = prepare_results_shell(
         results_meta=ResultMeta(),
-        small_samples={'male'},
-        sv_samples={'female'},
+        source_samples=source_samples,
         pedigree=PedigreeParser(pedigree_path=pedigree_path),
         panelapp=panelapp,
     )
diff --git a/uv.lock b/uv.lock
index 81e1331e..f335b60e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -591,7 +591,7 @@ wheels = [
 
 [[package]]
 name = "cpg-utils"
-version = "5.4.2"
+version = "5.7.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "boto3" },
@@ -600,13 +600,15 @@ dependencies = [
     { name = "deprecated" },
     { name = "frozendict" },
     { name = "google-auth" },
+    { name = "google-auth-oauthlib" },
     { name = "google-cloud-artifact-registry" },
     { name = "google-cloud-secret-manager" },
+    { name = "metamist" },
     { name = "requests" },
     { name = "tabulate" },
     { name = "toml" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f1/55/e069ef106f576e3e7916f4801d56dc2efd867387ff4fc0b6593815a54f47/cpg_utils-5.4.2.tar.gz", hash = "sha256:eb6f0808655e836171db279b44db3bff66b4e7a9b3a8629caf630483e7cc5bc6", size = 43646, upload-time = "2025-08-13T01:56:47.044Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/01/9e/145494610bd318645f834a0d8f7e7b0f819f77337b334254bb9ccfa89bd3/cpg_utils-5.7.0.tar.gz", hash = "sha256:aba177361a73003836de4a8c310dadccf6e7bff16760bf51bbb263a133d2d421", size = 51003, upload-time = "2026-06-30T21:32:18.12Z" }
 
 [[package]]
 name = "cryptography"
@@ -2320,6 +2322,26 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/5a/dc/491b7661614ab97483abf2056be1deee4dc2490ecbf7bff9ab5cdbac86e1/pyreadline3-3.5.4-py3-none-any.whl", hash = "sha256:eaf8e6cc3c49bcccf145fc6067ba8643d1df34d604a1ec0eccbf7a18e6d3fae6", size = 83178, upload-time = "2024-09-19T02:40:08.598Z" },
 ]
 
+[[package]]
+name = "pysam"
+version = "0.24.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/09/9b/e856ad525ed0847810cca936ee551b8e0a709525fea34e0e0e723173f62d/pysam-0.24.0.tar.gz", hash = "sha256:db0f86c15532ef5dad263748324f45d9a639668e3497d8cabce54ef47a1a78d9", size = 5201690, upload-time = "2026-04-27T12:26:50.879Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/25/ed/5b642a89de9a4cf5ce21f70b4c2c2cd10720ef12cb6ef60b005ecf9dcb08/pysam-0.24.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d06a94e2886cb8affba77bc4fe337513f29387113d708d14609765aa8bb143d3", size = 6217780, upload-time = "2026-04-27T12:25:00.598Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/39/2c9aea082383c8d41d0d8b8f9907dc6273f8cee47a8c83d0c8bad02488b4/pysam-0.24.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4d3260795e066f659ea94e6b7cac07727ef2877b874f69029b6a2e48551d72bd", size = 5946970, upload-time = "2026-04-27T12:25:02.783Z" },
+    { url = "https://files.pythonhosted.org/packages/12/0b/d9c1155257f2c2d0d8a33d15011964bf0c9ad927f85fa57f00965b0c1cfc/pysam-0.24.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9b299f6128601356e25ca94040eef9dadb1e196aa2f36736c93948aaa821c4bc", size = 22114024, upload-time = "2026-04-27T12:25:04.779Z" },
+    { url = "https://files.pythonhosted.org/packages/21/48/92dddc8df65b576c9d30752650c89301b5222d4ac10187724796cedfd723/pysam-0.24.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:98a40440b16bbf67b696914a10acc241a0cd0fa02dcb14aee8e614eb2c5d8b37", size = 22675883, upload-time = "2026-04-27T12:25:07.582Z" },
+    { url = "https://files.pythonhosted.org/packages/07/ff/eee9caac42c255150cc7eae19afa71bd2bb8347fd9895196f7daae2ccd62/pysam-0.24.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:8e4f5f1ebf3bdcad4c3b8f3a656ec2afdc44f0efc96ce8523858556e95d8ca9a", size = 22056750, upload-time = "2026-04-27T12:25:10.127Z" },
+    { url = "https://files.pythonhosted.org/packages/d7/7f/da483a98b11690f3c4b24f4aa12b7e0d0d1e19da8b8ab45785c129240eae/pysam-0.24.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:fcce1edc48a9d8610958296c0aaf3e28723d143113bcd9b7c13b5cd821300d2a", size = 22364034, upload-time = "2026-04-27T12:25:12.709Z" },
+    { url = "https://files.pythonhosted.org/packages/68/b3/1d586b4db6ff7220ac1df4b9588fae7c2622680ca51d53aa7cf32ee73604/pysam-0.24.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:48524b2132158ffeca7ca21c52490e92dca6687446faad95522db2354e376641", size = 8711987, upload-time = "2026-04-27T12:25:15.415Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/29/5fe6f53416cfc17cbe1440f5f55b655e5dcb64b67e18cae9b0a38366db0b/pysam-0.24.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:58e347406cec254bd68ac0f50da1e772273c3c891f65eab4b54641f2a89e8644", size = 8441919, upload-time = "2026-04-27T12:25:17.984Z" },
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+    { url = "https://files.pythonhosted.org/packages/91/70/852f6917e9d7592482db2c43ccd91fa57158cf20ba07c31e9a477af84b9d/pysam-0.24.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d528fc09a44c85b7d4018ba8be543e1cc2e7a4d06eeb779a28ac34947e182f5a", size = 25069351, upload-time = "2026-04-27T12:25:27.617Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/6b/0596765a59253958488fc7a8816df347d815ac3850a7a9cab785c1b1e184/pysam-0.24.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:1e9f20388c80dd0e4c09dfd7fe38adc9fdf771ec665e071e1b7350dc8da2d692", size = 25378507, upload-time = "2026-04-27T12:25:30.504Z" },
+]
+
 [[package]]
 name = "pyspark"
 version = "3.5.3"
@@ -2728,7 +2750,7 @@ wheels = [
 
 [[package]]
 name = "talos"
-version = "11.0.0"
+version = "11.1.0"
 source = { editable = "." }
 dependencies = [
     { name = "clinvarbitration" },
@@ -2762,6 +2784,9 @@ docs = [
     { name = "mkdocs-include-markdown-plugin" },
     { name = "mkdocs-material" },
 ]
+stripy = [
+    { name = "pysam" },
+]
 test = [
     { name = "bump-my-version" },
     { name = "pre-commit" },
@@ -2776,7 +2801,7 @@ requires-dist = [
     { name = "clinvarbitration", specifier = "~=2.2.7" },
     { name = "cloudpathlib", extras = ["all"], specifier = ">=0.16.0" },
     { name = "cpg-flow", marker = "extra == 'cpg'", specifier = "~=1.3" },
-    { name = "cpg-utils", specifier = ">=5.4.1" },
+    { name = "cpg-utils", specifier = "~=5.7" },
     { name = "cyvcf2", specifier = ">=0.30.18" },
     { name = "google-cloud-storage", marker = "extra == 'cpg'", specifier = "==2.14.0" },
     { name = "grpcio-status", specifier = ">=1.48,<1.50" },
@@ -2792,6 +2817,7 @@ requires-dist = [
     { name = "pendulum", specifier = ">=3.1.0" },
     { name = "pre-commit", marker = "extra == 'test'" },
     { name = "pydantic", specifier = ">=2.5.2" },
+    { name = "pysam", marker = "extra == 'stripy'" },
     { name = "pytest", marker = "extra == 'test'" },
     { name = "pytest-httpx", marker = "extra == 'test'" },
     { name = "pytest-xdist", marker = "extra == 'test'", specifier = ">=3.6.0" },
@@ -2801,7 +2827,7 @@ requires-dist = [
     { name = "tenacity", specifier = ">=9.0.0" },
     { name = "toml", specifier = "==0.10.2" },
 ]
-provides-extras = ["cpg", "test", "docs"]
+provides-extras = ["cpg", "test", "docs", "stripy"]
 
 [[package]]
 name = "tenacity"