diff --git a/.github/workflows/test-runner.yml b/.github/workflows/test-runner.yml
index 8886ca39..a443b567 100644
--- a/.github/workflows/test-runner.yml
+++ b/.github/workflows/test-runner.yml
@@ -42,3 +42,24 @@ jobs:
       - name: Run pytest preprocessing
         run: pytest -v ${{ github.workspace }}/tests/preprocessing
         shell: micromamba-shell {0}
+
+  DeepRVAT-Tests-Runner-Annotations:
+    runs-on: ubuntu-latest
+    steps:
+
+      - name: Check out repository code
+        uses: actions/checkout@v4
+      - uses: mamba-org/setup-micromamba@v1.8.0
+        with:
+          environment-name: deeprvat-annotation-gh-action
+          environment-file: ${{ github.workspace }}/deeprvat_annotations.yml
+          cache-environment: true
+          cache-downloads: true
+
+      - name: Install DeepRVAT
+        run: pip install -e ${{ github.workspace }}
+        shell: micromamba-shell {0}
+
+      - name: Run pytest annotations
+        run: pytest -v ${{ github.workspace }}/tests/annotations
+        shell: micromamba-shell {0}
diff --git a/deeprvat/annotations/annotations.py b/deeprvat/annotations/annotations.py
index ee534712..2ca4f2f8 100644
--- a/deeprvat/annotations/annotations.py
+++ b/deeprvat/annotations/annotations.py
@@ -1,5 +1,7 @@
 import logging
 import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1"
 import pickle
 import random
 import sys
@@ -1080,7 +1082,7 @@ def aggregate_abscores(
         delayed(process_chunk)(
             i, abs_splice_res_dir, tissues_to_exclude, tissue_agg_function, ca_shortened
         )
-        for i in tqdm(os.listdir(abs_splice_res_dir))
+        for i in tqdm(sorted(os.listdir(abs_splice_res_dir)))
     )
     all_absplice_scores = list(output_generator)
 
@@ -1360,45 +1362,6 @@ def merge_deepsea_pcas(
     merged.to_parquet(out_file)
 
 
-@cli.command()
-@click.argument("in_variants", type=click.Path(exists=True))
-@click.argument("out_variants", type=click.Path())
-def process_annotations(in_variants: str, out_variants: str):
-    """
-    Process variant annotations, filter for canonical variants, and aggregate consequences.
-
-    Parameters:
-    - in_variants (str): Path to the input variant annotation file in parquet format.
-    - out_variants (str): Path to save the processed variant annotation file in parquet format.
-
-    Returns:
-    None
-
-    Notes:
-    - The function reads the input variant annotation file.
-    - It filters for canonical variants where the 'CANONICAL' column is equal to 'YES'.
-    - The 'Gene' column is renamed to 'gene_id'.
-    - Consequences for different alleles are aggregated by combining the variant ID with the gene ID.
-    - The processed variant annotations are saved to the specified output file.
-
-    Example:
-    $ python annotations.py process_annotations input_variants.parquet output_variants.parquet
-    """
-    variant_path = Path(in_variants)
-    variants = pd.read_parquet(variant_path)
-
-    logger.info("filtering for canonical variants")
-
-    variants = variants.loc[variants.CANONICAL == "YES"]
-    variants.rename(columns={"Gene": "gene_id"}, inplace=True)
-
-    logger.info("aggregating consequences for different alleles")
-
-    # combining variant id with gene id
-    variants["censequence_id"] = variants["id"].astype(str) + variants["gene_id"]
-    variants.to_parquet(out_variants, compression="zstd")
-
-
 def process_chunk_addids(chunk: pd.DataFrame, variants: pd.DataFrame) -> pd.DataFrame:
     """
     Process a chunk of data by adding identifiers from a variants dataframe.
@@ -1507,16 +1470,14 @@ def add_ids(annotation_file: str, variant_file: str, njobs: int, out_file: str):
 @cli.command()
 @click.argument("annotation_file", type=click.Path(exists=True))
 @click.argument("variant_file", type=click.Path(exists=True))
-@click.argument("njobs", type=int)
 @click.argument("out_file", type=click.Path())
-def add_ids_dask(annotation_file: str, variant_file: str, njobs: int, out_file: str):
+def add_ids_dask(annotation_file: str, variant_file: str, out_file: str):
     """
     Add identifiers from a variant file to an annotation file using Dask and save the result.
 
     Parameters:
     - annotation_file (str): Path to the input annotation file in Parquet format.
     - variant_file (str): Path to the input variant file in Parquet format.
-    - njobs (int): Number of parallel jobs to process the data.
     - out_file (str): Path to save the processed data in Parquet format.
 
     Returns:
@@ -1532,7 +1493,7 @@ def add_ids_dask(annotation_file: str, variant_file: str, njobs: int, out_file:
     $ python annotations.py add_ids_dask annotation_data.parquet variant_data.parquet 4 processed_data.parquet
     """
     data = dd.read_parquet(annotation_file, blocksize=25e9)
-    all_variants = pd.read_table(variant_file)
+    all_variants = pd.read_parquet(variant_file)
     data = data.rename(
         columns={
             "#CHROM": "chrom",
@@ -1705,7 +1666,7 @@ def merge_annotations(
     logger.info("load variant_file")
 
     logger.info(f"reading in {variant_file}")
-    variants = pd.read_csv(variant_file, sep="\t")
+    variants = pd.read_parquet(variant_file)
 
     logger.info("merge vep to variants M:1")
     ca = vep_df.merge(
@@ -1777,7 +1738,23 @@ def process_vep(
         vcf_file, names=["chrom", "pos", "#Uploaded_variation", "ref", "alt"]
     )
     if "#Uploaded_variation" in vep_file.columns:
-        vep_file = vep_file.merge(vcf_df, on="#Uploaded_variation")
+        vep_file = vep_file.merge(vcf_df, on="#Uploaded_variation", how="left")
+        if vep_file.chrom.isna().sum() > 0:
+            vep_file.loc[vep_file.chrom.isna(), ["chrom", "pos", "ref", "alt"]] = (
+                vep_file[vep_file["chrom"].isna()]["#Uploaded_variation"]
+                .str.replace("_", ":")
+                .str.replace("/", ":")
+                .str.split(":", expand=True)
+                .values
+            )
+    assert vep_file.chrom.isna().sum() == 0
+    assert vep_file.pos.isna().sum() == 0
+    assert vep_file.ref.isna().sum() == 0
+    assert vep_file.alt.isna().sum() == 0
+    assert (
+        vep_file[["chrom", "pos", "ref", "alt"]].drop_duplicates().shape
+        == vcf_df[["chrom", "pos", "ref", "alt"]].drop_duplicates().shape
+    )
 
     if "pos" in vep_file.columns:
         vep_file["pos"] = vep_file["pos"].astype(int)
@@ -1979,7 +1956,7 @@ def get_af_from_gt(genotype_file: str, variants_filepath: str, out_file: str):
     """
     import h5py
 
-    variants = pd.read_table(variants_filepath)
+    variants = pd.read_parquet(variants_filepath)
     max_variant_id = variants["id"].max()
 
     logger.info("Computing allele frequencies")
@@ -2042,19 +2019,19 @@ def calculate_maf(annotations_path: str, out_file: str):
 
 
 @cli.command()
-@click.argument("protein_id_file", type=click.Path(exists=True))
+@click.argument("gene_id_file", type=click.Path(exists=True))
 @click.argument("annotations_path", type=click.Path(exists=True))
 @click.argument("out_file", type=click.Path())
-def add_protein_ids(protein_id_file: str, annotations_path: str, out_file: str):
+def add_gene_ids(gene_id_file: str, annotations_path: str, out_file: str):
     """
-    Add protein IDs to the annotations based on protein ID mapping file.
+    Add gene IDs to the annotations based on gene ID mapping file.
 
     Parameters:
-    - protein_id_file (str): Path to the protein ID mapping file.
+    - gene_id_file (str): Path to the gene ID mapping file.
     - annotations_path (str): Path to the annotations file.
     - out_file (str): Path to the output file to save the annotations with protein IDs.
     """
-    genes = pd.read_parquet(protein_id_file)
+    genes = pd.read_parquet(gene_id_file)
     genes[["gene_base", "feature"]] = genes["gene"].str.split(".", expand=True)
     genes.drop(columns=["feature", "gene", "gene_name", "gene_type"], inplace=True)
     genes.rename(columns={"id": "gene_id"}, inplace=True)
@@ -2069,7 +2046,7 @@ def add_protein_ids(protein_id_file: str, annotations_path: str, out_file: str):
 @cli.command()
 @click.argument("gtf_filepath", type=click.Path(exists=True))
 @click.argument("out_file", type=click.Path())
-def create_protein_id_file(gtf_filepath: str, out_file: str):
+def create_gene_id_file(gtf_filepath: str, out_file: str):
     """
     Create a protein ID mapping file from the GTF file.
 
diff --git a/deeprvat/cv_utils.py b/deeprvat/cv_utils.py
index 7c97271e..68a285e5 100644
--- a/deeprvat/cv_utils.py
+++ b/deeprvat/cv_utils.py
@@ -59,9 +59,11 @@ def spread_config(
     cv_path = f"{config_template['cv_path']}/{n_folds}_fold"
     for module in data_modules:
         config = copy.deepcopy(config_template)
-        sample_file = f"{cv_path}/samples_{split}{fold}.pkl"
-        logger.info(f"setting sample file {sample_file}")
-        config["sample_file"] = sample_file
+        data_slots = DATA_SLOT_DICT[module]
+        for data_slot in data_slots:
+            sample_file = f"{cv_path}/samples_{split}{fold}.pkl"
+            logger.info(f"setting sample file {sample_file}")
+            config[data_slot]["dataset_config"]["sample_file"] = sample_file
 
         if (module == "deeprvat") | (module == "deeprvat_pretrained"):
             logger.info("Writing baseline directories")
@@ -89,7 +91,8 @@ def generate_test_config(input_config, out_file, fold, n_folds):
     split = "test"
     sample_file = f"{cv_path}/samples_{split}{fold}.pkl"
     logger.info(f"setting sample file {sample_file}")
-    config["sample_file"] = sample_file
+    for data_slot in DATA_SLOT_DICT["deeprvat"]:
+        config[data_slot]["dataset_config"]["sample_file"] = sample_file
     with open(out_file, "w") as f:
         yaml.dump(config, f)
 
diff --git a/deeprvat/data/dense_gt.py b/deeprvat/data/dense_gt.py
index 7ebdaea9..12247518 100644
--- a/deeprvat/data/dense_gt.py
+++ b/deeprvat/data/dense_gt.py
@@ -362,7 +362,7 @@ def setup_phenotypes(
         # account for the fact that genotypes.h5 and phenotype_df can have different
         # orders of their samples
         self.index_map_geno, _ = get_matched_sample_indices(
-            samples_gt.astype(str), self.samples.astype(str)
+            samples_gt.astype(int), self.samples.astype(int)
         )
         # get_matched_sample_indices is a much, much faster implementation of the code below
         # self.index_map_geno = [np.where(samples_gt.astype(int) == i) for i in self.samples.astype(int)]
@@ -614,20 +614,13 @@ def setup_variants(
                     "Annotation dataframe has inconsistent allele frequency values"
                 )
             variants_with_af = safe_merge(
-                variants[["id"]].reset_index(drop=True), af_annotation, how="left"
+                variants[["id"]].reset_index(drop=True), af_annotation
             )
             assert np.all(
                 variants_with_af["id"].to_numpy() == variants["id"].to_numpy()
             )
-            af_isna = variants_with_af[af_col].isna()
-            if af_isna.sum() > 0:
-                logger.warning(
-                    f"Dropping {af_isna.sum()} variants missing from annotation dataframe"
-                )
-            mask = (
-                (~af_isna)
-                & (variants_with_af[af_col] >= af_threshold)
-                & (variants_with_af[af_col] <= 1 - af_threshold)
+            mask = (variants_with_af[af_col] >= af_threshold) & (
+                variants_with_af[af_col] <= 1 - af_threshold
             )
             mask = mask.to_numpy()
             del variants_with_af
@@ -938,10 +931,11 @@ def get_metadata(self) -> Dict[str, Any]:
         result = {
             "variant_metadata": self.variants[
                 ["id", "common_variant_mask", "rare_variant_mask", "matrix_index"]
-            ],
-            "samples": self.samples,
+            ]
         }
         if self.use_rare_variants:
             if hasattr(self.rare_embedding, "get_metadata"):
-                result["rare_embedding_metadata"] = self.rare_embedding.get_metadata()
+                result.update(
+                    {"rare_embedding_metadata": self.rare_embedding.get_metadata()}
+                )
         return result
diff --git a/deeprvat/deeprvat/associate.py b/deeprvat/deeprvat/associate.py
index 03404067..5af2e770 100644
--- a/deeprvat/deeprvat/associate.py
+++ b/deeprvat/deeprvat/associate.py
@@ -7,7 +7,7 @@
 import sys
 from pathlib import Path
 from pprint import pprint
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Dict, List, Optional, Tuple
 
 import click
 import dask.dataframe as dd
@@ -115,7 +115,9 @@ def cli():
 
 def make_dataset_(
     config: Dict,
+    debug: bool = False,
     data_key="data",
+    samples: Optional[List[int]] = None,
 ) -> Dataset:
     """
     Create a dataset based on the configuration.
@@ -147,17 +149,29 @@ def make_dataset_(
             **copy.deepcopy(data_config["dataset_config"]),
         )
 
+        restrict_samples = config.get("restrict_samples", None)
+        if debug:
+            logger.info("Debug flag set; Using only 1000 samples")
+            ds = Subset(ds, range(1_000))
+        elif samples is not None:
+            ds = Subset(ds, samples)
+        elif restrict_samples is not None:
+            ds = Subset(ds, range(restrict_samples))
+
     return ds
 
 
 @cli.command()
+@click.option("--debug", is_flag=True)
 @click.option("--data-key", type=str, default="data")
-@click.argument("config-file", type=click.Path(exists=True, path_type=Path))
-@click.argument("out-file", type=click.Path(path_type=Path))
-def make_dataset(data_key: str, config_file: Path, out_file: Path):
+@click.argument("config-file", type=click.Path(exists=True))
+@click.argument("out-file", type=click.Path())
+def make_dataset(debug: bool, data_key: str, config_file: str, out_file: str):
     """
     Create a dataset based on the provided configuration and save to a pickle file.
 
+    :param debug: Flag for debugging.
+    :type debug: bool
     :param data_key: Key for dataset configuration in the config dictionary, defaults to "data".
     :type data_key: str
     :param config_file: Path to the configuration file.
@@ -169,7 +183,7 @@ def make_dataset(data_key: str, config_file: Path, out_file: Path):
     with open(config_file) as f:
         config = yaml.safe_load(f)
 
-    ds = make_dataset_(config, data_key=data_key)
+    ds = make_dataset_(config, debug=debug, data_key=data_key)
 
     with open(out_file, "wb") as f:
         pickle.dump(ds, f)
@@ -222,8 +236,6 @@ def compute_burdens_(
     .. note::
         Checkpoint models all corresponding to the same repeat are averaged for that repeat.
     """
-    logger.setLevel(logging.INFO)
-
     if not skip_burdens:
         logger.info("agg_models[*][*].reverse:")
         pprint(
@@ -235,38 +247,10 @@ def compute_burdens_(
 
     data_config = config["data"]
 
-    if "sample_file" in config:
-        sample_file = Path(config["sample_file"])
-        logger.info(f"Using samples from {sample_file}")
-        if sample_file.suffix == ".pkl":
-            with open(sample_file, "rb") as f:
-                sample_ids = np.array(pickle.load(f))
-        elif sample_file.suffix == ".zarr":
-            sample_ids = zarr.load(sample_file)
-        elif sample_file.suffix == ".npy":
-            sample_ids = np.load(sample_file)
-        else:
-            raise ValueError("Unknown file type for sample_file")
-        ds_samples = ds.get_metadata()["samples"]
-        sample_indices = np.where(
-            np.isin(ds_samples.astype(str), sample_ids.astype(str))
-        )[0]
-        if debug:
-            sample_indices = sample_indices[:1000]
-    elif debug:
-        sample_indices = np.arange(min(1000, len(ds)))
-    else:
-        sample_indices = np.arange(len(ds))
-
-    logger.info(
-        f"Computing gene impairment for {sample_indices.shape[0]} samples: {sample_indices}"
-    )
-    ds = Subset(ds, sample_indices)
-
-    ds_full = ds.dataset  # if isinstance(ds, Subset) else ds
+    ds_full = ds.dataset if isinstance(ds, Subset) else ds
     collate_fn = getattr(ds_full, "collate_fn", None)
     n_total_samples = len(ds)
-    ds_full.rare_embedding.skip_embedding = skip_burdens
+    ds.rare_embedding.skip_embedding = skip_burdens
 
     if chunk is not None:
         if n_chunks is None:
@@ -919,7 +903,7 @@ def compute_burdens(
         with open(dataset_file, "rb") as f:
             dataset = pickle.load(f)
     else:
-        dataset = make_dataset_(data_config)
+        dataset = make_dataset_(config)
 
     if torch.cuda.is_available():
         logger.info("Using GPU")
diff --git a/deeprvat/deeprvat/train.py b/deeprvat/deeprvat/train.py
index ae9995a0..d730e03d 100644
--- a/deeprvat/deeprvat/train.py
+++ b/deeprvat/deeprvat/train.py
@@ -82,9 +82,8 @@ def subset_samples(
     input_tensor: torch.Tensor,
     covariates: torch.Tensor,
     y: torch.Tensor,
-    sample_ids: torch.Tensor,
     min_variant_count: int,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     # First sum over annotations (dim 2) for each variant in each gene.
     # Then get the number of non-zero values across all variants in all
     # genes for each sample.
@@ -106,23 +105,21 @@ def subset_samples(
     input_tensor = input_tensor[mask]
     covariates = covariates[mask]
     y = y[mask]
-    sample_ids = sample_ids[mask]
 
     logger.info(f"{input_tensor.shape[0]} / {n_samples_orig} samples kept")
 
-    return input_tensor, covariates, y, sample_ids
+    return input_tensor, covariates, y
 
 
 def make_dataset_(
     debug: bool,
     pickle_only: bool,
     compression_level: int,
-    training_dataset_file: Optional[Path],
+    training_dataset_file: Optional[str],
     config_file: Union[str, Path],
-    input_tensor_out_file: Path,
-    covariates_out_file: Path,
-    y_out_file: Path,
-    samples_out_file: Path,
+    input_tensor_out_file: str,
+    covariates_out_file: str,
+    y_out_file: str,
 ):
     """
     Subfunction of make_dataset()
@@ -228,11 +225,10 @@ def make_dataset_(
     )
     covariates = torch.cat([b["x_phenotypes"] for b in batches])
     y = torch.cat([b["y"] for b in batches])
-    sample_ids = np.concatenate([b["sample"] for b in batches])
 
     logger.info("Subsetting samples by min_variant_count and missing y values")
-    input_tensor, covariates, y, sample_ids = subset_samples(
-        input_tensor, covariates, y, sample_ids, config["training"]["min_variant_count"]
+    input_tensor, covariates, y = subset_samples(
+        input_tensor, covariates, y, config["training"]["min_variant_count"]
     )
 
     if not pickle_only:
@@ -246,7 +242,6 @@ def make_dataset_(
         del input_tensor
         zarr.save_array(covariates_out_file, covariates.numpy())
         zarr.save_array(y_out_file, y.numpy())
-        zarr.save_array(samples_out_file, sample_ids)
 
     # DEBUG
     return ds.dataset
@@ -256,22 +251,20 @@ def make_dataset_(
 @click.option("--debug", is_flag=True)
 @click.option("--pickle-only", is_flag=True)
 @click.option("--compression-level", type=int, default=1)
-@click.option("--training-dataset-file", type=click.Path(exists=True, path_type=Path))
-@click.argument("config-file", type=click.Path(exists=True, path_type=Path))
-@click.argument("input-tensor-out-file", type=click.Path(path_type=Path))
-@click.argument("covariates-out-file", type=click.Path(path_type=Path))
-@click.argument("y-out-file", type=click.Path(path_type=Path))
-@click.argument("samples-out-file", type=click.Path(path_type=Path))
+@click.option("--training-dataset-file", type=click.Path())
+@click.argument("config-file", type=click.Path(exists=True))
+@click.argument("input-tensor-out-file", type=click.Path())
+@click.argument("covariates-out-file", type=click.Path())
+@click.argument("y-out-file", type=click.Path())
 def make_dataset(
     debug: bool,
     pickle_only: bool,
     compression_level: int,
-    training_dataset_file: Optional[Path],
-    config_file: Path,
-    input_tensor_out_file: Path,
-    covariates_out_file: Path,
-    y_out_file: Path,
-    samples_out_file: Path,
+    training_dataset_file: Optional[str],
+    config_file: str,
+    input_tensor_out_file: str,
+    covariates_out_file: str,
+    y_out_file: str,
 ):
     """
     Uses function make_dataset_() to convert dataset to sparse format and stores the respective data
@@ -305,7 +298,6 @@ def make_dataset(
         input_tensor_out_file,
         covariates_out_file,
         y_out_file,
-        samples_out_file,
     )
 
 
@@ -369,10 +361,9 @@ def __init__(
             logger.info("Keeping all input tensors in main memory")
 
         for pheno, pheno_data in self.data.items():
-            n_samples = pheno_data["sample_indices"].shape[0]
-            if pheno_data["y"].shape == (n_samples, 1):
+            if pheno_data["y"].shape == (pheno_data["input_tensor_zarr"].shape[0], 1):
                 pheno_data["y"] = pheno_data["y"].squeeze()
-            elif pheno_data["y"].shape != (n_samples,):
+            elif pheno_data["y"].shape != (pheno_data["input_tensor_zarr"].shape[0],):
                 raise NotImplementedError(
                     "Multi-phenotype training is only implemented via multiple y files"
                 )
@@ -593,10 +584,8 @@ def __init__(
         self.n_annotations = any_pheno_data["input_tensor_zarr"].shape[2]
         self.n_covariates = any_pheno_data["covariates"].shape[1]
 
-        for pheno, pheno_data in self.data.items():
-            # n_samples = pheno_data["input_tensor_zarr"].shape[0]
-            sample_indices = pheno_data["sample_indices"]
-            n_samples = sample_indices.shape[0]
+        for _, pheno_data in self.data.items():
+            n_samples = pheno_data["input_tensor_zarr"].shape[0]
             assert pheno_data["covariates"].shape[0] == n_samples
             assert pheno_data["y"].shape[0] == n_samples
 
@@ -611,33 +600,29 @@ def __init__(
                 samples = self.upsample()
                 n_samples = self.samples.shape[0]
                 logger.info(f"New sample number: {n_samples}")
-            # else:
-            #     samples = np.arange(n_samples)
+            else:
+                samples = np.arange(n_samples)
 
             # Sample self.n_samples * train_proportion samples with replacement
             # for training, use all remaining samples for validation
             if train_proportion == 1.0:
-                self.train_samples = sample_indices
-                self.val_samples = sample_indices
+                self.train_samples = self.samples
+                self.val_samples = self.samples
             else:
                 n_train_samples = round(n_samples * train_proportion)
                 rng = np.random.default_rng()
                 # select training samples from the underlying dataframe
                 train_samples = np.sort(
                     rng.choice(
-                        sample_indices,
-                        size=n_train_samples,
-                        replace=sample_with_replacement,
+                        samples, size=n_train_samples, replace=sample_with_replacement
                     )
                 )
                 # samples which are not part of train_samples, but in samples
                 # are validation samples.
-                val_samples = np.setdiff1d(sample_indices, train_samples)
-                logger.info(
-                    f"{pheno}: Using {train_samples.shape[0]} samples for training, "
-                    f"{val_samples.shape[0]} for validation"
-                )
-                pheno_data["samples"] = {"train": train_samples, "val": val_samples}
+                pheno_data["samples"] = {
+                    "train": train_samples,
+                    "val": np.setdiff1d(samples, train_samples),
+                }
 
         self.save_hyperparameters(
             # "min_variant_count",
@@ -655,8 +640,6 @@ def upsample(self) -> np.ndarray:
         does not work at the moment for multi-phenotype training. Needs some minor changes
         to make it work again
         """
-        raise NotImplementedError
-
         unique_values = self.y.unique()
         if unique_values.size() != torch.Size([2]):
             raise ValueError(
@@ -945,19 +928,18 @@ def run_bagging(
 
 @cli.command()
 @click.option("--debug", is_flag=True)
-@click.option("--training-gene-file", type=click.Path(exists=True, path_type=Path))
+@click.option("--training-gene-file", type=click.Path(exists=True))
 @click.option("--n-trials", type=int, default=1)
 @click.option("--trial-id", type=int)
-@click.option("--samples-to-keep", type=click.Path(exists=True, path_type=Path))
+@click.option("--sample-file", type=click.Path(exists=True))
 @click.option(
     "--phenotype",
     multiple=True,
     type=(
         str,
-        click.Path(exists=True, path_type=Path),
-        click.Path(exists=True, path_type=Path),
-        click.Path(exists=True, path_type=Path),
-        click.Path(exists=True, path_type=Path),
+        click.Path(exists=True),
+        click.Path(exists=True),
+        click.Path(exists=True),
     ),
 )
 @click.argument("config-file", type=click.Path(exists=True))
@@ -965,11 +947,11 @@ def run_bagging(
 @click.argument("hpopt-file", type=click.Path())
 def train(
     debug: bool,
-    training_gene_file: Optional[Path],
+    training_gene_file: Optional[str],
     n_trials: int,
     trial_id: Optional[int],
-    samples_to_keep: Optional[Path],
-    phenotype: Tuple[Tuple[str, Path, Path, Path, Path]],
+    sample_file: Optional[str],
+    phenotype: Tuple[Tuple[str, str, str, str]],
     config_file: str,
     log_dir: str,
     hpopt_file: str,
@@ -985,8 +967,8 @@ def train(
     :type n_trials: int
     :param trial_id: Current trial in range n_trials. (optional)
     :type trial_id: Optional[int]
-    :param samples_to_keep: Path to a pickle file specifying which samples should be considered during training. (optional)
-    :type samples_to_keep: Optional[str]
+    :param sample_file: Path to a pickle file specifying which samples should be considered during training. (optional)
+    :type sample_file: Optional[str]
     :param phenotype: Array of phenotypes, containing an array of paths where the underlying data is stored:
         - str: Phenotype name
         - str: Annotated gene variants as zarr file
@@ -1020,57 +1002,30 @@ def train(
         logger.info(f"GPU memory: {torch.cuda.get_device_properties(0).total_memory}")
 
     logger.info("Loading input data")
-
-    if samples_to_keep is not None or "sample_file" in config:
-        if samples_to_keep is not None:
-            keep_sample_file = samples_to_keep
-            if "sample_file" in config:
-                logger.warning(
-                    f"--samples-to-keep option overrides sample_file in config.yaml"
-                )
-        else:
-            keep_sample_file = Path(config["sample_file"])
-
-        logger.info(f"Using samples from {keep_sample_file}")
-        if keep_sample_file.suffix == ".pkl":
-            with open(keep_sample_file, "rb") as f:
-                sample_ids = np.array(pickle.load(f))
-        elif keep_sample_file.suffix == ".zarr":
-            sample_ids = zarr.load(keep_sample_file)
-        elif keep_sample_file.suffix == ".npy":
-            sample_ids = np.load(keep_sample_file)
-        else:
-            raise ValueError("Unknown file type for sample_file")
+    if sample_file is not None:
+        logger.info(f"Using training samples from {sample_file}")
+        with open(sample_file, "rb") as f:
+            samples = pickle.load(f)["training_samples"]
+        if debug:
+            samples = [s for s in samples if s < 1000]
     else:
-        sample_ids = None
+        samples = slice(None)
 
     data = dict()
     # pack underlying data into a single dict that can be passed to downstream functions
-    for pheno, input_tensor_file, covariates_file, y_file, sample_file in phenotype:
+    for pheno, input_tensor_file, covariates_file, y_file in phenotype:
         data[pheno] = dict()
         data[pheno]["input_tensor_zarr"] = zarr.open(
             input_tensor_file, mode="r"
         )  # TODO: subset here?
-        n_samples = data[pheno]["input_tensor_zarr"].shape[0]
-
-        data[pheno]["sample_ids"] = zarr.load(sample_file)
-        sample_indices = np.arange(n_samples)
-        if sample_ids is not None:
-            sample_indices = sample_indices[
-                np.isin(data[pheno]["sample_ids"], sample_ids)
-            ]
-
-        if debug:
-            sample_indices = sample_indices[:1000]
-
-        data[pheno]["sample_indices"] = sample_indices
-
         data[pheno]["covariates"] = torch.tensor(
-            zarr.load(covariates_file)[sample_indices]
-        )  # TODO: or maybe shouldn't subset here?
-        data[pheno]["y"] = torch.tensor(
-            zarr.load(y_file)[sample_indices]
-        )  # TODO: or maybe shouldn't subset here?
+            zarr.open(covariates_file, mode="r")[:]
+        )[
+            samples
+        ]  # TODO: or maybe shouldn't subset here?
+        data[pheno]["y"] = torch.tensor(zarr.open(y_file, mode="r")[:])[
+            samples
+        ]  # TODO: or maybe shouldn't subset here?
 
         if training_gene_file is not None:
             with open(training_gene_file, "rb") as f:
diff --git a/deeprvat/utils.py b/deeprvat/utils.py
index 2cbbc1ea..a9c18801 100644
--- a/deeprvat/utils.py
+++ b/deeprvat/utils.py
@@ -225,7 +225,6 @@ def safe_merge(
     right: pd.DataFrame,
     validate: str = "1:1",
     equal_row_nums: bool = False,
-    **kwargs,
 ):
     """
     Safely merge two pandas DataFrames.
@@ -252,7 +251,7 @@ def safe_merge(
                 "left and right dataframes are unequal"
             )
 
-    merged = pd.merge(left, right, validate=validate, **kwargs)
+    merged = pd.merge(left, right, validate=validate)
 
     try:
         assert len(merged) == len(left)
diff --git a/deeprvat_annotations.yml b/deeprvat_annotations.yml
index 0c4b6896..dd6feb6e 100644
--- a/deeprvat_annotations.yml
+++ b/deeprvat_annotations.yml
@@ -16,6 +16,7 @@ dependencies:
   - tensorflow=2.11.0
   - pyarrow=11.0.0
   - fastparquet=2023.4.0
+  - bioconda::pyranges=0.0.129
   #comment out lines below if you want to use preinstalled bcftools or samtools
   - bcftools=1.17
   - samtools=1.17
diff --git a/docs/annotations.md b/docs/annotations.md
index 94bd9ff7..29e079fa 100644
--- a/docs/annotations.md
+++ b/docs/annotations.md
@@ -88,6 +88,12 @@ Data for VEP plugins and the CADD cache are stored in `annotation data`.
 
 ## Running the annotation pipeline
 ### Preconfiguration
+- Install the annotation environment
+    ```shell
+    mamba env create -f path/to/deeprvat/deeprvat_annotations.yml
+    mamba activate deeprvat_annotations
+    pip install -e path/to/deeprvat
+    ```
 - Clone the repositories mentioned in [requirements](#requirements) into `repo_dir` and install the needed conda environments with  
     ```shell
     mamba env create -f repo_dir/absplice/environment.yaml
diff --git a/example/annotations/preprocessing_workdir/norm/variants/variants.tsv.gz b/example/annotations/preprocessing_workdir/norm/variants/variants.parquet
similarity index 100%
rename from example/annotations/preprocessing_workdir/norm/variants/variants.tsv.gz
rename to example/annotations/preprocessing_workdir/norm/variants/variants.parquet
diff --git a/example/config.yaml b/example/config.yaml
index 435a4b22..c6a6a3fb 100644
--- a/example/config.yaml
+++ b/example/config.yaml
@@ -22,9 +22,6 @@ baseline_results:
         base: baseline_results
         type: missense/skat
 
-cv_path: sample_files
-n_folds: 5
-
 alpha: 0.05
 
 n_burden_chunks: 2
diff --git a/pipelines/annotations.snakefile b/pipelines/annotations.snakefile
index f3b617c5..db3c6d4d 100644
--- a/pipelines/annotations.snakefile
+++ b/pipelines/annotations.snakefile
@@ -52,7 +52,7 @@ included_chromosomes = config.get(
 )
 
 preprocess_dir = Path(config.get("preprocessing_workdir", ""))
-variant_file = config.get("variant_file_path") or preprocess_dir / 'norm' / 'variants' / 'variants.tsv.gz'
+variant_pq = config.get("variant_parquetfile_path") or preprocess_dir / 'norm' / 'variants' / 'variants.parquet'
 genotype_file = config.get("genotype_file_path") or preprocess_dir / 'preprocessed' / 'genotypes.h5'
 saved_deepripe_models_path = (
     Path(config["faatpipe_repo_dir"]) / "data" / "deepripe_models"
@@ -84,7 +84,10 @@ vep_input_format = config.get("vep_input_format") or "vcf"
 vep_nfork = int(config.get("vep_nfork") or 5)
 af_mode = config.get("af_mode") or "af"
 condel_config_path = vep_plugin_dir / "config" / "Condel" / "config"
-
+if config.get('additional_vep_plugin_cmds'):
+    VEP_plugin_cmds = config['additional_vep_plugin_cmds'].values()
+else:
+    VEP_plugin_cmds = []
 
 #init deepSEA
 deepSEA_tmp_dir = config.get('deepSEA_tmp_dir')or anno_tmp_dir / 'deepSEA_PCA'
@@ -115,7 +118,8 @@ file_paths = [
 
 file_paths = list(chain.from_iterable(file_paths))
 human_sort(file_paths)
-file_stems = [Path(p).stem.split('.')[0] for p in file_paths]
+file_stems = [re.compile(source_variant_file_pattern.format(chr = "(\d+|X|Y)",block='\d+')).search(i).group() for i in file_paths]
+
 
 absplice_download_dir = config.get('absplice_download_dir') or  absplice_repo_dir /'example'/'data'/'resources'/'downloaded_files'
 absplice_output_dir = config.get('absplice_output_dir', anno_tmp_dir /'absplice')
@@ -165,7 +169,7 @@ rule select_rename_fill_columns:
     resources: mem_mb = lambda wildcards, attempt: 15_000 * (attempt + 1),
     shell:
         " ".join([
-            f"python {annotation_python_file}", 
+            f"deeprvat_annotations", 
             "select-rename-fill-annotations",
             "{input.yaml_file}",
             "{input.annotations_path}",
@@ -182,8 +186,8 @@ if not gene_id_file:
         resources: mem_mb = lambda wildcards, attempt: 15_000 * (attempt + 1),
         shell:
             " ".join([
-                f"python {annotation_python_file}", 
-                "create-protein-id-file",
+                f"deeprvat_annotations", 
+                "create-gene-id-file",
                 "{input}",
                 "{output}"
             ])
@@ -198,7 +202,7 @@ rule filter_by_exon_distance:
     resources: mem_mb = lambda wildcards, attempt: 25_000 * (attempt + 1),
     shell:
         " ".join([
-            f"python {annotation_python_file}", 
+            f"deeprvat_annotations", 
             "filter-annotations-by-exon-distance",
             "{input.annotations_path}",
             "{input.gtf_file}",
@@ -214,8 +218,8 @@ rule add_gene_ids:
     resources: mem_mb = lambda wildcards, attempt: 19_000 * (attempt + 1),
     shell:
         " ".join([
-            f"python {annotation_python_file}", 
-            "add-protein-ids",
+            f"deeprvat_annotations", 
+            "add-gene-ids",
             "{input.gene_id_file}",
             "{input.annotations_path}",
             "{output}"
@@ -230,7 +234,7 @@ rule calculate_MAF:
     resources: mem_mb = lambda wildcards, attempt: 15_000 * (attempt + 1),
     shell:
         " ".join([
-            f"python {annotation_python_file}", 
+            f"deeprvat_annotations", 
             "calculate-maf",
             "{input}",
             "{output}"
@@ -249,7 +253,7 @@ rule merge_allele_frequency:
     resources: mem_mb = lambda wildcards, attempt: 15_000 * (attempt + 1),
     shell:
         " ".join([
-            f"python {annotation_python_file}", 
+            f"deeprvat_annotations", 
             "merge-af",
             "{input.annotation_file}",
             "{input.allele_frequencies}",
@@ -264,13 +268,13 @@ rule merge_allele_frequency:
 rule calculate_allele_frequency:
     input: 
         genotype_file = genotype_file,
-        variants = variant_file
+        variants = variant_pq
     output:
         allele_frequencies = anno_tmp_dir / "af_df.parquet"
     resources: mem_mb = lambda wildcards, attempt: 15_000 * (attempt + 1),
     shell:
         " ".join([
-            f"python {annotation_python_file}", 
+            f"deeprvat_annotations", 
             "get-af-from-gt",
             "{input.genotype_file}",
             "{input.variants}",
@@ -293,8 +297,7 @@ rule merge_absplice_scores:
     shell: 
         " ".join(
             [
-                "python",
-                f"{annotation_python_file}",
+                "deeprvat_annotations",
                 "merge-abscores",
                 "{input.current_annotation_file}",
                 "{input.absplice_scores}",
@@ -312,8 +315,7 @@ rule aggregate_absplice_scores:
     shell:
         " ".join(
             [
-                "python",
-                f"{annotation_python_file}",
+                "deeprvat_annotations",
                 "aggregate-abscores {input.current_annotation_file}",
                 str(absplice_output_dir / absplice_main_conf['genome'] / 'dna' ),
                 "{output.score_file} {threads}"
@@ -331,8 +333,7 @@ rule merge_deepsea_pcas:
     shell:
         " ".join(
             [
-                "python",
-                f"{annotation_python_file}",
+                "deeprvat_annotations",
                 "merge-deepsea-pcas",
                 "{input.annotations}",
                 "{input.deepsea_pcas}",
@@ -355,8 +356,7 @@ rule concat_annotations:
     shell:
         " ".join(
             [
-                "python",
-                str(annotation_python_file),
+                "deeprvat_annotations",
                 "concat-annotations",
                 '{params.joined}',
                 "{output}"
@@ -373,7 +373,7 @@ rule merge_annotations:
         / (source_variant_file_pattern + "_variants.eclip_k5_deepripe.csv.gz"),
         deepripe_hg2=anno_dir
         / (source_variant_file_pattern + "_variants.eclip_hg2_deepripe.csv.gz"),
-        variant_file=variant_file,
+        variant_file=variant_pq,
         vcf_file= anno_tmp_dir / (source_variant_file_pattern + "_variants.vcf"),
     output:
         anno_dir / f"{source_variant_file_pattern}_merged.parquet",
@@ -382,8 +382,7 @@ rule merge_annotations:
         (
             "HEADER=$(grep  -n  '#Uploaded_variation' "
             + "{input.vep}"
-            + "| head | cut -f 1 -d ':') && python "
-            + f"{annotation_python_file} "
+            + "| head | cut -f 1 -d ':') && deeprvat_annotations "
             + "merge-annotations $(($HEADER-1)) {input.vep} {input.deepripe_parclip} {input.deepripe_hg2} {input.deepripe_k5} {input.variant_file} {input.vcf_file} {output}"
         )
 
@@ -398,8 +397,7 @@ rule deepSea_PCA:
             ["mkdir -p",
               str(deepSEA_tmp_dir),
               "&&",
-              "python",
-              f"{annotation_python_file}",
+              "deeprvat_annotations",
               "deepsea-pca",
               "{input.deepsea_anno}",
               f"{str(deepSEA_pca_obj)}",
@@ -412,7 +410,7 @@ rule deepSea_PCA:
 
 rule add_ids_deepSea:
     input:
-        variant_file=variant_file,
+        variant_file=variant_pq,
         annotation_file=deepSEA_tmp_dir / "deepsea_pca.parquet",
     output:
         directory(anno_dir / "all_variants.wID.deepSea.parquet"),
@@ -421,12 +419,10 @@ rule add_ids_deepSea:
     shell:
         " ".join(
             [
-                "python",
-                f"{annotation_python_file}",
+                "deeprvat_annotations",
                 "add-ids-dask",
                 "{input.annotation_file}",
                 "{input.variant_file}",
-                "{threads}",
                 "{output}",
             ]
         )
@@ -449,8 +445,7 @@ rule concat_deepSea:
     shell:
         " ".join(
         [
-            "python",
-            f"{annotation_python_file}",
+            "deeprvat_annotations",
             "concatenate-deepsea",
             "{params.joined}",
             "{output}",
@@ -484,7 +479,7 @@ rule deepRiPe_parclip:
     threads: n_jobs_deepripe
     resources: mem_mb = lambda wildcards, attempt: 5_000 * (attempt + 1),
     shell:
-        f"mkdir -p {pybedtools_tmp_path / 'parclip'} && python {annotation_python_file} scorevariants-deepripe {{input.variants}} {anno_dir}  {{input.fasta}} {pybedtools_tmp_path / 'parclip'} {saved_deepripe_models_path} {{threads}} 'parclip'"
+        f"mkdir -p {pybedtools_tmp_path / 'parclip'} && deeprvat_annotations scorevariants-deepripe {{input.variants}} {anno_dir}  {{input.fasta}} {pybedtools_tmp_path / 'parclip'} {saved_deepripe_models_path} {{threads}} 'parclip'"
 
 
 rule deepRiPe_eclip_hg2:
@@ -496,7 +491,7 @@ rule deepRiPe_eclip_hg2:
     threads: lambda wildcards, attempt: n_jobs_deepripe * attempt
     resources: mem_mb = lambda wildcards, attempt: 5_000 * (attempt + 1),
     shell:
-        f"mkdir -p {pybedtools_tmp_path / 'hg2'} && python {annotation_python_file} scorevariants-deepripe {{input.variants}} {anno_dir}  {{input.fasta}} {pybedtools_tmp_path / 'hg2'} {saved_deepripe_models_path} {{threads}} 'eclip_hg2'"
+        f"mkdir -p {pybedtools_tmp_path / 'hg2'} && deeprvat_annotations scorevariants-deepripe {{input.variants}} {anno_dir}  {{input.fasta}} {pybedtools_tmp_path / 'hg2'} {saved_deepripe_models_path} {{threads}} 'eclip_hg2'"
 
 
 rule deepRiPe_eclip_k5:
@@ -508,7 +503,7 @@ rule deepRiPe_eclip_k5:
     threads: lambda wildcards, attempt: n_jobs_deepripe * attempt
     resources: mem_mb = lambda wildcards, attempt: 5_000 * (attempt + 1),
     shell:
-        f"mkdir -p {pybedtools_tmp_path / 'k5'} && python {annotation_python_file} scorevariants-deepripe {{input.variants}} {anno_dir}  {{input.fasta}} {pybedtools_tmp_path / 'k5'} {saved_deepripe_models_path} {{threads}} 'eclip_k5'"
+        f"mkdir -p {pybedtools_tmp_path / 'k5'} && deeprvat_annotations scorevariants-deepripe {{input.variants}} {anno_dir}  {{input.fasta}} {pybedtools_tmp_path / 'k5'} {saved_deepripe_models_path} {{threads}} 'eclip_k5'"
 
 
 rule vep:
@@ -562,7 +557,7 @@ rule vep:
                 "--no_stats",
                 "--per_gene",
                 "--pick_order biotype,mane_select,mane_plus_clinical,canonical,appris,tsl,ccds,rank,length,ensembl,refseq"
-            ]+['--plugin '+i for i in config['additional_vep_plugin_cmds'].values()]
+            ]+['--plugin '+i for i in VEP_plugin_cmds]
         )
 
 
diff --git a/pipelines/association_testing/association_dataset.snakefile b/pipelines/association_testing/association_dataset.snakefile
index 818e43de..9c8ba228 100644
--- a/pipelines/association_testing/association_dataset.snakefile
+++ b/pipelines/association_testing/association_dataset.snakefile
@@ -1,5 +1,8 @@
 configfile: "config.yaml"
 
+debug_flag = config.get('debug', False)
+debug = '--debug ' if debug_flag else ''
+
 rule association_dataset:
     input:
         config = '{phenotype}/deeprvat/hpopt_config.yaml'
@@ -11,5 +14,6 @@ rule association_dataset:
     priority: 30
     shell:
         'deeprvat_associate make-dataset '
+        + debug +
         '{input.config} '
         '{output}'
diff --git a/pipelines/association_testing_pretrained.snakefile b/pipelines/association_testing_pretrained.snakefile
index a26b7f1b..9d7799df 100644
--- a/pipelines/association_testing_pretrained.snakefile
+++ b/pipelines/association_testing_pretrained.snakefile
@@ -9,7 +9,7 @@ training_phenotypes = config["training"].get("phenotypes", phenotypes)
 
 n_burden_chunks = config.get('n_burden_chunks', 1) if not debug_flag else 2
 n_regression_chunks = config.get('n_regression_chunks', 40) if not debug_flag else 2
-n_avg_chunks = config.get('n_avg_chunks', 40)
+n_avg_chunks = config.get('n_avg_chunks', 1)
 n_trials = config['hyperparameter_optimization']['n_trials']
 n_bags = config['training']['n_bags'] if not debug_flag else 3
 n_repeats = config['n_repeats']
diff --git a/pipelines/cv_training/cv_burdens.snakefile b/pipelines/cv_training/cv_burdens.snakefile
index 0a6960bc..f618e136 100644
--- a/pipelines/cv_training/cv_burdens.snakefile
+++ b/pipelines/cv_training/cv_burdens.snakefile
@@ -42,6 +42,10 @@ rule make_deeprvat_test_config:
 # pass the sample file here
 # then just use this data set nomrally for burden computation
 use rule association_dataset from deeprvat_associate as deeprvat_association_dataset with:
+    input:
+        config="cv_split{cv_split}/deeprvat/{phenotype}/deeprvat/hpopt_config_test.yaml",
+    output:
+        "cv_split{cv_split}/deeprvat/{phenotype}/deeprvat/association_dataset.pkl",
     threads: 4
 
 
@@ -89,31 +93,14 @@ rule combine_test_burdens:
         )
 
 
-use rule link_burdens from deeprvat_associate as deeprvat_link_burdens with:
-    input:
-        checkpoints = expand(
-            'cv_split{cv_split}/deeprvat' / model_path / "repeat_{repeat}/best/bag_{bag}.ckpt",
-            cv_split=range(cv_splits), repeat=range(n_repeats), bag=range(n_bags)
-        ),
-        dataset = 'cv_split0/deeprvat/{phenotype}/deeprvat/association_dataset.pkl',
-        data_config = 'cv_split{cv_split}/deeprvat/{phenotype}/deeprvat/hpopt_config_test.yaml',
-        model_config = "cv_split{cv_split}/deeprvat" / model_path / 'config.yaml',
+use rule link_burdens from deeprvat_workflow as deeprvat_link_burdens with:
     params:
         prefix="cv_split{cv_split}/deeprvat",
 
 
-use rule compute_burdens from deeprvat_associate as deeprvat_compute_burdens with:
-    input:
-        reversed = "cv_split{cv_split}/deeprvat" / model_path / "reverse_finished.tmp",
-        checkpoints = expand(
-            'cv_split{cv_split}/deeprvat' / model_path / "repeat_{repeat}/best/bag_{bag}.ckpt",
-            cv_split=range(cv_splits), repeat=range(n_repeats), bag=range(n_bags)
-        ),
-        dataset = 'cv_split0/deeprvat/{phenotype}/deeprvat/association_dataset.pkl',
-        data_config = 'cv_split{cv_split}/deeprvat/{phenotype}/deeprvat/hpopt_config_test.yaml',
-        model_config = "cv_split{cv_split}/deeprvat" / model_path / 'config.yaml',
+use rule compute_burdens from deeprvat_workflow as deeprvat_compute_burdens with:
     params:
         prefix="cv_split{cv_split}/deeprvat",
 
 
-use rule reverse_models from deeprvat_associate as deeprvat_reverse_models
+use rule reverse_models from deeprvat_workflow as deeprvat_reverse_models
diff --git a/pipelines/cv_training/cv_training.snakefile b/pipelines/cv_training/cv_training.snakefile
index 0cd08647..2ad880ae 100644
--- a/pipelines/cv_training/cv_training.snakefile
+++ b/pipelines/cv_training/cv_training.snakefile
@@ -52,36 +52,18 @@ use rule best_training_run from deeprvat_workflow as deeprvat_best_training_run
 
 use rule train from deeprvat_workflow as deeprvat_train with:
     priority: 1000
-    input:
-        config = expand('cv_split{{cv_split}}/deeprvat/{phenotype}/deeprvat/hpopt_config.yaml',
-                        phenotype=training_phenotypes),
-        input_tensor = expand('cv_split0/deeprvat/{phenotype}/deeprvat/input_tensor.zarr',
-                              phenotype=training_phenotypes),
-        covariates = expand('cv_split0/deeprvat/{phenotype}/deeprvat/covariates.zarr',
-                            phenotype=training_phenotypes),
-        y = expand('cv_split0/deeprvat/{phenotype}/deeprvat/y.zarr',
-                   phenotype=training_phenotypes),
-        samples = expand('cv_split0/deeprvat/{phenotype}/deeprvat/sample_ids.zarr',
-                   phenotype=training_phenotypes),
     params:
         prefix = 'cv_split{cv_split}/deeprvat',
         phenotypes = " ".join( #TODO like need the prefix here as well
             [f"--phenotype {p} "
-             f"cv_split0/deeprvat/{p}/deeprvat/input_tensor.zarr "
-             f"cv_split0/deeprvat/{p}/deeprvat/covariates.zarr "
-             f"cv_split0/deeprvat/{p}/deeprvat/y.zarr "
-             f"cv_split0/deeprvat/{p}/deeprvat/sample_ids.zarr "
+             f"cv_split{{cv_split}}/deeprvat/{p}/deeprvat/input_tensor.zarr "
+             f"cv_split{{cv_split}}/deeprvat/{p}/deeprvat/covariates.zarr "
+             f"cv_split{{cv_split}}/deeprvat/{p}/deeprvat/y.zarr"
              for p in training_phenotypes])
 
 use rule training_dataset from deeprvat_workflow as deeprvat_training_dataset
-    # output:
-    #     input_tensor=directory("{phenotype}/deeprvat/input_tensor.zarr"),
-    #     covariates=directory("{phenotype}/deeprvat/covariates.zarr"),
-    #     y=directory("{phenotype}/deeprvat/y.zarr"),
-    #     sample_ids=directory("{phenotype}/deeprvat/sample_ids.zarr"),
-
 
-# use rule training_dataset_pickle from deeprvat_workflow as deeprvat_training_dataset_pickle
+use rule training_dataset_pickle from deeprvat_workflow as deeprvat_training_dataset_pickle
 
 use rule config from deeprvat_workflow as deeprvat_config with:
     input:
@@ -97,4 +79,12 @@ use rule config from deeprvat_workflow as deeprvat_config with:
             for b in input.baseline
         ])  if wildcards.phenotype in training_phenotypes else ' ',
         baseline_out = lambda wildcards: f'--baseline-results-out cv_split{wildcards.cv_split}/deeprvat/{wildcards.phenotype}/deeprvat/baseline_results.parquet' if wildcards.phenotype in training_phenotypes else ' ',
-        seed_genes_out = lambda wildcards: f'--seed-genes-out cv_split{wildcards.cv_split}/deeprvat/{wildcards.phenotype}/deeprvat/seed_genes.parquet' if wildcards.phenotype in training_phenotypes else ' '
+        seed_genes_out = lambda wildcards: f'--seed-genes-out cv_split{wildcards.cv_split}/deeprvat/{wildcards.phenotype}/deeprvat/seed_genes.parquet' if wildcards.phenotype in training_phenotypes else ' ',
+        association_only = lambda wildcards: f'--association-only' if wildcards.phenotype not in training_phenotypes else ' '
+
+
+
+
+
+
+
diff --git a/pipelines/cv_training/cv_training_association_testing.snakefile b/pipelines/cv_training/cv_training_association_testing.snakefile
index 14443e83..bb825587 100644
--- a/pipelines/cv_training/cv_training_association_testing.snakefile
+++ b/pipelines/cv_training/cv_training_association_testing.snakefile
@@ -14,7 +14,7 @@ burden_phenotype = phenotypes[0]
 
 n_burden_chunks = config.get("n_burden_chunks", 1) if not debug_flag else 2
 n_regression_chunks = config.get("n_regression_chunks", 40) if not debug_flag else 2
-n_avg_chunks = config.get('n_avg_chunks', 40)
+n_avg_chunks = config.get('n_avg_chunks', 1)
 n_trials = config["hyperparameter_optimization"]["n_trials"]
 n_bags = config["training"]["n_bags"] if not debug_flag else 3
 n_repeats = config["n_repeats"]
@@ -28,21 +28,26 @@ n_parallel_training_jobs = config["training"].get("n_parallel_jobs", 1)
 wildcard_constraints:
     repeat="\d+",
     trial="\d+",
-    cv_split="\d+",
-    phenotype="[A-z0-9_]+",
 
 
 cv_splits = config.get("n_folds", 5)
 cv_exp = True
 
-
-
 include: "cv_training.snakefile"
 include: "cv_burdens.snakefile"
 include: "../association_testing/burdens.snakefile"
 include: "../association_testing/regress_eval.snakefile"
 
 
+rule all_evaluate:  #regress_eval.snakefile
+    input:
+        significant=expand(
+            "{phenotype}/deeprvat/eval/significant.parquet", phenotype=phenotypes
+        ),
+        results=expand(
+            "{phenotype}/deeprvat/eval/all_results.parquet", phenotype=phenotypes
+        ),
+
 
 rule all_regression:  #regress_eval.snakefile
     input:
@@ -80,25 +85,6 @@ rule all_training:  #cv_training.snakefile
             cv_split=range(cv_splits),
         ),
 
-rule all_training_dataset:  #cv_training.snakefile
-    input:
-        expand(
-            "cv_split0/deeprvat/{phenotype}/deeprvat/input_tensor.zarr",
-            phenotype=training_phenotypes,
-        ),
-        expand(
-            "cv_split0/deeprvat/{phenotype}/deeprvat/covariates.zarr",
-            phenotype=training_phenotypes,
-        ),
-        expand(
-            "cv_split0/deeprvat/{phenotype}/deeprvat/y.zarr",
-            phenotype=training_phenotypes,
-        ),
-        expand(
-            "cv_split0/deeprvat/{phenotype}/deeprvat/sample_ids.zarr",
-            phenotype=training_phenotypes,
-        ),
-
 
 rule all_config:  #cv_training.snakefile
     input:
diff --git a/pipelines/resources/absplice.yaml b/pipelines/resources/absplice.yaml
index 08494895..e837f1d8 100755
--- a/pipelines/resources/absplice.yaml
+++ b/pipelines/resources/absplice.yaml
@@ -29,3 +29,4 @@ dependencies:
     - interpret == 0.2.7
     - interpret-core == 0.2.7
     - git+https://github.com/gagneurlab/splicemap.git@9e9831f32c221e850e26757a5f1c132dcd565640
+    - git+https://github.com/gagneurlab/absplice.git@e4060e63cca074ac938ea28850a38290c7e8b198
diff --git a/pipelines/resources/absplice_splicing_pred_DNA.snakefile b/pipelines/resources/absplice_splicing_pred_DNA.snakefile
index 913780d3..47106018 100644
--- a/pipelines/resources/absplice_splicing_pred_DNA.snakefile
+++ b/pipelines/resources/absplice_splicing_pred_DNA.snakefile
@@ -119,7 +119,7 @@ rule absplice_dna:
     params:
         extra_info = absplice_main_conf['extra_info_dna']
     conda:
-            "absplice"
+        "absplice"
     output:
         absplice_dna = absplice_output_dir / '{genome}' / 'dna' / '{vcf_id}_AbSplice_DNA.csv'
     script:
@@ -127,7 +127,6 @@ rule absplice_dna:
 
 rule all_predict_dna:
     input:
-        #expand([absplice_output_dir / absplice_main_conf['genome'] / 'dna' / source_variant_file_pattern],zip, chr=chromosomes, block=block)
         expand([absplice_output_dir / absplice_main_conf['genome'] / 'dna' / '{file_stem}_AbSplice_DNA.csv'],file_stem=file_stems)
 
 del splicemap5
diff --git a/pipelines/training/config.snakefile b/pipelines/training/config.snakefile
index 93075fff..3799ef7e 100644
--- a/pipelines/training/config.snakefile
+++ b/pipelines/training/config.snakefile
@@ -33,11 +33,15 @@ rule config:
         baseline_out=lambda wildcards: f"--baseline-results-out  {wildcards.phenotype}/deeprvat/baseline_results.parquet"
         if wildcards.phenotype in training_phenotypes
         else " ",
+        association_only=lambda wildcards: f"--association-only"
+        if wildcards.phenotype not in training_phenotypes
+        else " ",
     shell:
         (
             "deeprvat_config update-config "
             "--phenotype {wildcards.phenotype} "
-            "{params.baseline_results}"
+            "{params.association_only} "
+            "{params.baseline_results} "
             "{params.baseline_out} "
             "{params.seed_genes_out} "
             "{input.config} "
diff --git a/pipelines/training/train.snakefile b/pipelines/training/train.snakefile
index 4d8e9cd7..f33fd5b6 100644
--- a/pipelines/training/train.snakefile
+++ b/pipelines/training/train.snakefile
@@ -51,8 +51,7 @@ rule train:
             [f"--phenotype {p} "
              f"{p}/deeprvat/input_tensor.zarr "
              f"{p}/deeprvat/covariates.zarr "
-             f"{p}/deeprvat/y.zarr "
-             f"{p}/deeprvat/sample_ids.zarr "
+             f"{p}/deeprvat/y.zarr"
              for p in training_phenotypes]),
         prefix = '.',
     priority: 1000
@@ -65,8 +64,7 @@ rule train:
         + debug +
         '--trial-id {{2}} '
         "{params.phenotypes} "
-        # 'config.yaml '
-        "{params.prefix}/config.yaml "
+        'config.yaml '
         '{params.prefix}/{model_path}/repeat_{{1}}/trial{{2}} '
         "{params.prefix}/{model_path}/repeat_{{1}}/hyperparameter_optimization.db '&&' "
         "touch {params.prefix}/{model_path}/repeat_{{1}}/trial{{2}}/finished.tmp "
diff --git a/pipelines/training/training_dataset.snakefile b/pipelines/training/training_dataset.snakefile
index 3c40760b..2cf00229 100644
--- a/pipelines/training/training_dataset.snakefile
+++ b/pipelines/training/training_dataset.snakefile
@@ -1,16 +1,15 @@
 rule training_dataset:
     input:
         config="{phenotype}/deeprvat/hpopt_config.yaml",
-        # training_dataset="{phenotype}/deeprvat/training_dataset.pkl",
+        training_dataset="{phenotype}/deeprvat/training_dataset.pkl",
     output:
         input_tensor=directory("{phenotype}/deeprvat/input_tensor.zarr"),
         covariates=directory("{phenotype}/deeprvat/covariates.zarr"),
         y=directory("{phenotype}/deeprvat/y.zarr"),
-        sample_ids=directory("{phenotype}/deeprvat/sample_ids.zarr"),
     threads: 8
     resources:
         mem_mb=lambda wildcards, attempt: 32000 + 12000 * attempt,
-        load=32000,
+        load=16000,
     priority: 5000
     shell:
         (
@@ -19,12 +18,11 @@ rule training_dataset:
             + "--compression-level "
             + str(tensor_compression_level)
             + " "
-            # "--training-dataset-file {input.training_dataset} "
+            "--training-dataset-file {input.training_dataset} "
             "{input.config} "
             "{output.input_tensor} "
             "{output.covariates} "
-            "{output.y} "
-            "{output.sample_ids}"
+            "{output.y}"
         )
 
 
@@ -32,11 +30,11 @@ rule training_dataset_pickle:
     input:
         "{phenotype}/deeprvat/hpopt_config.yaml",
     output:
-        temp("{phenotype}/deeprvat/training_dataset.pkl"),
+        "{phenotype}/deeprvat/training_dataset.pkl",
     threads: 1
     resources:
         mem_mb=40000,  # lambda wildcards, attempt: 38000 + 12000 * attempt
-        load=40000,
+        load=16000,
     shell:
         (
             "deeprvat_train make-dataset "
@@ -44,4 +42,4 @@ rule training_dataset_pickle:
             "--training-dataset-file {output} "
             "{input} "
             "dummy dummy dummy"
-        )
+        )
\ No newline at end of file
diff --git a/pipelines/training_association_testing.snakefile b/pipelines/training_association_testing.snakefile
index 8c360136..b816752e 100644
--- a/pipelines/training_association_testing.snakefile
+++ b/pipelines/training_association_testing.snakefile
@@ -9,7 +9,7 @@ training_phenotypes = config["training"].get("phenotypes", phenotypes)
 
 n_burden_chunks = config.get('n_burden_chunks', 1) if not debug_flag else 2
 n_regression_chunks = config.get('n_regression_chunks', 40) if not debug_flag else 2
-n_avg_chunks = config.get('n_avg_chunks', 40)
+n_avg_chunks = config.get('n_avg_chunks', 1)
 n_trials = config['hyperparameter_optimization']['n_trials']
 n_bags = config['training']['n_bags'] if not debug_flag else 3
 n_repeats = config['n_repeats']
diff --git a/setup.py b/setup.py
index 15f5830b..5aa4fa41 100644
--- a/setup.py
+++ b/setup.py
@@ -29,6 +29,7 @@
             "deeprvat_train=deeprvat.deeprvat.train:cli",
             "deeprvat_associate=deeprvat.deeprvat.associate:cli",
             "deeprvat_preprocess=deeprvat.preprocessing.preprocess:cli",
+            "deeprvat_annotations=deeprvat.annotations.annotations:cli",
             "deeprvat_evaluate=deeprvat.deeprvat.evaluate:evaluate",
             "seed_gene_pipeline=deeprvat.seed_gene_discovery.seed_gene_discovery:cli",
             "seed_gene_evaluate=deeprvat.seed_gene_discovery.evaluate:cli",
diff --git a/tests/annotations/test_annotations.py b/tests/annotations/test_annotations.py
new file mode 100644
index 00000000..4056eed4
--- /dev/null
+++ b/tests/annotations/test_annotations.py
@@ -0,0 +1,652 @@
+from pathlib import Path
+
+import pandas as pd
+import pytest
+from click.testing import CliRunner
+from pandas.testing import assert_frame_equal
+from deeprvat.annotations.annotations import cli as annotations_cli
+
+script_dir = Path(__file__).resolve().parent
+tests_data_dir = script_dir / "test_data"
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, deepsea_scores_1, deepsea_scores_2, out_scores, expected_out_scores",
+    [
+        (
+            "concatenate_deepseascores_small",
+            "deepsea_scores_1.tsv",
+            "deepsea_scores_2.tsv",
+            "out_scores.parquet",
+            "expected.parquet",
+        ),
+        (
+            "concatenate_deepseascores_medium",
+            "deepsea_scores_1.tsv",
+            "deepsea_scores_2.tsv",
+            "out_scores.parquet",
+            "expected.parquet",
+        ),
+    ],
+)
+def test_concatenate_deepsea(
+    test_data_name_dir,
+    deepsea_scores_1,
+    deepsea_scores_2,
+    out_scores,
+    expected_out_scores,
+    tmp_path,
+):
+    cli_runner = CliRunner()
+
+    current_test_data_dir = tests_data_dir / "concatenate_deepsea" / test_data_name_dir
+
+    deepsea_score_file_1 = current_test_data_dir / "input" / deepsea_scores_1
+    deepsea_score_file_2 = current_test_data_dir / "input" / deepsea_scores_2
+    out_scores_file = tmp_path / out_scores
+    expected_out_scores_file = current_test_data_dir / "expected" / expected_out_scores
+
+    cli_parameters = [
+        "concatenate-deepsea",
+        ",".join([deepsea_score_file_1.as_posix(), deepsea_score_file_2.as_posix()]),
+        out_scores_file.as_posix(),
+        "8",
+    ]
+
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+
+    written_results = pd.read_parquet(out_scores_file)
+
+    expected_scores = pd.read_parquet(expected_out_scores_file)
+    assert_frame_equal(written_results, expected_scores, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, deapseascores_file, variant_file, out_df, expected_out_df",
+    [
+        (
+            "add_ids_small",
+            "deepseascores.parquet",
+            "variants.parquet",
+            "out_df.parquet",
+            "expected.parquet",
+        ),
+        (
+            "add_ids_medium",
+            "deepseascores.parquet",
+            "variants.parquet",
+            "out_df.parquet",
+            "expected.parquet",
+        ),
+    ],
+)
+def test_add_ids_dask(
+    test_data_name_dir,
+    deapseascores_file,
+    variant_file,
+    out_df,
+    expected_out_df,
+    tmp_path,
+):
+    cli_runner = CliRunner()
+
+    current_test_data_dir = tests_data_dir / "add_ids_dask" / test_data_name_dir
+
+    deepsea_score_file = current_test_data_dir / "input" / deapseascores_file
+    variant_path = current_test_data_dir / "input" / variant_file
+    out_scores_file = tmp_path / out_df
+    expected_out_scores_file = current_test_data_dir / "expected" / expected_out_df
+
+    cli_parameters = [
+        "add-ids-dask",
+        deepsea_score_file.as_posix(),
+        variant_path.as_posix(),
+        out_scores_file.as_posix(),
+    ]
+
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+
+    written_results = pd.read_parquet(out_scores_file)
+
+    expected_scores = pd.read_parquet(expected_out_scores_file)
+    assert_frame_equal(written_results, expected_scores, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, deapseascores_file, pca_file, mean_sds_file, expected_out_df",
+    [
+        (
+            "deepsea_pca_small",
+            "deepseascores.parquet",
+            "deepsea_pca/pca.npy",
+            "deepsea_pca/mean_sds.parquet",
+            "deepsea_pca.parquet",
+        ),
+        (
+            "deepsea_pca_small",
+            "deepseascores.parquet",
+            "deepsea_pca/pca.npy",
+            "{tmp_path}/mean_sds.parquet",
+            "deepsea_pca.parquet",
+        ),
+        (
+            "deepsea_pca_small",
+            "deepseascores.parquet",
+            "{tmp_path}/pca.npy",
+            "deepsea_pca/mean_sds.parquet",
+            "deepsea_pca.parquet",
+        ),
+        (
+            "deepsea_pca_small",
+            "deepseascores.parquet",
+            "{tmp_path}/pca.npy",
+            "{tmp_path}/mean_sds.parquet",
+            "deepsea_pca.parquet",
+        ),
+    ],
+)
+def test_deepsea_pca(
+    test_data_name_dir,
+    deapseascores_file,
+    pca_file,
+    mean_sds_file,
+    expected_out_df,
+    tmp_path,
+):
+    cli_runner = CliRunner()
+
+    current_test_data_dir = tests_data_dir / "deepsea_pca" / test_data_name_dir
+
+    deepsea_score_file = current_test_data_dir / "input" / deapseascores_file
+    pca_file = current_test_data_dir / pca_file.format(tmp_path=tmp_path)
+    mean_sds_file = current_test_data_dir / mean_sds_file.format(tmp_path=tmp_path)
+    expected_out_scores_file = current_test_data_dir / "expected" / expected_out_df
+
+    cli_parameters = [
+        "deepsea-pca",
+        deepsea_score_file.as_posix(),
+        pca_file.as_posix(),
+        mean_sds_file.as_posix(),
+        tmp_path.as_posix(),
+    ]
+
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+
+    written_results = pd.read_parquet(tmp_path / expected_out_df)
+
+    expected_scores = pd.read_parquet(expected_out_scores_file)
+    assert_frame_equal(written_results, expected_scores, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, expected, hg2_output, k5_output, parclip_output, variants, vcf, vep_output, vep_header_line",
+    [
+        (
+            "merge_annotations_small",
+            "merged_annotations_expected.parquet",
+            "test_hg2_deepripe.csv.gz",
+            "test_k5_deepripe.csv.gz",
+            "test_parclip.csv.gz",
+            "variants.parquet",
+            "test.vcf",
+            "test_vep.tsv",
+            "49",
+        ),
+        (
+            "merge_annotations_mixedIDs",
+            "merged_annotations_expected.parquet",
+            "test_hg2_deepripe.csv.gz",
+            "test_k5_deepripe.csv.gz",
+            "test_parclip.csv.gz",
+            "variants.parquet",
+            "test.vcf",
+            "test_vep.tsv",
+            "49",
+        ),
+    ],
+)
+def test_merge_annotations(
+    test_data_name_dir,
+    expected,
+    hg2_output,
+    k5_output,
+    parclip_output,
+    variants,
+    vcf,
+    vep_output,
+    vep_header_line,
+    tmp_path,
+):
+    current_test_data_dir = tests_data_dir / "merge_annotations" / test_data_name_dir
+    expected_path = current_test_data_dir / "expected" / expected
+    hg2_deepripe_path = current_test_data_dir / "input" / hg2_output
+    k5_deepripe_path = current_test_data_dir / "input" / k5_output
+    parclip_deepripe_path = current_test_data_dir / "input" / parclip_output
+    vcf_path = current_test_data_dir / "input" / vcf
+    vep_output_path = current_test_data_dir / "input" / vep_output
+    variants_path = current_test_data_dir / "input" / variants
+    output_path = tmp_path / "out_merged.parquet"
+    cli_runner = CliRunner()
+
+    cli_parameters = [
+        "merge-annotations",
+        vep_header_line,
+        vep_output_path.as_posix(),
+        parclip_deepripe_path.as_posix(),
+        hg2_deepripe_path.as_posix(),
+        k5_deepripe_path.as_posix(),
+        variants_path.as_posix(),
+        vcf_path.as_posix(),
+        output_path.as_posix(),
+    ]
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(
+        written_results[expected_results.columns], expected_results, check_exact=False
+    )
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, expected, merged_files",
+    [
+        (
+            "concatenate_annotations_small",
+            "expected.parquet",
+            "chr3test_merged.parquet,chr4test_merged.parquet",
+        ),
+    ],
+)
+def test_concatenate_annotations(test_data_name_dir, expected, merged_files, tmp_path):
+    current_test_data_dir = (
+        tests_data_dir / "concatenate_annotations" / test_data_name_dir
+    )
+    expected_path = current_test_data_dir / "expected" / expected
+    merged_files = [
+        (current_test_data_dir / "input" / file).as_posix()
+        for file in merged_files.split(",")
+    ]
+    output_path = tmp_path / "out.parquet"
+    cli_runner = CliRunner()
+
+    cli_parameters = [
+        "concat-annotations",
+        ",".join(merged_files),
+        output_path.as_posix(),
+    ]
+
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(
+        written_results[expected_results.columns], expected_results, check_exact=False
+    )
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, annotations, deepSEA_scores, annotation_columns_yaml_file, expected",
+    [
+        (
+            "merge_deepsea_pcas_small",
+            "vep_deepripe.parquet",
+            "all_variants.wID.deepSea.parquet",
+            "annotation_colnames_filling_values.yaml",
+            "expected.parquet",
+        ),
+    ],
+)
+def test_merge_deepsea_pcas(
+    test_data_name_dir,
+    annotations,
+    deepSEA_scores,
+    annotation_columns_yaml_file,
+    expected,
+    tmp_path,
+):
+    current_test_data_dir = tests_data_dir / "merge_deepsea_pcas" / test_data_name_dir
+    expected_path = current_test_data_dir / "expected" / expected
+    annotations_path = current_test_data_dir / "input" / annotations
+    deepSEA_scores_path = current_test_data_dir / "input" / deepSEA_scores
+    annotation_columns_yaml_path = (
+        current_test_data_dir / "input" / annotation_columns_yaml_file
+    )
+    output_path = tmp_path / "out.parquet"
+    cli_runner = CliRunner()
+    cli_parameters = [
+        "merge-deepsea-pcas",
+        annotations_path.as_posix(),
+        deepSEA_scores_path.as_posix(),
+        annotation_columns_yaml_path.as_posix(),
+        output_path.as_posix(),
+    ]
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(
+        written_results, expected_results[written_results.columns], check_exact=False
+    )
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, annotations, abSplice_score_dir, njobs, expected",
+    [
+        (
+            "aggregate_absplice_scores_small",
+            "vep_deepripe_deepsea.parquet",
+            "absplice_res_dir",
+            "8",
+            "abSplice_score_file.parquet",
+        ),
+    ],
+)
+def test_aggregate_abscores(
+    test_data_name_dir, annotations, abSplice_score_dir, njobs, expected, tmp_path
+):
+    current_test_data_dir = (
+        tests_data_dir / "aggregate_absplice_scores" / test_data_name_dir
+    )
+    annotations_path = current_test_data_dir / "input" / annotations
+    abscore_path = current_test_data_dir / "input" / abSplice_score_dir
+    expected_path = current_test_data_dir / "expected" / expected
+    output_path = tmp_path / "out.parquet"
+    cli_runner = CliRunner()
+    cli_parameters = [
+        "aggregate-abscores",
+        annotations_path.as_posix(),
+        abscore_path.as_posix(),
+        output_path.as_posix(),
+        njobs,
+    ]
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(
+        written_results, expected_results[written_results.columns], check_exact=False
+    )
+
+
+@pytest.mark.parametrize(
+    "test_name_dir, absplice_scores, annotations, expected",
+    [
+        (
+            "merge_absplice_scores_small",
+            "abSplice_score_file.parquet",
+            "vep_deepripe_deepsea.parquet",
+            "vep_deepripe_deepsea_absplice.parquet",
+        ),
+    ],
+)
+def test_merge_absplice_scores(
+    test_name_dir, absplice_scores, annotations, expected, tmp_path
+):
+    current_test_data_dir = tests_data_dir / "merge_absplice_scores" / test_name_dir
+    absplice_score_path = current_test_data_dir / "input" / absplice_scores
+    annotation_path = current_test_data_dir / "input" / annotations
+    expected_path = current_test_data_dir / "expected" / expected
+    output_path = tmp_path / "out.parquet"
+    cli_runner = CliRunner()
+    cli_parameters = [
+        "merge-abscores",
+        annotation_path.as_posix(),
+        absplice_score_path.as_posix(),
+        output_path.as_posix(),
+    ]
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(written_results, expected_results, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, genotype_file, variant_file, expected",
+    [
+        (
+            "calculate_allele_frequency_small",
+            "genotypes.h5",
+            "variants.parquet",
+            "af_df.parquet",
+        ),
+    ],
+)
+def test_calculate_allele_frequencies(
+    test_data_name_dir, genotype_file, variant_file, expected, tmp_path
+):
+    current_test_data_dir = (
+        tests_data_dir / "calculate_allele_frequency" / test_data_name_dir
+    )
+    genotype_filepath = current_test_data_dir / "input" / genotype_file
+    variant_filepath = current_test_data_dir / "input" / variant_file
+    expected_path = current_test_data_dir / "expected" / expected
+    output_path = tmp_path / "out.parquet"
+    cli_runner = CliRunner()
+    cli_parameters = [
+        "get-af-from-gt",
+        genotype_filepath.as_posix(),
+        variant_filepath.as_posix(),
+        output_path.as_posix(),
+    ]
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(written_results, expected_results, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, af_df, annotaton_df, expected",
+    [
+        (
+            "merge_af_small",
+            "af_df.parquet",
+            "vep_deepripe_deepsea_absplice.parquet",
+            "vep_deepripe_deepsea_absplice_af.parquet",
+        ),
+    ],
+)
+def test_merge_af(test_data_name_dir, af_df, annotaton_df, expected, tmp_path):
+    current_test_data_dir = tests_data_dir / "merge_af" / test_data_name_dir
+    af_path = current_test_data_dir / "input" / af_df
+    annotaions_path = current_test_data_dir / "input" / annotaton_df
+    expected_path = current_test_data_dir / "expected" / expected
+    output_path = tmp_path / "out.parquet"
+    cli_runner = CliRunner()
+    cli_parameters = [
+        "merge-af",
+        af_path.as_posix(),
+        annotaions_path.as_posix(),
+        output_path.as_posix(),
+    ]
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(
+        written_results, expected_results[written_results.columns], check_exact=False
+    )
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, annotations, expected",
+    [
+        (
+            "calculate_MAF_small",
+            "annotations.parquet",
+            "expected.parquet",
+        ),
+    ],
+)
+def test_calculate_maf(test_data_name_dir, annotations, expected, tmp_path):
+    current_test_data_dir = tests_data_dir / "calculate_MAF" / test_data_name_dir
+    annotations_path = current_test_data_dir / "input" / annotations
+    expected_path = current_test_data_dir / "expected" / expected
+    output_path = tmp_path / "out.parquet"
+    cli_runner = CliRunner()
+    cli_parameters = [
+        "calculate-maf",
+        annotations_path.as_posix(),
+        output_path.as_posix(),
+    ]
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(written_results, expected_results, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, gtf_file, expected",
+    [
+        (
+            "create_gene_id_file_small",
+            "gencode.v44.annotation.gtf.gz",
+            "protein_coding_genes.parquet",
+        ),
+    ],
+)
+def test_create_gene_id_file(test_data_name_dir, gtf_file, expected, tmp_path):
+    current_test_data_dir = tests_data_dir / "create_gene_id_file" / test_data_name_dir
+    input_path1 = current_test_data_dir / "input" / gtf_file
+    expected_path = current_test_data_dir / "expected" / expected
+    output_path = tmp_path / "out.parquet"
+    cli_runner = CliRunner()
+    cli_parameters = [
+        "create-gene-id-file",
+        input_path1.as_posix(),
+        output_path.as_posix(),
+    ]
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(
+        written_results, expected_results[written_results.columns], check_exact=False
+    )
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, annotations, gene_id_file , expected",
+    [
+        (
+            "add_gene_ids_small",
+            "annotations.parquet",
+            "protein_coding_genes.parquet",
+            "expected.parquet",
+        ),
+    ],
+)
+def test_add_gene_ids(
+    test_data_name_dir, annotations, gene_id_file, expected, tmp_path
+):
+    current_test_data_dir = tests_data_dir / "add_gene_ids" / test_data_name_dir
+    annotations_path = current_test_data_dir / "input" / annotations
+    gene_id_path = current_test_data_dir / "input" / gene_id_file
+    expected_path = current_test_data_dir / "expected" / expected
+    output_path = tmp_path / "out.parquet"
+    cli_runner = CliRunner()
+    cli_parameters = [
+        "add-gene-ids",
+        gene_id_path.as_posix(),
+        annotations_path.as_posix(),
+        output_path.as_posix(),
+    ]
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(written_results, expected_results, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, gtf_file, annotations, gene_id_file, expected",
+    [
+        (
+            "filter_by_exon_distance_small",
+            "gencode.v44.annotation.gtf.gz",
+            "annotations.parquet",
+            "protein_coding_genes.parquet",
+            "expected.parquet",
+        ),
+    ],
+)
+def test_filter_by_exon_distance(
+    test_data_name_dir, gtf_file, annotations, gene_id_file, expected, tmp_path
+):
+    current_test_data_dir = (
+        tests_data_dir / "filter_by_exon_distance" / test_data_name_dir
+    )
+    gtf_file_path = current_test_data_dir / "input" / gtf_file
+    annotations_path = current_test_data_dir / "input" / annotations
+    gene_id_path = current_test_data_dir / "input" / gene_id_file
+
+    expected_path = current_test_data_dir / "expected" / expected
+    output_path = tmp_path / "out.parquet"
+    cli_runner = CliRunner()
+    cli_parameters = [
+        "filter-annotations-by-exon-distance",
+        annotations_path.as_posix(),
+        gtf_file_path.as_posix(),
+        gene_id_path.as_posix(),
+        output_path.as_posix(),
+    ]
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(written_results, expected_results, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "test_data_name_dir, yaml_file, annotations, expected",
+    [
+        (
+            "select_rename_fill_columns_small",
+            "annotation_colnames_filling_values.yaml",
+            "annotations.parquet",
+            "expected.parquet",
+        ),
+    ],
+)
+def test_select_rename_fill_annotations(
+    test_data_name_dir, yaml_file, annotations, expected, tmp_path
+):
+    current_test_data_dir = (
+        tests_data_dir / "select_rename_fill_columns" / test_data_name_dir
+    )
+    yaml_file_path = current_test_data_dir / "input" / yaml_file
+    annotations_path = current_test_data_dir / "input" / annotations
+    expected_path = current_test_data_dir / "expected" / expected
+    output_path = tmp_path / "out.parquet"
+    cli_runner = CliRunner()
+    cli_parameters = [
+        "select-rename-fill-annotations",
+        yaml_file_path.as_posix(),
+        annotations_path.as_posix(),
+        output_path.as_posix(),
+    ]
+    result = cli_runner.invoke(annotations_cli, cli_parameters, catch_exceptions=False)
+    assert result.exit_code == 0
+    written_results = pd.read_parquet(output_path)
+    expected_results = pd.read_parquet(expected_path)
+    assert written_results.shape == expected_results.shape
+    assert_frame_equal(
+        written_results, expected_results[written_results.columns], check_exact=False
+    )
diff --git a/tests/annotations/test_data/add_gene_ids/add_gene_ids_small/expected/expected.parquet b/tests/annotations/test_data/add_gene_ids/add_gene_ids_small/expected/expected.parquet
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diff --git a/tests/annotations/test_data/add_ids_dask/add_ids_medium/input/variants.parquet b/tests/annotations/test_data/add_ids_dask/add_ids_medium/input/variants.parquet
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diff --git a/tests/annotations/test_data/add_ids_dask/add_ids_small/input/variants.parquet b/tests/annotations/test_data/add_ids_dask/add_ids_small/input/variants.parquet
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diff --git a/tests/annotations/test_data/aggregate_absplice_scores/aggregate_absplice_scores_small/expected/abSplice_score_file.parquet b/tests/annotations/test_data/aggregate_absplice_scores/aggregate_absplice_scores_small/expected/abSplice_score_file.parquet
new file mode 100644
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diff --git a/tests/annotations/test_data/aggregate_absplice_scores/aggregate_absplice_scores_small/input/absplice_res_dir/chr3test_variants_header.vcf.gz_AbSplice_DNA.csv b/tests/annotations/test_data/aggregate_absplice_scores/aggregate_absplice_scores_small/input/absplice_res_dir/chr3test_variants_header.vcf.gz_AbSplice_DNA.csv
new file mode 100644
index 00000000..60f5e03a
--- /dev/null
+++ b/tests/annotations/test_data/aggregate_absplice_scores/aggregate_absplice_scores_small/input/absplice_res_dir/chr3test_variants_header.vcf.gz_AbSplice_DNA.csv
@@ -0,0 +1,193 @@
+variant,gene_id,tissue,delta_logit_psi,delta_psi,delta_score,splice_site_is_expressed,AbSplice_DNA
+chr3:13336897:T>A,ENSG00000132182,Adrenal_Gland,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Pituitary,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Esophagus_Mucosa,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Kidney_Cortex,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Liver,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Lung,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Minor_Salivary_Gland,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Pancreas,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Prostate,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Cells_EBV_transformed_lymphocytes,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Small_Intestine_Terminal_Ileum,0.1271947632745491,0.0013409990441992,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Spleen,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Stomach,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Testis,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Thyroid,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Colon_Transverse,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Whole_Blood,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Brain_Cortex,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Brain_Nucleus_accumbens_basal_ganglia,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Brain_Hypothalamus,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Brain_Caudate_basal_ganglia,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Brain_Cerebellar_Hemisphere,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Brain_Cerebellum,-0.0987188615790356,-0.0010261192228098,0.0,1,0.002860864576028723
+chr3:13336897:T>A,ENSG00000132182,Breast_Mammary_Tissue,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Vagina,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Uterus,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Artery_Aorta,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Artery_Coronary,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Artery_Tibial,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Brain_Amygdala,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Brain_Anterior_cingulate_cortex_BA24,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Skin_Sun_Exposed_Lower_leg,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Skin_Not_Sun_Exposed_Suprapubic,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Ovary,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Brain_Substantia_nigra,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Nerve_Tibial,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Muscle_Skeletal,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Brain_Frontal_Cortex_BA9,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Brain_Hippocampus,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Heart_Left_Ventricle,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Heart_Atrial_Appendage,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Adipose_Visceral_Omentum,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Brain_Putamen_basal_ganglia,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Esophagus_Gastroesophageal_Junction,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Brain_Spinal_cord_cervical_c_1,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Colon_Sigmoid,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Esophagus_Muscularis,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Adipose_Subcutaneous,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Adipose_Visceral_Omentum,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Pituitary,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Heart_Left_Ventricle,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Kidney_Cortex,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Liver,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Lung,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Minor_Salivary_Gland,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Muscle_Skeletal,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Nerve_Tibial,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Ovary,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Pancreas,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Prostate,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Esophagus_Muscularis,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Skin_Not_Sun_Exposed_Suprapubic,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Skin_Sun_Exposed_Lower_leg,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Small_Intestine_Terminal_Ileum,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Spleen,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Stomach,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Testis,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Thyroid,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Uterus,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Vagina,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Heart_Atrial_Appendage,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Esophagus_Mucosa,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Adrenal_Gland,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Frontal_Cortex_BA9,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Artery_Aorta,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Artery_Coronary,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Artery_Tibial,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Amygdala,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Anterior_cingulate_cortex_BA24,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Caudate_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Cerebellar_Hemisphere,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Cerebellum,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Cortex,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Hippocampus,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Esophagus_Gastroesophageal_Junction,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Hypothalamus,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Nucleus_accumbens_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Putamen_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Spinal_cord_cervical_c_1,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Brain_Substantia_nigra,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Breast_Mammary_Tissue,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Cells_EBV_transformed_lymphocytes,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Colon_Sigmoid,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Colon_Transverse,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Adipose_Subcutaneous,,,0.0,0,0.0005700424787606313
+chr3:13336897:T>A,ENSG00000132182,Adipose_Subcutaneous,-0.0987188615790356,-0.0010261192228098,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Adipose_Visceral_Omentum,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Whole_Blood,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Heart_Atrial_Appendage,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Heart_Left_Ventricle,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Kidney_Cortex,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Liver,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Lung,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Minor_Salivary_Gland,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Muscle_Skeletal,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Nerve_Tibial,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Ovary,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Pancreas,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Pituitary,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Prostate,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Skin_Not_Sun_Exposed_Suprapubic,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Skin_Sun_Exposed_Lower_leg,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Small_Intestine_Terminal_Ileum,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Spleen,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Stomach,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Testis,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Thyroid,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Uterus,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Vagina,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Esophagus_Muscularis,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Esophagus_Mucosa,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Esophagus_Gastroesophageal_Junction,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Cortex,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Adrenal_Gland,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Artery_Aorta,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Artery_Coronary,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Artery_Tibial,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Amygdala,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Anterior_cingulate_cortex_BA24,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Caudate_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Cerebellar_Hemisphere,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Cerebellum,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Frontal_Cortex_BA9,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Colon_Transverse,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Hippocampus,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Hypothalamus,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Nucleus_accumbens_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Putamen_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Spinal_cord_cervical_c_1,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Brain_Substantia_nigra,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Breast_Mammary_Tissue,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Cells_EBV_transformed_lymphocytes,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Colon_Sigmoid,,,0.0,0,0.0005700424787606313
+chr3:10151779:G>T,ENSG00000134086,Whole_Blood,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Adipose_Subcutaneous,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Adipose_Visceral_Omentum,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Pituitary,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Heart_Left_Ventricle,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Kidney_Cortex,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Liver,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Lung,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Minor_Salivary_Gland,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Muscle_Skeletal,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Nerve_Tibial,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Ovary,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Pancreas,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Prostate,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Esophagus_Muscularis,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Skin_Not_Sun_Exposed_Suprapubic,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Skin_Sun_Exposed_Lower_leg,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Small_Intestine_Terminal_Ileum,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Spleen,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Stomach,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Testis,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Thyroid,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Uterus,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Vagina,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Heart_Atrial_Appendage,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Esophagus_Mucosa,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Adrenal_Gland,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Frontal_Cortex_BA9,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Artery_Aorta,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Artery_Coronary,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Artery_Tibial,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Amygdala,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Anterior_cingulate_cortex_BA24,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Caudate_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Cerebellar_Hemisphere,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Cerebellum,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Cortex,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Hippocampus,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Esophagus_Gastroesophageal_Junction,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Hypothalamus,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Nucleus_accumbens_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Putamen_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Spinal_cord_cervical_c_1,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Brain_Substantia_nigra,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Breast_Mammary_Tissue,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Cells_EBV_transformed_lymphocytes,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Colon_Sigmoid,,,0.0,0,0.0005700424787606313
+chr3:125013245:A>G,ENSG00000173706,Colon_Transverse,,,0.0,0,0.0005700424787606313
+chr3:139349025:T>C,ENSG00000175110,Whole_Blood,,,0.0,0,0.0005700424787606313
diff --git a/tests/annotations/test_data/aggregate_absplice_scores/aggregate_absplice_scores_small/input/absplice_res_dir/chr4test_variants_header.vcf.gz_AbSplice_DNA.csv b/tests/annotations/test_data/aggregate_absplice_scores/aggregate_absplice_scores_small/input/absplice_res_dir/chr4test_variants_header.vcf.gz_AbSplice_DNA.csv
new file mode 100644
index 00000000..db7d0c74
--- /dev/null
+++ b/tests/annotations/test_data/aggregate_absplice_scores/aggregate_absplice_scores_small/input/absplice_res_dir/chr4test_variants_header.vcf.gz_AbSplice_DNA.csv
@@ -0,0 +1,246 @@
+variant,gene_id,tissue,delta_logit_psi,delta_psi,delta_score,splice_site_is_expressed,AbSplice_DNA
+chr4:169007628:T>C,ENSG00000145439,Brain_Cerebellar_Hemisphere,-0.2924805955494345,-0.0588679816775568,0.05,1,0.006833637741834292
+chr4:169007628:T>C,ENSG00000145439,Brain_Cerebellum,-0.2924805955494345,-0.0573214450667628,0.05,1,0.006833637741834292
+chr4:169007628:T>C,ENSG00000145439,Brain_Substantia_nigra,-0.2924805955494345,-0.0566263142416471,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Brain_Nucleus_accumbens_basal_ganglia,-0.2924805955494345,-0.0542171459327623,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Brain_Hypothalamus,-0.2924805955494345,-0.0537109292025781,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Brain_Hippocampus,-0.2924805955494345,-0.0513790826672216,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Brain_Frontal_Cortex_BA9,-0.2924805955494345,-0.0539980667521867,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Brain_Cortex,-0.2924805955494345,-0.0520890438905559,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Brain_Putamen_basal_ganglia,-0.2924805955494345,-0.0551813984735419,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Brain_Caudate_basal_ganglia,-0.2924805955494345,-0.052206396676825,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Brain_Anterior_cingulate_cortex_BA24,-0.2924805955494345,-0.0537744175754923,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Brain_Amygdala,-0.2924805955494345,-0.0538417662920955,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Ovary,-0.2924805955494345,-0.0453503859773065,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Brain_Spinal_cord_cervical_c_1,-0.2924805955494345,-0.0510839631880544,0.05,1,0.006522249142751264
+chr4:169007628:T>C,ENSG00000145439,Pituitary,-0.2924805955494345,-0.0341719319284121,0.05,1,0.005140125524902324
+chr4:169007628:T>C,ENSG00000145439,Nerve_Tibial,-0.2924805955494345,-0.0339228709569385,0.05,1,0.004484302690380517
+chr4:169007628:T>C,ENSG00000145439,Thyroid,-0.2924805955494345,-0.0328589953455117,0.05,1,0.004480573733525163
+chr4:169007628:T>C,ENSG00000145439,Uterus,-0.2924805955494345,-0.0310821996594723,0.05,1,0.004480573733525163
+chr4:169007628:T>C,ENSG00000145439,Artery_Tibial,-0.2924805955494345,-0.0287037168558088,0.05,1,0.004480573733525163
+chr4:169007628:T>C,ENSG00000145439,Artery_Aorta,-0.2924805955494345,-0.0303005879830458,0.05,1,0.004480573733525163
+chr4:169007628:T>C,ENSG00000145439,Esophagus_Gastroesophageal_Junction,-0.2924805955494345,-0.0280732952512678,0.05,1,0.0043930876001134845
+chr4:169007628:T>C,ENSG00000145439,Kidney_Cortex,-0.2924805955494345,-0.0267086965806592,0.05,1,0.0043930876001134845
+chr4:169007628:T>C,ENSG00000145439,Artery_Coronary,-0.2924805955494345,-0.0273913899791004,0.05,1,0.0043930876001134845
+chr4:169007628:T>C,ENSG00000145439,Vagina,-0.2924805955494345,-0.0260689205351327,0.05,1,0.004273871261352944
+chr4:169007628:T>C,ENSG00000145439,Esophagus_Muscularis,-0.2924805955494345,-0.0255090745923955,0.05,1,0.0040471476529160035
+chr4:169007628:T>C,ENSG00000145439,Adipose_Subcutaneous,-0.2924805955494345,-0.0249133133651749,0.05,1,0.0040471476529160035
+chr4:169007628:T>C,ENSG00000145439,Breast_Mammary_Tissue,-0.2924805955494345,-0.0240961278969513,0.05,1,0.003882386621127518
+chr4:169007628:T>C,ENSG00000145439,Colon_Sigmoid,-0.2924805955494345,-0.024098663506736,0.05,1,0.003882386621127518
+chr4:169007628:T>C,ENSG00000145439,Prostate,-0.2924805955494345,-0.0236635694806509,0.05,1,0.003882386621127518
+chr4:169007628:T>C,ENSG00000145439,Lung,-0.2924805955494345,-0.0231295590665679,0.05,1,0.0035359001894868835
+chr4:169007628:T>C,ENSG00000145439,Adipose_Visceral_Omentum,-0.2924805955494345,-0.0229348909817636,0.05,1,0.0035359001894868835
+chr4:169007628:T>C,ENSG00000145439,Cells_EBV_transformed_lymphocytes,-0.2924805955494345,-0.0151715165568312,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Testis,-0.2924805955494345,-0.0181086649405058,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Heart_Atrial_Appendage,-0.2924805955494345,-0.0166600789232371,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Minor_Salivary_Gland,-0.2924805955494345,-0.0219233810154098,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Muscle_Skeletal,-0.2924805955494345,-0.0219827204341519,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Esophagus_Mucosa,-0.2924805955494345,-0.0160150577921673,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Pancreas,-0.2924805955494345,-0.0193831257243948,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Colon_Transverse,-0.2924805955494345,-0.0153900794275066,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Skin_Not_Sun_Exposed_Suprapubic,-0.2924805955494345,-0.019492169216431,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Skin_Sun_Exposed_Lower_leg,-0.2924805955494345,-0.0200246209787826,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Small_Intestine_Terminal_Ileum,-0.2924805955494345,-0.0164571845860465,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Spleen,-0.2924805955494345,-0.0185292383145769,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Stomach,-0.2924805955494345,-0.019371217400388,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Liver,-0.2924805955494345,-0.0151578033921386,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Adrenal_Gland,-0.2924805955494345,-0.0178600541748558,0.05,1,0.003530657395908191
+chr4:169007628:T>C,ENSG00000145439,Heart_Left_Ventricle,-0.2924805955494345,-0.0148995068950208,0.05,1,0.0030678102944556164
+chr4:169007628:T>C,ENSG00000145439,Cells_Cultured_fibroblasts,-0.2924805955494345,-0.0132909983842892,0.05,1,0.0030678102944556164
+chr4:86734352:C>T,ENSG00000163629,Skin_Sun_Exposed_Lower_leg,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Vagina,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Uterus,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Artery_Tibial,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Thyroid,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Testis,0.2628502134288226,0.0030646066609705,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Breast_Mammary_Tissue,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Esophagus_Mucosa,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Prostate,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Pituitary,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Ovary,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Nerve_Tibial,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Kidney_Cortex,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Cells_Cultured_fibroblasts,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Artery_Coronary,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Skin_Not_Sun_Exposed_Suprapubic,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Adipose_Visceral_Omentum,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:86734352:C>T,ENSG00000163629,Adipose_Subcutaneous,0.2628502134288226,0.0022936167070785,0.0,1,0.0028656508177969994
+chr4:122258831:A>T,ENSG00000138688,Adipose_Visceral_Omentum,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Artery_Tibial,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Brain_Frontal_Cortex_BA9,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Brain_Cortex,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Brain_Cerebellum,-0.0024281248594303,-3.241125210099068e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Brain_Cerebellar_Hemisphere,-0.0024281248594303,-2.7290232610815285e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Brain_Caudate_basal_ganglia,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Brain_Anterior_cingulate_cortex_BA24,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Brain_Amygdala,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Artery_Coronary,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Brain_Hippocampus,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Artery_Aorta,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Adipose_Visceral_Omentum,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Adipose_Subcutaneous,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Brain_Putamen_basal_ganglia,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Breast_Mammary_Tissue,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Cells_Cultured_fibroblasts,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Esophagus_Muscularis,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Lung,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Prostate,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:77035112:G>A,ENSG00000138758,Brain_Nucleus_accumbens_basal_ganglia,-0.0024281248594303,-2.400985783492307e-05,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Adipose_Subcutaneous,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Esophagus_Muscularis,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Brain_Cerebellum,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Cells_EBV_transformed_lymphocytes,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Vagina,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Uterus,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Colon_Sigmoid,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Thyroid,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Testis,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Breast_Mammary_Tissue,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Cells_Cultured_fibroblasts,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Small_Intestine_Terminal_Ileum,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Skin_Sun_Exposed_Lower_leg,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Brain_Cortex,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Skin_Not_Sun_Exposed_Suprapubic,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Esophagus_Gastroesophageal_Junction,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Pituitary,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Ovary,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Nerve_Tibial,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Minor_Salivary_Gland,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Lung,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Artery_Tibial,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Artery_Coronary,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Artery_Aorta,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:122258831:A>T,ENSG00000138688,Adrenal_Gland,-0.0812138713384427,-0.0008368600811415,0.0,1,0.002860864576028723
+chr4:86734352:C>T,ENSG00000163629,Brain_Cerebellum,0.2628502134288226,0.0022936167070785,0.0,0,0.0005709983575454501
+chr4:86734352:C>T,ENSG00000163629,Brain_Nucleus_accumbens_basal_ganglia,0.2628502134288226,0.0022936167070785,0.0,0,0.0005709983575454501
+chr4:86734352:C>T,ENSG00000163629,Brain_Hypothalamus,0.2628502134288226,0.0022936167070785,0.0,0,0.0005709983575454501
+chr4:86734352:C>T,ENSG00000163629,Brain_Hippocampus,0.2628502134288226,0.0022936167070785,0.0,0,0.0005709983575454501
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+chr4:77035112:G>A,ENSG00000138758,Skin_Not_Sun_Exposed_Suprapubic,,,0.0,0,0.0005700424787606313
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+chr4:77035112:G>A,ENSG00000138758,Uterus,,,0.0,0,0.0005700424787606313
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+chr4:77035112:G>A,ENSG00000138758,Vagina,,,0.0,0,0.0005700424787606313
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+chr4:77035112:G>A,ENSG00000138758,Liver,,,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Brain_Substantia_nigra,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Brain_Spinal_cord_cervical_c_1,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Brain_Putamen_basal_ganglia,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:86734352:C>T,ENSG00000163629,Liver,,,0.0,0,0.0005700424787606313
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+chr4:77035112:G>A,ENSG00000138758,Whole_Blood,,,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Brain_Hippocampus,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
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+chr4:122258831:A>T,ENSG00000138688,Brain_Cerebellar_Hemisphere,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:86734352:C>T,ENSG00000163629,Cells_EBV_transformed_lymphocytes,,,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Brain_Anterior_cingulate_cortex_BA24,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Thyroid,,,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Esophagus_Gastroesophageal_Junction,,,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Kidney_Cortex,-0.0024281248594303,-4.652301549626717e-05,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Heart_Left_Ventricle,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Heart_Left_Ventricle,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Heart_Atrial_Appendage,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Esophagus_Muscularis,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Esophagus_Mucosa,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Esophagus_Gastroesophageal_Junction,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Colon_Transverse,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Colon_Sigmoid,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Cells_EBV_transformed_lymphocytes,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Cells_Cultured_fibroblasts,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Breast_Mammary_Tissue,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Substantia_nigra,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Spinal_cord_cervical_c_1,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Putamen_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Nucleus_accumbens_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Hypothalamus,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Kidney_Cortex,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Liver,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Lung,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Skin_Sun_Exposed_Lower_leg,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Vagina,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Whole_Blood,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Testis,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Stomach,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Spleen,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Small_Intestine_Terminal_Ileum,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Skin_Not_Sun_Exposed_Suprapubic,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Minor_Salivary_Gland,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Prostate,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Pituitary,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Pancreas,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Ovary,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Nerve_Tibial,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Muscle_Skeletal,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Hippocampus,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Frontal_Cortex_BA9,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Cortex,,,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Heart_Left_Ventricle,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Muscle_Skeletal,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Brain_Hypothalamus,-0.0024281248594303,-2.400985783492307e-05,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Liver,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Brain_Spinal_cord_cervical_c_1,,,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Brain_Substantia_nigra,-0.0024281248594303,-3.182189889891322e-05,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Kidney_Cortex,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Cells_EBV_transformed_lymphocytes,-0.0024281248594303,-0.0002068757826844,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Colon_Sigmoid,,,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Colon_Transverse,-0.0024281248594303,-2.400985783492307e-05,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Uterus,,,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Esophagus_Mucosa,,,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Heart_Atrial_Appendage,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Heart_Atrial_Appendage,,,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Pancreas,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Prostate,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Cerebellum,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Artery_Aorta,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Cerebellar_Hemisphere,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Caudate_basal_ganglia,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Anterior_cingulate_cortex_BA24,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Brain_Amygdala,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Artery_Tibial,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Artery_Coronary,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Adrenal_Gland,,,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Spleen,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Adipose_Visceral_Omentum,,,0.0,0,0.0005700424787606313
+chr4:154609484:A>C,ENSG00000171557,Adipose_Subcutaneous,,,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Whole_Blood,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:77035112:G>A,ENSG00000138758,Adrenal_Gland,,,0.0,0,0.0005700424787606313
+chr4:122258831:A>T,ENSG00000138688,Stomach,-0.0812138713384427,-0.0008368600811415,0.0,0,0.0005700424787606313
+chr4:86734352:C>T,ENSG00000163629,Whole_Blood,,,0.0,0,0.0005700424787606313
+chr4:169007628:T>C,ENSG00000145439,Whole_Blood,-0.2924805955494345,-0.0136947033424804,0.05,0,0.0005001247511210176
diff --git a/tests/annotations/test_data/aggregate_absplice_scores/aggregate_absplice_scores_small/input/vep_deepripe_deepsea.parquet b/tests/annotations/test_data/aggregate_absplice_scores/aggregate_absplice_scores_small/input/vep_deepripe_deepsea.parquet
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index 00000000..675c2332
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index 00000000..33835b07
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index 00000000..2a0d5962
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diff --git a/tests/annotations/test_data/calculate_allele_frequency/calculate_allele_frequency_small/expected/af_df.parquet b/tests/annotations/test_data/calculate_allele_frequency/calculate_allele_frequency_small/expected/af_df.parquet
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index 00000000..d453427a
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diff --git a/tests/annotations/test_data/calculate_allele_frequency/calculate_allele_frequency_small/input/genotypes.h5 b/tests/annotations/test_data/calculate_allele_frequency/calculate_allele_frequency_small/input/genotypes.h5
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index 00000000..614d7a38
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diff --git a/tests/annotations/test_data/calculate_allele_frequency/calculate_allele_frequency_small/input/variants.parquet b/tests/annotations/test_data/calculate_allele_frequency/calculate_allele_frequency_small/input/variants.parquet
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index 00000000..37d317c4
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diff --git a/tests/annotations/test_data/concatenate_annotations/concatenate_annotations_small/expected/expected.parquet b/tests/annotations/test_data/concatenate_annotations/concatenate_annotations_small/expected/expected.parquet
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index 00000000..715ee750
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diff --git a/tests/annotations/test_data/concatenate_annotations/concatenate_annotations_small/input/chr3test_merged.parquet b/tests/annotations/test_data/concatenate_annotations/concatenate_annotations_small/input/chr3test_merged.parquet
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index 00000000..6f549b94
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diff --git a/tests/annotations/test_data/concatenate_annotations/concatenate_annotations_small/input/chr4test_merged.parquet b/tests/annotations/test_data/concatenate_annotations/concatenate_annotations_small/input/chr4test_merged.parquet
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diff --git a/tests/annotations/test_data/concatenate_deepsea/concatenate_deepseascores_medium/input/deepsea_scores_2.tsv b/tests/annotations/test_data/concatenate_deepsea/concatenate_deepseascores_medium/input/deepsea_scores_2.tsv
new file mode 100644
index 00000000..35131f36
--- /dev/null
+++ b/tests/annotations/test_data/concatenate_deepsea/concatenate_deepseascores_medium/input/deepsea_scores_2.tsv
@@ -0,0 +1,61 @@
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diff --git a/tests/annotations/test_data/concatenate_deepsea/concatenate_deepseascores_small/expected/expected.parquet b/tests/annotations/test_data/concatenate_deepsea/concatenate_deepseascores_small/expected/expected.parquet
new file mode 100644
index 00000000..56b44625
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diff --git a/tests/annotations/test_data/concatenate_deepsea/concatenate_deepseascores_small/input/deepsea_scores_1.tsv b/tests/annotations/test_data/concatenate_deepsea/concatenate_deepseascores_small/input/deepsea_scores_1.tsv
new file mode 100644
index 00000000..24a4a6d3
--- /dev/null
+++ b/tests/annotations/test_data/concatenate_deepsea/concatenate_deepseascores_small/input/deepsea_scores_1.tsv
@@ -0,0 +1,5 @@
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diff --git a/tests/annotations/test_data/concatenate_deepsea/concatenate_deepseascores_small/input/deepsea_scores_2.tsv b/tests/annotations/test_data/concatenate_deepsea/concatenate_deepseascores_small/input/deepsea_scores_2.tsv
new file mode 100644
index 00000000..3461df97
--- /dev/null
+++ b/tests/annotations/test_data/concatenate_deepsea/concatenate_deepseascores_small/input/deepsea_scores_2.tsv
@@ -0,0 +1,9 @@
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diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test.vcf b/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test.vcf
new file mode 100644
index 00000000..91b7a2e8
--- /dev/null
+++ b/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test.vcf
@@ -0,0 +1,60 @@
+chr3	3474106	.	C	T
+chr3	6134790	rs881	A	G
+chr3	6492413	.	A	G
+chr3	7479092	.	T	A
+chr3	10151779	.	G	T
+chr3	10963200	.	C	A
+chr3	13336897	rs178	T	A
+chr3	25565017	rs37	C	G
+chr3	28027872	rs721	T	C
+chr3	30429305	rs135	T	C
+chr3	39059372	rs23	A	C
+chr3	47378509	rs727	T	A
+chr3	47839379	rs268	C	T
+chr3	55062103	rs873	A	G
+chr3	56288165	rs664	G	C
+chr3	64813843	rs815	A	G
+chr3	70306576	rs107	C	G
+chr3	72140079	rs492	A	T
+chr3	72906610	rs930	T	G
+chr3	74562325	rs523	G	T
+chr3	78839934	rs583	G	A
+chr3	81414874	rs170	A	T
+chr3	97458263	rs548	A	T
+chr3	97649369	rs546	C	G
+chr3	97949211	rs543	G	A
+chr3	99075824	rs838	T	C
+chr3	101580812	rs311	A	C
+chr3	103151123	rs382	C	A
+chr3	103329532	rs179	T	C
+chr3	103928516	rs19	A	T
+chr3	105180981	rs341	A	G
+chr3	111113126	rs470	A	G
+chr3	111866541	rs467	T	A
+chr3	117455785	rs718	C	A
+chr3	120258434	rs506	A	C
+chr3	120364684	rs367	T	G
+chr3	122803142	rs488	A	C
+chr3	125013245	rs146	A	G
+chr3	127342540	rs318	G	T
+chr3	133734681	rs104	G	A
+chr3	139349025	rs665	T	C
+chr3	140275153	rs791	G	C
+chr3	145304395	rs102	C	G
+chr3	147901161	rs274	C	T
+chr3	150051584	rs123	C	A
+chr3	150399452	rs648	T	A
+chr3	158349305	rs748	T	A
+chr3	158851780	rs408	A	T
+chr3	160382108	rs963	A	C
+chr3	168465216	rs751	G	C
+chr3	171089322	rs197	A	T
+chr3	177499702	rs376	G	C
+chr3	185836100	rs581	G	T
+chr3	191856146	rs596	G	C
+chr3	192824921	rs701	C	G
+chr3	193390684	rs434	A	C
+chr3	194577309	rs70	T	G
+chr3	194762766	rs598	G	A
+chr3	197345633	rs356	T	A
+chr3	197732094	rs693	G	T
diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test_hg2_deepripe.csv.gz b/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test_hg2_deepripe.csv.gz
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diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test_k5_deepripe.csv.gz b/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test_k5_deepripe.csv.gz
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diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test_parclip.csv.gz b/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test_parclip.csv.gz
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index 00000000..271142ab
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diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test_vep.tsv b/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test_vep.tsv
new file mode 100644
index 00000000..d55de3f0
--- /dev/null
+++ b/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/test_vep.tsv
@@ -0,0 +1,122 @@
+## ENSEMBL VARIANT EFFECT PREDICTOR v110.1
+## Output produced at 2024-04-24 15:05:38
+## Using cache in repo_dir/ensembl-vep/cache/homo_sapiens/110_GRCh38
+## Using API version 110, DB version ?
+## ensembl-variation version 110.d34d25e
+## ensembl-io version 110.b1a0d57
+## ensembl version 110.584a8f3
+## ensembl-funcgen version 110.24e6da6
+## dbSNP version 154
+## 1000genomes version phase3
+## gencode version GENCODE 44
+## polyphen version 2.2.3
+## sift version 6.2.1
+## gnomADe version r2.1.1
+## ClinVar version 202301
+## HGMD-PUBLIC version 20204
+## assembly version GRCh38.p14
+## COSMIC version 97
+## regbuild version 1.0
+## gnomADg version v3.1.2
+## genebuild version 2014-07
+## Column descriptions:
+## Uploaded_variation : Identifier of uploaded variant
+## Location : Location of variant in standard coordinate format (chr:start or chr:start-end)
+## Allele : The variant allele used to calculate the consequence
+## Gene : Stable ID of affected gene
+## Feature : Stable ID of feature
+## Feature_type : Type of feature - Transcript, RegulatoryFeature or MotifFeature
+## Consequence : Consequence type
+## cDNA_position : Relative position of base pair in cDNA sequence
+## CDS_position : Relative position of base pair in coding sequence
+## Protein_position : Relative position of amino acid in protein
+## Amino_acids : Reference and variant amino acids
+## Codons : Reference and variant codon sequence
+## Existing_variation : Identifier(s) of co-located known variants
+## IMPACT : Subjective impact classification of consequence type
+## DISTANCE : Shortest distance from variant to transcript
+## STRAND : Strand of the feature (1/-1)
+## FLAGS : Transcript quality flags
+## BIOTYPE : Biotype of transcript or regulatory feature
+## CANONICAL : Indicates if transcript is canonical for this gene
+## ENSP : Protein identifer
+## SIFT : SIFT prediction and/or score
+## PolyPhen : PolyPhen prediction and/or score
+## AF : Frequency of existing variant in 1000 Genomes combined population
+## CLIN_SIG : ClinVar clinical significance of the dbSNP variant
+## SOMATIC : Somatic status of existing variant
+## PHENO : Indicates if existing variant(s) is associated with a phenotype, disease or trait; multiple values correspond to multiple variants
+## VEP command-line: vep --af --assembly GRCh38 --biotype --cache --canonical --database 0 --dir_cache [PATH]/cache --dir_plugins [PATH]/Plugins --fasta [PATH]/GRCh38.primary_assembly.genome.fa --force_overwrite --fork 5 --format vcf --input_file [PATH]/chr3test_stripped.vcf.gz --no_escape --no_stats --offline --output_file [PATH]/chr3test_vep_anno.tsv --per_gene --pick_order biotype,mane_select,mane_plus_clinical,canonical,appris,tsl,ccds,rank,length,ensembl,refseq --polyphen s --protein --sift s --tab --total_length
+#Uploaded_variation	Location	Allele	Gene	Feature	Feature_type	Consequence	cDNA_position	CDS_position	Protein_position	Amino_acids	Codons	Existing_variation	IMPACT	DISTANCE	STRAND	FLAGS	BIOTYPE	CANONICAL	ENSP	SIFT	PolyPhen	AF	CLIN_SIG	SOMATIC	PHENO
+3_3474106_C/T	3:3474106	T	ENSG00000223727	ENST00000420000	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs881	3:6134790	G	-	-	-	intergenic_variant	-	-	-	-	-	rs1007430246	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+3_6492413_A/G	3:6492413	G	ENSG00000189229	ENST00000655754	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+3_7479092_T/A	3:7479092	A	ENSG00000196277	ENST00000357716	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000350348	-	-	-	-	-	-
+3_10151779_G/T	3:10151779	T	ENSG00000287086	ENST00000660063	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	COSV56556349	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	1	1
+3_10151779_G/T	3:10151779	T	ENSG00000134086	ENST00000256474	Transcript	3_prime_UTR_variant	2526/4414	-	-	-	-	COSV56556349	MODIFIER	-	1	-	protein_coding	YES	ENSP00000256474	-	-	-	-	1	1
+3_10963200_C/A	3:10963200	A	ENSG00000286962	ENST00000656787	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs178	3:13336897	A	ENSG00000132182	ENST00000254508	Transcript	missense_variant	3670/7206	3574/5664	1192/1887	I/F	Atc/Ttc	-	MODERATE	-	-1	-	protein_coding	YES	ENSP00000254508	0.01	0.712	-	-	-	-
+rs37	3:25565017	G	ENSG00000077092	ENST00000330688	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000332296	-	-	-	-	-	-
+rs721	3:28027872	C	ENSG00000235493	ENST00000356047	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	-	-	-	-	-	-	-	-
+rs135	3:30429305	C	ENSG00000289450	ENST00000691186	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs23	3:39059372	C	ENSG00000114742	ENST00000302313	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000307491	-	-	-	-	-	-
+rs727	3:47378509	A	ENSG00000260236	ENST00000568593	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	580	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs727	3:47378509	A	ENSG00000076201	ENST00000265562	Transcript	upstream_gene_variant	-	-	-	-	-	-	MODIFIER	2512	1	-	protein_coding	YES	ENSP00000265562	-	-	-	-	-	-
+rs268	3:47839379	T	ENSG00000132153	ENST00000445061	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000405620	-	-	-	-	-	-
+rs873	3:55062103	G	ENSG00000157445	ENST00000474759	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000419101	-	-	-	-	-	-
+rs664	3:56288165	C	ENSG00000187672	ENST00000288221	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000288221	-	-	-	-	-	-
+rs815	3:64813843	G	ENSG00000241684	ENST00000650103	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs107	3:70306576	G	ENSG00000240405	ENST00000642114	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	rs536908099	MODIFIER	-	1	-	lncRNA	YES	-	-	-	0.0002	-	-	-
+rs107	3:70306576	G	ENSG00000242120	ENST00000567252	Transcript	intron_variant	-	-	-	-	-	rs536908099	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000490638	-	-	0.0002	-	-	-
+rs492	3:72140079	T	ENSG00000241163	ENST00000626474	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs930	3:72906610	G	ENSG00000172986	ENST00000389617	Transcript	intron_variant	-	-	-	-	-	COSV67474846	MODIFIER	-	1	-	protein_coding	YES	ENSP00000374268	-	-	-	-	1	1
+rs523	3:74562325	T	ENSG00000113805	ENST00000263665	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000263665	-	-	-	-	-	-
+rs583	3:78839934	A	ENSG00000169855	ENST00000464233	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000420321	-	-	-	-	-	-
+rs170	3:81414874	T	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs548	3:97458263	T	ENSG00000080224	ENST00000389672	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000374323	-	-	-	-	-	-
+rs546	3:97649369	G	ENSG00000080224	ENST00000389672	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000374323	-	-	-	-	-	-
+rs543	3:97949211	A	ENSG00000080200	ENST00000389622	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	4227	1	-	protein_coding	YES	ENSP00000374273	-	-	-	-	-	-
+rs543	3:97949211	A	ENSG00000170854	ENST00000394198	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000377748	-	-	-	-	-	-
+rs838	3:99075824	C	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs311	3:101580812	C	ENSG00000081154	ENST00000265260	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000265260	-	-	-	-	-	-
+rs311	3:101580812	C	ENSG00000242299	ENST00000496294	Transcript	upstream_gene_variant	-	-	-	-	-	-	MODIFIER	3865	-1	-	processed_pseudogene	YES	-	-	-	-	-	-	-
+rs382	3:103151123	A	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs179	3:103329532	C	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs19	3:103928516	T	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs341	3:105180981	G	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs470	3:111113126	G	ENSG00000177707	ENST00000485303	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000418070	-	-	-	-	-	-
+rs467	3:111866541	A	ENSG00000144824	ENST00000431670	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000405405	-	-	-	-	-	-
+rs718	3:117455785	A	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs506	3:120258434	C	ENSG00000175697	ENST00000464295	Transcript	intron_variant	-	-	-	-	-	rs1470110063	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000417261	-	-	-	-	-	-
+rs367	3:120364684	G	ENSG00000240661	ENST00000634410	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	transcribed_unitary_pseudogene	YES	-	-	-	-	-	-	-
+rs367	3:120364684	G	ENSG00000282950	ENST00000634744	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	1309	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs488	3:122803142	C	ENSG00000138463	ENST00000261038	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000261038	-	-	-	-	-	-
+rs146	3:125013245	G	ENSG00000173706	ENST00000311127	Transcript	synonymous_variant	2441/9195	2334/4146	778/1381	L	ctT/ctC	-	LOW	-	-1	-	protein_coding	YES	ENSP00000311502	-	-	-	-	-	-
+rs318	3:127342540	T	ENSG00000244215	ENST00000488425	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs104	3:133734681	A	ENSG00000291042	ENST00000460564	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs665	3:139349025	C	ENSG00000272656	ENST00000608472	Transcript	non_coding_transcript_exon_variant	347/348	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs665	3:139349025	C	ENSG00000184432	ENST00000503326	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	4921	-1	-	protein_coding	-	ENSP00000426682	-	-	-	-	-	-
+rs665	3:139349025	C	ENSG00000175110	ENST00000680020	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000505414	-	-	-	-	-	-
+rs791	3:140275153	C	ENSG00000158258	ENST00000458420	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000402460	-	-	-	-	-	-
+rs102	3:145304395	G	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs274	3:147901161	T	-	-	-	intergenic_variant	-	-	-	-	-	rs1305018800	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs123	3:150051584	A	ENSG00000243944	ENST00000487840	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs123	3:150051584	A	ENSG00000070087	ENST00000497148	Transcript	upstream_gene_variant	-	-	-	-	-	-	MODIFIER	796	-1	-	protein_coding	-	ENSP00000417817	-	-	-	-	-	-
+rs123	3:150051584	A	ENSG00000240477	ENST00000466044	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	128	1	-	processed_pseudogene	YES	-	-	-	-	-	-	-
+rs648	3:150399452	A	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs748	3:158349305	A	ENSG00000174891	ENST00000611884	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000481697	-	-	-	-	-	-
+rs408	3:158851780	T	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs963	3:160382108	C	ENSG00000068885	ENST00000326448	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000312778	-	-	-	-	-	-
+rs751	3:168465216	C	ENSG00000206120	ENST00000431685	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	transcribed_unitary_pseudogene	YES	-	-	-	-	-	-	-
+rs197	3:171089322	T	ENSG00000154310	ENST00000436636	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000399511	-	-	-	-	-	-
+rs376	3:177499702	C	ENSG00000252028	ENST00000516219	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	3601	-1	-	misc_RNA	YES	-	-	-	-	-	-	-
+rs376	3:177499702	C	ENSG00000228221	ENST00000656037	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs581	3:185836100	T	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs596	3:191856146	C	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs701	3:192824921	G	ENSG00000180611	ENST00000392452	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000376246	-	-	-	-	-	-
+rs434	3:193390684	C	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs70	3:194577309	G	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs598	3:194762766	A	ENSG00000237222	ENST00000667646	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs598	3:194762766	A	ENSG00000230401	ENST00000422271	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	2472	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs356	3:197345633	A	ENSG00000286870	ENST00000669801	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	4927	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs693	3:197732094	T	ENSG00000145016	ENST00000296343	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000296343	-	-	-	-	-	-
diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/variants.parquet b/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/variants.parquet
new file mode 100644
index 00000000..35d9aaf3
Binary files /dev/null and b/tests/annotations/test_data/merge_annotations/merge_annotations_mixedIDs/input/variants.parquet differ
diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_small/expected/merged_annotations_expected.parquet b/tests/annotations/test_data/merge_annotations/merge_annotations_small/expected/merged_annotations_expected.parquet
new file mode 100644
index 00000000..6ea8c2d8
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diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test.vcf b/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test.vcf
new file mode 100644
index 00000000..357a3a89
--- /dev/null
+++ b/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test.vcf
@@ -0,0 +1,60 @@
+chr3	3474106	rs111	C	T
+chr3	6134790	rs881	A	G
+chr3	6492413	rs94	A	G
+chr3	7479092	rs200	T	A
+chr3	10151779	rs337	G	T
+chr3	10963200	rs632	C	A
+chr3	13336897	rs178	T	A
+chr3	25565017	rs37	C	G
+chr3	28027872	rs721	T	C
+chr3	30429305	rs135	T	C
+chr3	39059372	rs23	A	C
+chr3	47378509	rs727	T	A
+chr3	47839379	rs268	C	T
+chr3	55062103	rs873	A	G
+chr3	56288165	rs664	G	C
+chr3	64813843	rs815	A	G
+chr3	70306576	rs107	C	G
+chr3	72140079	rs492	A	T
+chr3	72906610	rs930	T	G
+chr3	74562325	rs523	G	T
+chr3	78839934	rs583	G	A
+chr3	81414874	rs170	A	T
+chr3	97458263	rs548	A	T
+chr3	97649369	rs546	C	G
+chr3	97949211	rs543	G	A
+chr3	99075824	rs838	T	C
+chr3	101580812	rs311	A	C
+chr3	103151123	rs382	C	A
+chr3	103329532	rs179	T	C
+chr3	103928516	rs19	A	T
+chr3	105180981	rs341	A	G
+chr3	111113126	rs470	A	G
+chr3	111866541	rs467	T	A
+chr3	117455785	rs718	C	A
+chr3	120258434	rs506	A	C
+chr3	120364684	rs367	T	G
+chr3	122803142	rs488	A	C
+chr3	125013245	rs146	A	G
+chr3	127342540	rs318	G	T
+chr3	133734681	rs104	G	A
+chr3	139349025	rs665	T	C
+chr3	140275153	rs791	G	C
+chr3	145304395	rs102	C	G
+chr3	147901161	rs274	C	T
+chr3	150051584	rs123	C	A
+chr3	150399452	rs648	T	A
+chr3	158349305	rs748	T	A
+chr3	158851780	rs408	A	T
+chr3	160382108	rs963	A	C
+chr3	168465216	rs751	G	C
+chr3	171089322	rs197	A	T
+chr3	177499702	rs376	G	C
+chr3	185836100	rs581	G	T
+chr3	191856146	rs596	G	C
+chr3	192824921	rs701	C	G
+chr3	193390684	rs434	A	C
+chr3	194577309	rs70	T	G
+chr3	194762766	rs598	G	A
+chr3	197345633	rs356	T	A
+chr3	197732094	rs693	G	T
diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test_hg2_deepripe.csv.gz b/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test_hg2_deepripe.csv.gz
new file mode 100644
index 00000000..69a44862
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diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test_k5_deepripe.csv.gz b/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test_k5_deepripe.csv.gz
new file mode 100644
index 00000000..cc970e2a
Binary files /dev/null and b/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test_k5_deepripe.csv.gz differ
diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test_parclip.csv.gz b/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test_parclip.csv.gz
new file mode 100644
index 00000000..271142ab
Binary files /dev/null and b/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test_parclip.csv.gz differ
diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test_vep.tsv b/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test_vep.tsv
new file mode 100644
index 00000000..ef9eeab0
--- /dev/null
+++ b/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/test_vep.tsv
@@ -0,0 +1,122 @@
+## ENSEMBL VARIANT EFFECT PREDICTOR v110.1
+## Output produced at 2024-04-24 15:05:38
+## Using cache in repo_dir/ensembl-vep/cache/homo_sapiens/110_GRCh38
+## Using API version 110, DB version ?
+## ensembl-variation version 110.d34d25e
+## ensembl-io version 110.b1a0d57
+## ensembl version 110.584a8f3
+## ensembl-funcgen version 110.24e6da6
+## dbSNP version 154
+## 1000genomes version phase3
+## gencode version GENCODE 44
+## polyphen version 2.2.3
+## sift version 6.2.1
+## gnomADe version r2.1.1
+## ClinVar version 202301
+## HGMD-PUBLIC version 20204
+## assembly version GRCh38.p14
+## COSMIC version 97
+## regbuild version 1.0
+## gnomADg version v3.1.2
+## genebuild version 2014-07
+## Column descriptions:
+## Uploaded_variation : Identifier of uploaded variant
+## Location : Location of variant in standard coordinate format (chr:start or chr:start-end)
+## Allele : The variant allele used to calculate the consequence
+## Gene : Stable ID of affected gene
+## Feature : Stable ID of feature
+## Feature_type : Type of feature - Transcript, RegulatoryFeature or MotifFeature
+## Consequence : Consequence type
+## cDNA_position : Relative position of base pair in cDNA sequence
+## CDS_position : Relative position of base pair in coding sequence
+## Protein_position : Relative position of amino acid in protein
+## Amino_acids : Reference and variant amino acids
+## Codons : Reference and variant codon sequence
+## Existing_variation : Identifier(s) of co-located known variants
+## IMPACT : Subjective impact classification of consequence type
+## DISTANCE : Shortest distance from variant to transcript
+## STRAND : Strand of the feature (1/-1)
+## FLAGS : Transcript quality flags
+## BIOTYPE : Biotype of transcript or regulatory feature
+## CANONICAL : Indicates if transcript is canonical for this gene
+## ENSP : Protein identifer
+## SIFT : SIFT prediction and/or score
+## PolyPhen : PolyPhen prediction and/or score
+## AF : Frequency of existing variant in 1000 Genomes combined population
+## CLIN_SIG : ClinVar clinical significance of the dbSNP variant
+## SOMATIC : Somatic status of existing variant
+## PHENO : Indicates if existing variant(s) is associated with a phenotype, disease or trait; multiple values correspond to multiple variants
+## VEP command-line: vep --af --assembly GRCh38 --biotype --cache --canonical --database 0 --dir_cache [PATH]/cache --dir_plugins [PATH]/Plugins --fasta [PATH]/GRCh38.primary_assembly.genome.fa --force_overwrite --fork 5 --format vcf --input_file [PATH]/chr3test_stripped.vcf.gz --no_escape --no_stats --offline --output_file [PATH]/chr3test_vep_anno.tsv --per_gene --pick_order biotype,mane_select,mane_plus_clinical,canonical,appris,tsl,ccds,rank,length,ensembl,refseq --polyphen s --protein --sift s --tab --total_length
+#Uploaded_variation	Location	Allele	Gene	Feature	Feature_type	Consequence	cDNA_position	CDS_position	Protein_position	Amino_acids	Codons	Existing_variation	IMPACT	DISTANCE	STRAND	FLAGS	BIOTYPE	CANONICAL	ENSP	SIFT	PolyPhen	AF	CLIN_SIG	SOMATIC	PHENO
+rs111	3:3474106	T	ENSG00000223727	ENST00000420000	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs881	3:6134790	G	-	-	-	intergenic_variant	-	-	-	-	-	rs1007430246	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs94	3:6492413	G	ENSG00000189229	ENST00000655754	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs200	3:7479092	A	ENSG00000196277	ENST00000357716	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000350348	-	-	-	-	-	-
+rs337	3:10151779	T	ENSG00000287086	ENST00000660063	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	COSV56556349	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	1	1
+rs337	3:10151779	T	ENSG00000134086	ENST00000256474	Transcript	3_prime_UTR_variant	2526/4414	-	-	-	-	COSV56556349	MODIFIER	-	1	-	protein_coding	YES	ENSP00000256474	-	-	-	-	1	1
+rs632	3:10963200	A	ENSG00000286962	ENST00000656787	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs178	3:13336897	A	ENSG00000132182	ENST00000254508	Transcript	missense_variant	3670/7206	3574/5664	1192/1887	I/F	Atc/Ttc	-	MODERATE	-	-1	-	protein_coding	YES	ENSP00000254508	0.01	0.712	-	-	-	-
+rs37	3:25565017	G	ENSG00000077092	ENST00000330688	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000332296	-	-	-	-	-	-
+rs721	3:28027872	C	ENSG00000235493	ENST00000356047	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	-	-	-	-	-	-	-	-
+rs135	3:30429305	C	ENSG00000289450	ENST00000691186	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs23	3:39059372	C	ENSG00000114742	ENST00000302313	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000307491	-	-	-	-	-	-
+rs727	3:47378509	A	ENSG00000260236	ENST00000568593	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	580	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs727	3:47378509	A	ENSG00000076201	ENST00000265562	Transcript	upstream_gene_variant	-	-	-	-	-	-	MODIFIER	2512	1	-	protein_coding	YES	ENSP00000265562	-	-	-	-	-	-
+rs268	3:47839379	T	ENSG00000132153	ENST00000445061	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000405620	-	-	-	-	-	-
+rs873	3:55062103	G	ENSG00000157445	ENST00000474759	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000419101	-	-	-	-	-	-
+rs664	3:56288165	C	ENSG00000187672	ENST00000288221	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000288221	-	-	-	-	-	-
+rs815	3:64813843	G	ENSG00000241684	ENST00000650103	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs107	3:70306576	G	ENSG00000240405	ENST00000642114	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	rs536908099	MODIFIER	-	1	-	lncRNA	YES	-	-	-	0.0002	-	-	-
+rs107	3:70306576	G	ENSG00000242120	ENST00000567252	Transcript	intron_variant	-	-	-	-	-	rs536908099	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000490638	-	-	0.0002	-	-	-
+rs492	3:72140079	T	ENSG00000241163	ENST00000626474	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs930	3:72906610	G	ENSG00000172986	ENST00000389617	Transcript	intron_variant	-	-	-	-	-	COSV67474846	MODIFIER	-	1	-	protein_coding	YES	ENSP00000374268	-	-	-	-	1	1
+rs523	3:74562325	T	ENSG00000113805	ENST00000263665	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000263665	-	-	-	-	-	-
+rs583	3:78839934	A	ENSG00000169855	ENST00000464233	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000420321	-	-	-	-	-	-
+rs170	3:81414874	T	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs548	3:97458263	T	ENSG00000080224	ENST00000389672	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000374323	-	-	-	-	-	-
+rs546	3:97649369	G	ENSG00000080224	ENST00000389672	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000374323	-	-	-	-	-	-
+rs543	3:97949211	A	ENSG00000080200	ENST00000389622	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	4227	1	-	protein_coding	YES	ENSP00000374273	-	-	-	-	-	-
+rs543	3:97949211	A	ENSG00000170854	ENST00000394198	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000377748	-	-	-	-	-	-
+rs838	3:99075824	C	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs311	3:101580812	C	ENSG00000081154	ENST00000265260	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000265260	-	-	-	-	-	-
+rs311	3:101580812	C	ENSG00000242299	ENST00000496294	Transcript	upstream_gene_variant	-	-	-	-	-	-	MODIFIER	3865	-1	-	processed_pseudogene	YES	-	-	-	-	-	-	-
+rs382	3:103151123	A	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs179	3:103329532	C	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs19	3:103928516	T	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs341	3:105180981	G	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs470	3:111113126	G	ENSG00000177707	ENST00000485303	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000418070	-	-	-	-	-	-
+rs467	3:111866541	A	ENSG00000144824	ENST00000431670	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000405405	-	-	-	-	-	-
+rs718	3:117455785	A	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs506	3:120258434	C	ENSG00000175697	ENST00000464295	Transcript	intron_variant	-	-	-	-	-	rs1470110063	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000417261	-	-	-	-	-	-
+rs367	3:120364684	G	ENSG00000240661	ENST00000634410	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	transcribed_unitary_pseudogene	YES	-	-	-	-	-	-	-
+rs367	3:120364684	G	ENSG00000282950	ENST00000634744	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	1309	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs488	3:122803142	C	ENSG00000138463	ENST00000261038	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000261038	-	-	-	-	-	-
+rs146	3:125013245	G	ENSG00000173706	ENST00000311127	Transcript	synonymous_variant	2441/9195	2334/4146	778/1381	L	ctT/ctC	-	LOW	-	-1	-	protein_coding	YES	ENSP00000311502	-	-	-	-	-	-
+rs318	3:127342540	T	ENSG00000244215	ENST00000488425	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs104	3:133734681	A	ENSG00000291042	ENST00000460564	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs665	3:139349025	C	ENSG00000272656	ENST00000608472	Transcript	non_coding_transcript_exon_variant	347/348	-	-	-	-	-	MODIFIER	-	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs665	3:139349025	C	ENSG00000184432	ENST00000503326	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	4921	-1	-	protein_coding	-	ENSP00000426682	-	-	-	-	-	-
+rs665	3:139349025	C	ENSG00000175110	ENST00000680020	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000505414	-	-	-	-	-	-
+rs791	3:140275153	C	ENSG00000158258	ENST00000458420	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000402460	-	-	-	-	-	-
+rs102	3:145304395	G	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs274	3:147901161	T	-	-	-	intergenic_variant	-	-	-	-	-	rs1305018800	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs123	3:150051584	A	ENSG00000243944	ENST00000487840	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs123	3:150051584	A	ENSG00000070087	ENST00000497148	Transcript	upstream_gene_variant	-	-	-	-	-	-	MODIFIER	796	-1	-	protein_coding	-	ENSP00000417817	-	-	-	-	-	-
+rs123	3:150051584	A	ENSG00000240477	ENST00000466044	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	128	1	-	processed_pseudogene	YES	-	-	-	-	-	-	-
+rs648	3:150399452	A	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs748	3:158349305	A	ENSG00000174891	ENST00000611884	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	1	-	protein_coding	YES	ENSP00000481697	-	-	-	-	-	-
+rs408	3:158851780	T	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs963	3:160382108	C	ENSG00000068885	ENST00000326448	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000312778	-	-	-	-	-	-
+rs751	3:168465216	C	ENSG00000206120	ENST00000431685	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	transcribed_unitary_pseudogene	YES	-	-	-	-	-	-	-
+rs197	3:171089322	T	ENSG00000154310	ENST00000436636	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000399511	-	-	-	-	-	-
+rs376	3:177499702	C	ENSG00000252028	ENST00000516219	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	3601	-1	-	misc_RNA	YES	-	-	-	-	-	-	-
+rs376	3:177499702	C	ENSG00000228221	ENST00000656037	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs581	3:185836100	T	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs596	3:191856146	C	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs701	3:192824921	G	ENSG00000180611	ENST00000392452	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000376246	-	-	-	-	-	-
+rs434	3:193390684	C	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs70	3:194577309	G	-	-	-	intergenic_variant	-	-	-	-	-	-	MODIFIER	-	-	-	-	-	-	-	-	-	-	-	-
+rs598	3:194762766	A	ENSG00000237222	ENST00000667646	Transcript	intron_variant,non_coding_transcript_variant	-	-	-	-	-	-	MODIFIER	-	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs598	3:194762766	A	ENSG00000230401	ENST00000422271	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	2472	-1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs356	3:197345633	A	ENSG00000286870	ENST00000669801	Transcript	downstream_gene_variant	-	-	-	-	-	-	MODIFIER	4927	1	-	lncRNA	YES	-	-	-	-	-	-	-
+rs693	3:197732094	T	ENSG00000145016	ENST00000296343	Transcript	intron_variant	-	-	-	-	-	-	MODIFIER	-	-1	-	protein_coding	YES	ENSP00000296343	-	-	-	-	-	-
diff --git a/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/variants.parquet b/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/variants.parquet
new file mode 100644
index 00000000..35d9aaf3
Binary files /dev/null and b/tests/annotations/test_data/merge_annotations/merge_annotations_small/input/variants.parquet differ
diff --git a/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/expected/expected.parquet b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/expected/expected.parquet
new file mode 100644
index 00000000..675c2332
Binary files /dev/null and b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/expected/expected.parquet differ
diff --git a/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/all_variants.wID.deepSea.parquet/.snakemake_timestamp b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/all_variants.wID.deepSea.parquet/.snakemake_timestamp
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/all_variants.wID.deepSea.parquet/_common_metadata b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/all_variants.wID.deepSea.parquet/_common_metadata
new file mode 100644
index 00000000..d7226a1c
Binary files /dev/null and b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/all_variants.wID.deepSea.parquet/_common_metadata differ
diff --git a/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/all_variants.wID.deepSea.parquet/_metadata b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/all_variants.wID.deepSea.parquet/_metadata
new file mode 100644
index 00000000..d7226a1c
Binary files /dev/null and b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/all_variants.wID.deepSea.parquet/_metadata differ
diff --git a/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/all_variants.wID.deepSea.parquet/part.0.parquet b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/all_variants.wID.deepSea.parquet/part.0.parquet
new file mode 100644
index 00000000..b73874c5
Binary files /dev/null and b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/all_variants.wID.deepSea.parquet/part.0.parquet differ
diff --git a/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/annotation_colnames_filling_values.yaml b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/annotation_colnames_filling_values.yaml
new file mode 100644
index 00000000..fe0c483b
--- /dev/null
+++ b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/annotation_colnames_filling_values.yaml
@@ -0,0 +1,63 @@
+annotation_column_names: 
+  'af' : 
+    'combined_UKB_NFE_AF' : 0
+  'maf_mb' : 
+    'combined_UKB_NFE_AF_MB' : 10000
+  'maf' :
+    'combined_UKB_NFE_MAF' : 0
+  'PolyPhen' : 
+    'polyphen_score' : 0
+  'SIFT' : 
+    'sift_score' : 1
+  'QKI_hg2' : 
+    'DeepRipe_plus_QKI_lip_hg2' : 0
+  'QKI_k5' : 
+    'DeepRipe_plus_QKI_clip_k5' : 0
+  'KHDRBS1_k5' : 
+    'DeepRipe_plus_KHDRBS1_clip_k5' : 0
+  'ELAVL1_parclip' : 
+    'DeepRipe_plus_ELAVL1_parclip' : 0
+  'TARDBP_parclip' : 
+    'DeepRipe_plus_TARDBP_parclip' : 0
+  'HNRNPD_parclip' : 
+    'DeepRipe_plus_HNRNPD_parclip' : 0
+  'MBNL1_parclip' : 
+    'DeepRipe_plus_MBNL1_parclip' : 0
+  'QKI_parclip' : 
+    'DeepRipe_plus_QKI_parclip' : 0
+  'Consequence_splice_acceptor_variant' :
+    'Consequence_splice_acceptor_variant' : 0
+  'Consequence_splice_donor_variant' :
+    'Consequence_splice_donor_variant' : 0
+  'Consequence_stop_gained' :
+    'Consequence_stop_gained' : 0
+  'Consequence_frameshift_variant' :
+    'Consequence_frameshift_variant' : 0
+  'Consequence_stop_lost' :
+    'Consequence_stop_lost' : 0
+  'Consequence_start_lost' :
+    'Consequence_start_lost' : 0
+  'Consequence_inframe_insertion' :
+    'Consequence_inframe_insertion' : 0
+  'Consequence_inframe_deletion' :
+    'Consequence_inframe_deletion' : 0
+  'Consequence_missense_variant' :
+    'Consequence_missense_variant' : 0
+  'Consequence_protein_altering_variant' :
+    'Consequence_protein_altering_variant' : 0
+  'Consequence_splice_region_variant' :
+    'Consequence_splice_region_variant' : 0
+  'DeepSEA_PC_1' :
+    'DeepSEA_PC_1' : 0
+  'DeepSEA_PC_2' :
+    'DeepSEA_PC_2' : 0
+  'DeepSEA_PC_3' :
+    'DeepSEA_PC_3' : 0
+  'DeepSEA_PC_4' :
+    'DeepSEA_PC_4' : 0
+  'DeepSEA_PC_5' :
+    'DeepSEA_PC_5' : 0
+  'DeepSEA_PC_6' :
+    'DeepSEA_PC_6' : 0
+  'AF' :
+    'AF' : 0
diff --git a/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/vep_deepripe.parquet b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/vep_deepripe.parquet
new file mode 100644
index 00000000..715ee750
Binary files /dev/null and b/tests/annotations/test_data/merge_deepsea_pcas/merge_deepsea_pcas_small/input/vep_deepripe.parquet differ
diff --git a/tests/annotations/test_data/process_annotations/process_annotations_small/expected_variants.parquet b/tests/annotations/test_data/process_annotations/process_annotations_small/expected_variants.parquet
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/annotations/test_data/process_annotations/process_annotations_small/in_variants.parquet b/tests/annotations/test_data/process_annotations/process_annotations_small/in_variants.parquet
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/annotations/test_data/select_rename_fill_columns/select_rename_fill_columns_small/expected/expected.parquet b/tests/annotations/test_data/select_rename_fill_columns/select_rename_fill_columns_small/expected/expected.parquet
new file mode 100644
index 00000000..710d4112
Binary files /dev/null and b/tests/annotations/test_data/select_rename_fill_columns/select_rename_fill_columns_small/expected/expected.parquet differ
diff --git a/tests/annotations/test_data/select_rename_fill_columns/select_rename_fill_columns_small/input/annotation_colnames_filling_values.yaml b/tests/annotations/test_data/select_rename_fill_columns/select_rename_fill_columns_small/input/annotation_colnames_filling_values.yaml
new file mode 100644
index 00000000..fe0c483b
--- /dev/null
+++ b/tests/annotations/test_data/select_rename_fill_columns/select_rename_fill_columns_small/input/annotation_colnames_filling_values.yaml
@@ -0,0 +1,63 @@
+annotation_column_names: 
+  'af' : 
+    'combined_UKB_NFE_AF' : 0
+  'maf_mb' : 
+    'combined_UKB_NFE_AF_MB' : 10000
+  'maf' :
+    'combined_UKB_NFE_MAF' : 0
+  'PolyPhen' : 
+    'polyphen_score' : 0
+  'SIFT' : 
+    'sift_score' : 1
+  'QKI_hg2' : 
+    'DeepRipe_plus_QKI_lip_hg2' : 0
+  'QKI_k5' : 
+    'DeepRipe_plus_QKI_clip_k5' : 0
+  'KHDRBS1_k5' : 
+    'DeepRipe_plus_KHDRBS1_clip_k5' : 0
+  'ELAVL1_parclip' : 
+    'DeepRipe_plus_ELAVL1_parclip' : 0
+  'TARDBP_parclip' : 
+    'DeepRipe_plus_TARDBP_parclip' : 0
+  'HNRNPD_parclip' : 
+    'DeepRipe_plus_HNRNPD_parclip' : 0
+  'MBNL1_parclip' : 
+    'DeepRipe_plus_MBNL1_parclip' : 0
+  'QKI_parclip' : 
+    'DeepRipe_plus_QKI_parclip' : 0
+  'Consequence_splice_acceptor_variant' :
+    'Consequence_splice_acceptor_variant' : 0
+  'Consequence_splice_donor_variant' :
+    'Consequence_splice_donor_variant' : 0
+  'Consequence_stop_gained' :
+    'Consequence_stop_gained' : 0
+  'Consequence_frameshift_variant' :
+    'Consequence_frameshift_variant' : 0
+  'Consequence_stop_lost' :
+    'Consequence_stop_lost' : 0
+  'Consequence_start_lost' :
+    'Consequence_start_lost' : 0
+  'Consequence_inframe_insertion' :
+    'Consequence_inframe_insertion' : 0
+  'Consequence_inframe_deletion' :
+    'Consequence_inframe_deletion' : 0
+  'Consequence_missense_variant' :
+    'Consequence_missense_variant' : 0
+  'Consequence_protein_altering_variant' :
+    'Consequence_protein_altering_variant' : 0
+  'Consequence_splice_region_variant' :
+    'Consequence_splice_region_variant' : 0
+  'DeepSEA_PC_1' :
+    'DeepSEA_PC_1' : 0
+  'DeepSEA_PC_2' :
+    'DeepSEA_PC_2' : 0
+  'DeepSEA_PC_3' :
+    'DeepSEA_PC_3' : 0
+  'DeepSEA_PC_4' :
+    'DeepSEA_PC_4' : 0
+  'DeepSEA_PC_5' :
+    'DeepSEA_PC_5' : 0
+  'DeepSEA_PC_6' :
+    'DeepSEA_PC_6' : 0
+  'AF' :
+    'AF' : 0
diff --git a/tests/annotations/test_data/select_rename_fill_columns/select_rename_fill_columns_small/input/annotations.parquet b/tests/annotations/test_data/select_rename_fill_columns/select_rename_fill_columns_small/input/annotations.parquet
new file mode 100644
index 00000000..cf3bad1a
Binary files /dev/null and b/tests/annotations/test_data/select_rename_fill_columns/select_rename_fill_columns_small/input/annotations.parquet differ
diff --git a/tests/deeprvat/test_train.py b/tests/deeprvat/test_train.py
index d97cd2d8..f60e0f36 100644
--- a/tests/deeprvat/test_train.py
+++ b/tests/deeprvat/test_train.py
@@ -61,7 +61,6 @@ def make_multipheno_data():
         )
         data[p]["input_tensor"] = data[p]["input_tensor_zarr"][:]
         data[p]["samples"] = {"train": np.arange(data[p]["y"].shape[0])}
-        data[p]["sample_indices"] = np.arange(data[p]["y"].shape[0])
 
     return data
 
@@ -147,10 +146,9 @@ def test_make_dataset(phenotype: str, min_variant_count: int, tmp_path: Path):
         yaml.dump(config, f)
 
     # This is the function we want to test
-    input_tensor_out_file = tmp_path / "input_tensor.zarr"
-    covariates_out_file = tmp_path / "covariates.zarr"
-    y_out_file = tmp_path / "y.zarr"
-    samples_out_file = tmp_path / "sample_ids.zarr"
+    input_tensor_out_file = str(tmp_path / "input_tensor.zarr")
+    covariates_out_file = str(tmp_path / "covariates.zarr")
+    y_out_file = str(tmp_path / "y.zarr")
     logger.info("Constructing test dataset")
     _ = make_dataset_(
         False,
@@ -161,7 +159,6 @@ def test_make_dataset(phenotype: str, min_variant_count: int, tmp_path: Path):
         input_tensor_out_file,
         covariates_out_file,
         y_out_file,
-        samples_out_file,
     )
 
     # Load the data it output