diff --git a/src/depiction_targeted_preproc/pipeline/prepare_inputs.py b/src/depiction_targeted_preproc/pipeline/prepare_inputs.py
index 752435f..0bad7a9 100644
--- a/src/depiction_targeted_preproc/pipeline/prepare_inputs.py
+++ b/src/depiction_targeted_preproc/pipeline/prepare_inputs.py
@@ -64,19 +64,9 @@ def write_inputs_spec(dataset_id: int, imzml_resource_id: int, client: Bfabric,
 def prepare_inputs(
     client: Bfabric,
     sample_dir: Path,
-    workunit_id: int,
+    dataset_id: int,
+    imzml_resource_id: int,
     ssh_user: str | None,
 ) -> None:
-    dataset_id, imzml_resource_id = _get_resource_flow_ids(client=client, workunit_id=workunit_id)
     write_inputs_spec(dataset_id=dataset_id, imzml_resource_id=imzml_resource_id, client=client, sample_dir=sample_dir)
     prepare_folder(inputs_yaml=sample_dir / "inputs.yml", target_folder=sample_dir, client=client, ssh_user=ssh_user)
-
-
-def _get_resource_flow_ids(client: Bfabric, workunit_id: int) -> tuple[int, int]:
-    workunit = Workunit.find(id=workunit_id, client=client)
-    dataset_id = workunit.parameter_values["mass_list_id"]
-    imzml_resources = [r for r in workunit.input_resources if r["name"].endswith(".imzML")]
-    if len(imzml_resources) != 1:
-        raise ValueError(f"Expected exactly one .imzML resource, found {len(imzml_resources)}")
-    imzml_resource_id = imzml_resources[0].id
-    return dataset_id, imzml_resource_id
diff --git a/src/depiction_targeted_preproc/pipeline/run.py b/src/depiction_targeted_preproc/pipeline/run.py
index 3c3ebf2..f4eef12 100644
--- a/src/depiction_targeted_preproc/pipeline/run.py
+++ b/src/depiction_targeted_preproc/pipeline/run.py
@@ -1,18 +1,66 @@
+import zipfile
 from pathlib import Path
 
 import cyclopts
+import yaml
 from bfabric import Bfabric
+from bfabric.entities import Workunit
 from depiction_targeted_preproc.pipeline.prepare_inputs import prepare_inputs
-from depiction_targeted_preproc.pipeline.prepare_params import prepare_params
+from depiction_targeted_preproc.pipeline.prepare_params import prepare_params, Params
+from depiction_targeted_preproc.pipeline.store_outputs import store_outputs
+from depiction_targeted_preproc.pipeline_config.artifacts_mapping import get_result_files_new
+from depiction_targeted_preproc.workflow.snakemake_invoke import SnakemakeInvoke
 
 app = cyclopts.App()
 
 
+def _get_resource_flow_ids(client: Bfabric, workunit_id: int) -> tuple[int, int, str]:
+    workunit = Workunit.find(id=workunit_id, client=client)
+    dataset_id = workunit.parameter_values["mass_list_id"]
+    imzml_resources = [r for r in workunit.input_resources if r["name"].endswith(".imzML")]
+    if len(imzml_resources) != 1:
+        raise ValueError(f"Expected exactly one .imzML resource, found {len(imzml_resources)}")
+    imzml_resource_id = imzml_resources[0].id
+    sample_name = Path(imzml_resources[0]["name"]).stem
+    return dataset_id, imzml_resource_id, sample_name
+
+
+def run_workflow(sample_dir: Path) -> Path:
+    # TODO to be refactored
+    params = Params.model_validate(yaml.safe_load((sample_dir / "params.yml").read_text()))
+    result_files = get_result_files_new(requested_artifacts=params.requested_artifacts, sample_dir=sample_dir)
+
+    # invoke snakemake
+    SnakemakeInvoke().invoke(work_dir=sample_dir.parent, result_files=result_files)
+
+    # zip the results
+    sample_name = sample_dir.name
+    output_dir = sample_dir.parent / "output"
+    output_dir.mkdir(exist_ok=True)
+    zip_file_path = output_dir / f"{sample_name}.zip"
+    with zipfile.ZipFile(zip_file_path, "w") as zip_file:
+        for result_file in result_files:
+            zip_entry_path = result_file.relative_to(sample_dir.parent)
+            zip_file.write(result_file, arcname=zip_entry_path)
+    return zip_file_path
+
+
 @app.default()
-def run(workunit_id: int, sample_dir: Path, ssh_user: str | None = None) -> None:
+def run(workunit_id: int, work_dir: Path, ssh_user: str | None = None) -> None:
     client = Bfabric.from_config()
+    dataset_id, imzml_resource_id, sample_name = _get_resource_flow_ids(client=client, workunit_id=workunit_id)
+    sample_dir = work_dir / sample_name
+
     prepare_params(client=client, sample_dir=sample_dir, workunit_id=workunit_id)
-    prepare_inputs(client=client, sample_dir=sample_dir, workunit_id=workunit_id, ssh_user=ssh_user)
+    prepare_inputs(
+        client=client,
+        sample_dir=sample_dir,
+        dataset_id=dataset_id,
+        imzml_resource_id=imzml_resource_id,
+        ssh_user=ssh_user,
+    )
+    zip_file_path = run_workflow(sample_dir=sample_dir)
+    store_outputs(client=client, zip_file_path=zip_file_path, workunit_id=workunit_id)
 
 
 if __name__ == "__main__":