diff --git a/.gitignore b/.gitignore
index 0b50f890..a20ec1c1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -13,3 +13,5 @@ ste_notes.txt
 assets/HDP_files*
 scratch/
 scratchhhh/
+tests/test_data/all_samples.somatic.mutations.maf
+tests/test_data/all_samples_indv.depths.tsv.gz
diff --git a/bin/annotate_omega_failing.py b/bin/annotate_omega_failing.py
index 9d860ed6..af6f6ae6 100755
--- a/bin/annotate_omega_failing.py
+++ b/bin/annotate_omega_failing.py
@@ -271,8 +271,26 @@ def main(omegas_file: str, compiled_flagged_files: str, output: str) -> None:
         lines = [ln.strip() for ln in fh if ln.strip()]
     flagged_paths = [Path(l) for l in lines]
 
+    # Read omegas with resilience to missing header lines
+    # Some aggregation steps may drop the header; if so, re-read with explicit names
+    def _read_omegas(path: Path) -> pd.DataFrame:
+        try:
+            df = pd.read_csv(path, sep="\t", header=0, dtype=str, skip_blank_lines=True)
+        except pd.errors.EmptyDataError:
+            return pd.DataFrame(columns=["gene","sample","impact","mutations","dnds","pvalue","lower","upper"])  # empty
+        # If expected columns are missing (e.g., header was dropped), re-read with names
+        expected = {"gene","sample","impact","mutations","dnds","pvalue","lower","upper"}
+        if not expected.issubset(set(map(str, df.columns))):
+            df = pd.read_csv(path,
+                             sep="\t",
+                             header=None,
+                             names=["gene","sample","impact","mutations","dnds","pvalue","lower","upper"],
+                             dtype=str,
+                             skip_blank_lines=True)
+        return df.fillna("")
+
     # Read omegas
-    omegas = pd.read_csv(omegas_path, sep="\t", header=0, dtype=str).fillna("")
+    omegas = _read_omegas(omegas_path)
 
     syn_flagged_sample, syn_flagged_gene, npa_flagged_sample, npa_flagged_gene = load_flagged_tables(flagged_paths)
 
diff --git a/bin/create_consensus_panel.py b/bin/create_consensus_panel.py
index 7b910af4..ea928abf 100755
--- a/bin/create_consensus_panel.py
+++ b/bin/create_consensus_panel.py
@@ -53,6 +53,9 @@ def create_consensus_panel(compact_annot_panel_path, depths_path, version, conse
     #####
     # Filter failing columns only for rows that pass the compliance threshold
     compliance_df_passing = compliance_df.filter(passing_rows)
+    
+    print(f"DEBUG: Total positions passing compliance threshold: {compliance_df_passing.height}")
+    print(f"DEBUG: Number of samples: {compliance_df_passing.width}")
 
     # Invert all boolean values (True → False, False → True)
     failing_mask = pl.DataFrame([
@@ -70,6 +73,7 @@ def create_consensus_panel(compact_annot_panel_path, depths_path, version, conse
                     "Failed": True
                 })
 
+    print(f"DEBUG: Total failing entries found: {len(failing_columns_counts)}")
 
     if failing_columns_counts:
         failing_columns_counts_df = pl.DataFrame(failing_columns_counts)
@@ -79,6 +83,7 @@ def create_consensus_panel(compact_annot_panel_path, depths_path, version, conse
             .rename({"count": "FAILING_COUNT"})
         )
         failure_counts_filtered.write_csv(f"failing_consensus.{version}.tsv", separator="\t")
+        print(f"DEBUG: Created failing_consensus.{version}.tsv with {failure_counts_filtered.height} samples")
 
 
 @click.command()
diff --git a/bin/create_panel_versions.py b/bin/create_panel_versions.py
index 655d0762..32da041e 100755
--- a/bin/create_panel_versions.py
+++ b/bin/create_panel_versions.py
@@ -1,14 +1,20 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 
+"""
+create_panel_versions.py
 
-import click
-import pandas as pd
-import os
+Generates multiple VEP annotation panel subsets based on the 'IMPACT' column
+using the high-performance Polars library.
+
+Usage:
+    python create_panel_versions.py --compact-annot-panel-path <input_tsv> --output <output_prefix>
+"""
 
-# TODO: check pandas version 2.0.3
-# -- Auxiliary functions -- #
+import polars as pl
+import click
+import sys
 
-panel_impact_dict = {
+PANEL_IMPACT_DICT = {
 
     "protein_affecting": ["nonsense", "missense",
                             "essential_splice",
@@ -68,25 +74,33 @@
 
 }
 
-# -- Main function -- #
 
-def create_panel_versions(compact_annot_panel_path, output_path):
+def create_panel_versions(input_path: str, output_prefix: str) -> None:
+    """
+    Generates panel subsets from a VEP-annotated file using Polars.
+
+    \b
+    INPUT_PATH: Path to the annotated TSV file.
+    OUTPUT_PREFIX: Prefix for the output files (e.g., 'output/panel').
+    """
+    try:
+        df = pl.read_csv(input_path, separator="\t")
+    except Exception as e:
+        click.echo(f"Error reading input file: {e}", err=True)
+        sys.exit(1)
 
-    # Load VEP annotated panel, already compacted to have one variant per site
-    ## requires column named IMPACT with consequence type
-    compact_annot_panel_df = pd.read_csv(compact_annot_panel_path, sep = "\t")
+    if "IMPACT" not in df.columns:
+        click.echo("ERROR: 'IMPACT' column not found in input file.", err=True)
+        sys.exit(1)
 
-    # Create panel versions
-    for version in panel_impact_dict:
+    for version_name, impact_values in PANEL_IMPACT_DICT.items():
+        filtered = df.filter(pl.col("IMPACT").is_in(impact_values))
+        filtered.write_csv(f"{output_prefix}.{version_name}.tsv", separator="\t")
 
-        panel_version = compact_annot_panel_df.loc[compact_annot_panel_df["IMPACT"].isin(panel_impact_dict[version])]
-        panel_version.to_csv(f"{output_path}.{version}.tsv",
-                                sep = "\t", index = False)
+    # Write the full file as a version
+    df.write_csv(f"{output_prefix}.all.tsv", separator="\t")
 
-    # Store complete panel (better change this way of using this version in nextflow)
-    version = "all"
-    compact_annot_panel_df.to_csv(f"{output_path}.{version}.tsv",
-                                    sep = "\t", index = False)
+    click.echo("Panel versions generated successfully.")
 
 
 @click.command()
diff --git a/bin/panel_custom_processing.py b/bin/panel_custom_processing.py
index 1c6cb5c8..5eee4b37 100755
--- a/bin/panel_custom_processing.py
+++ b/bin/panel_custom_processing.py
@@ -3,10 +3,9 @@
 
 
 import click
-import pandas as pd
-import numpy as np
+import polars as pl
 
-from read_utils import custom_na_values
+from read_utils import custom_na_values_list
 muttype_conversion_map = {
                 'G/A': 'C/T',
                 'G/C': 'C/G',
@@ -21,100 +20,173 @@ def customize_panel_regions(VEP_output_file, custom_regions_file, customized_out
                             simple = True
                             ):
     """
-    # TODO
-    explain what this function does
+    Modifies annotations in a VEP output file based on custom genomic regions.
+
+    - For each region in the custom regions file, identifies the corresponding slice
+      in the VEP output.
+    - Updates gene names and impact values for the region.
+    - Saves both the modified annotation file and a record of added regions.
+
+    Args:
+        VEP_output_file (str): Path to the full VEP output file (TSV).
+        custom_regions_file (str): Custom region definitions (tab-delimited).
+        customized_output_annotation_file (str): Output file for updated annotations.
+        simple (bool): If True, outputs simplified annotations; else adds more fields.
     """
+
     # simple = ['CHROM', 'POS', 'REF', 'ALT', 'MUT_ID'          , 'GENE', 'IMPACT'                                              , 'CONTEXT_MUT', 'CONTEXT']
     # rich   = ['CHROM', 'POS', 'REF', 'ALT', 'MUT_ID', 'STRAND', 'GENE', 'IMPACT', 'Feature', 'Protein_position', 'Amino_acids', 'CONTEXT_MUT', 'CONTEXT']
-    all_possible_sites = pd.read_csv(VEP_output_file, sep = "\t",
-                                        na_values = custom_na_values)
+
+    # Read entire file once, partition by chromosome
+    all_possible_sites = pl.read_csv(VEP_output_file, separator="\t",
+                                        null_values=custom_na_values_list,
+                                        schema_overrides={"CHROM": pl.Utf8, "Protein_position": pl.Utf8}
+                                    ).with_row_index("__idx")
     print("all possible sites loaded")
 
-    custom_regions_df = pd.read_table(custom_regions_file)
-
-    added_regions_df = pd.DataFrame()
-
-    current_chr = ""
-    for ind, row in custom_regions_df.iterrows():
-        try:
-            if row["CHROM"] != current_chr:
-                current_chr = row["CHROM"]
-                chr_data = all_possible_sites[all_possible_sites["CHROM"] == current_chr]
-                print("Updating chromosome to:", current_chr)
-
-            # Get start and end indices
-            ind_start = np.where(chr_data["POS"] == row["START"])[0][0]
-            ind_end = np.where(chr_data.iloc[ind_start:,:]["POS"] == row["END"])[0][-1]
-            # gene = chr_data.iloc[ind_start]["GENE"]
-
-            ind_end += ind_start
-
-            upd_start = ind_start
-            upd_end = ind_end
-
-            # Extract hotspot data and modify gene names
-            hotspot_data = chr_data.iloc[upd_start: upd_end + 1, :].copy().drop("IMPACT", axis = 'columns')
-            hotspot_data["index_col"] = hotspot_data.index
-            original_df_start = hotspot_data.index[0]
-            original_df_end = hotspot_data.index[-1]
-            hotspot_data["GENE"] = row["NAME"]
-
-            # Split the string into individual entries
-            entries = row["IMPACTFUL"].split(',')
-
-            # Create a DataFrame
-            impactful_df = pd.DataFrame(entries, columns = ["MUT_ID_pyr"])
-            impactful_df["IMPACT"] = row["IMPACT"]
-
-            all_pos_len = hotspot_data.shape[0]
-
-            # convert MUT_ID to C>N and T>N only
-            hotspot_data["MUT_ID_pyr"] = hotspot_data["MUT_ID"].replace(muttype_conversion_map, regex = True)
-            hotspot_data = hotspot_data.merge(impactful_df,
-                                                on = ["MUT_ID_pyr"],
-                                                how = 'outer'
-                                                )
-            all_pos_len_after = hotspot_data.shape[0]
-
-            # TODO add an error raise
-            if all_pos_len != all_pos_len_after:
-                print("Some of the mutations provided are not in the desired region")
-                print(hotspot_data[hotspot_data["POS"].isna()]["MUT_ID_pyr"])
-                hotspot_data = hotspot_data[~(hotspot_data["POS"].isna())].reset_index(drop = True)
-                hotspot_data["POS"] = hotspot_data["POS"].astype(int)
-
-            hotspot_data["IMPACT"] = hotspot_data["IMPACT"].fillna(row["NEUTRAL"])
-            hotspot_data = hotspot_data.sort_values(by='index_col').drop("index_col", axis = 'columns')
-
-            ## Insert modified rows back into the df
-            if simple:
-                all_possible_sites.loc[original_df_start: original_df_end, ["GENE", "IMPACT"]] = hotspot_data[["GENE", "IMPACT"]].values
-            else:
-                print("Getting Feature to '-'")
-                hotspot_data["Feature"] = '-'
-                all_possible_sites.loc[original_df_start: original_df_end, ["GENE", "IMPACT", "Feature"]] = hotspot_data[["GENE", "IMPACT", "Feature"]].values
-
-            added_regions_df = pd.concat((added_regions_df, hotspot_data))
-            print("Small region added:", row["NAME"])
-
-        except Exception as e:
-            print(f"Error processing row {row}: {e}")
-
-    all_possible_sites = all_possible_sites.drop_duplicates(subset = ['CHROM', 'POS', 'REF', 'ALT', 'MUT_ID',
-                                                                    'GENE', 'CONTEXT_MUT', 'CONTEXT', 'IMPACT'],
-                                                            keep = 'first')
-    all_possible_sites.to_csv(customized_output_annotation_file,
-                                        header = True,
-                                        index = False,
-                                        sep = "\t")
-
-    added_regions_df = added_regions_df.drop_duplicates(subset = ['CHROM', 'POS', 'REF', 'ALT', 'MUT_ID',
-                                                                    'GENE', 'CONTEXT_MUT', 'CONTEXT', 'IMPACT'],
-                                                        keep = 'first')
-    added_regions_df.to_csv('added_regions.tsv' if simple else 'added_regions.rich.tsv',
-                                header = True,
-                                index = False,
-                                sep = "\t")
+    # Get chromosome order before partitioning
+    all_chroms = all_possible_sites["CHROM"].unique(maintain_order=True).to_list()
+
+    # Partition by chromosome and release the full dataframe
+    chrom_partitions = {
+        df["CHROM"][0]: df
+        for df in all_possible_sites.partition_by("CHROM", maintain_order=True)
+    }
+    del all_possible_sites
+
+    custom_regions_df = pl.read_csv(custom_regions_file, separator="\t",
+                                        schema_overrides={"CHROM": pl.Utf8})
+
+    # Group custom regions by chromosome
+    custom_by_chrom = {}
+    for row in custom_regions_df.iter_rows(named=True):
+        custom_by_chrom.setdefault(row["CHROM"], []).append(row)
+
+    added_regions_list = []
+    write_header = True
+
+    for chrom in all_chroms:
+        chr_data = chrom_partitions.pop(chrom)
+        print(f"Processing chromosome: {chrom} ({chr_data.height} rows)")
+
+        chr_updates = []
+
+        if chrom in custom_by_chrom:
+            for row in custom_by_chrom[chrom]:
+                try:
+                    # Find start and end indices
+                    idx_start = chr_data.filter(pl.col("POS") == row["START"])["__idx"][0]
+                    from_start = chr_data.filter(pl.col("__idx") >= idx_start)
+                    idx_end = from_start.filter(pl.col("POS") == row["END"])["__idx"][-1]
+
+                    # Extract hotspot data and modify gene names
+                    hotspot_data = chr_data.filter(
+                        (pl.col("__idx") >= idx_start) & (pl.col("__idx") <= idx_end)
+                    ).drop("IMPACT").with_columns(
+                        pl.lit(row["NAME"]).alias("GENE")
+                    )
+
+                    # Split the string into individual entries
+                    entries = row["IMPACTFUL"].split(',')
+
+                    # Create a DataFrame
+                    impactful_df = pl.DataFrame({
+                        "MUT_ID_pyr": entries,
+                        "IMPACT": [row["IMPACT"]] * len(entries)
+                    })
+
+                    all_pos_len = hotspot_data.height
+
+                    # convert MUT_ID to C>N and T>N only
+                    pyr_expr = pl.col("MUT_ID")
+                    for old, new in muttype_conversion_map.items():
+                        pyr_expr = pyr_expr.str.replace(old, new, literal=True)
+                    hotspot_data = hotspot_data.with_columns(pyr_expr.alias("MUT_ID_pyr"))
+
+                    hotspot_data = hotspot_data.join(impactful_df,
+                                                        on = "MUT_ID_pyr",
+                                                        how = "full",
+                                                        suffix = "_imp"
+                                                        )
+                    # Coalesce MUT_ID_pyr from both sides of the full join
+                    if "MUT_ID_pyr_imp" in hotspot_data.columns:
+                        hotspot_data = hotspot_data.with_columns(
+                            pl.coalesce(["MUT_ID_pyr", "MUT_ID_pyr_imp"]).alias("MUT_ID_pyr")
+                        ).drop("MUT_ID_pyr_imp")
+
+                    all_pos_len_after = hotspot_data.height
+
+                    # TODO add an error raise
+                    if all_pos_len != all_pos_len_after:
+                        print("Some of the mutations provided are not in the desired region")
+                        print(hotspot_data.filter(pl.col("POS").is_null())["MUT_ID_pyr"])
+                        hotspot_data = hotspot_data.filter(pl.col("POS").is_not_null())
+                        hotspot_data = hotspot_data.with_columns(pl.col("POS").cast(pl.Int64))
+
+                    hotspot_data = hotspot_data.with_columns(
+                        pl.col("IMPACT").fill_null(row["NEUTRAL"])
+                    ).sort("__idx")
+
+                    ## Collect updates for this chromosome
+                    if simple:
+                        chr_updates.append(hotspot_data.select(["__idx", "GENE", "IMPACT"]))
+                    else:
+                        print("Getting Feature to '-'")
+                        chr_updates.append(
+                            hotspot_data.select(["__idx", "GENE", "IMPACT"]).with_columns(
+                                pl.lit("-").alias("Feature")
+                            )
+                        )
+
+                    added_regions_list.append(hotspot_data)
+                    print("Small region added:", row["NAME"])
+
+                except Exception as e:
+                    print(f"Error processing row {row}: {e}")
+
+        # Apply updates for this chromosome
+        if chr_updates:
+            all_updates = pl.concat(chr_updates)
+            all_updates = all_updates.unique(subset=["__idx"], keep="last")
+
+            chr_data = chr_data.join(all_updates, on="__idx", how="left", suffix="_upd")
+
+            update_cols = [
+                pl.coalesce(["GENE_upd", "GENE"]).alias("GENE"),
+                pl.coalesce(["IMPACT_upd", "IMPACT"]).alias("IMPACT"),
+            ]
+            drop_cols = ["GENE_upd", "IMPACT_upd"]
+
+            if not simple and "Feature_upd" in chr_data.columns:
+                update_cols.append(pl.coalesce(["Feature_upd", "Feature"]).alias("Feature"))
+                drop_cols.append("Feature_upd")
+
+            chr_data = chr_data.with_columns(update_cols).drop(drop_cols)
+
+        # Deduplicate, sort, and write this chromosome
+        chr_data = chr_data.sort("__idx").drop("__idx").unique(
+                                                    subset = ['CHROM', 'POS', 'REF', 'ALT', 'MUT_ID',
+                                                                'GENE', 'CONTEXT_MUT', 'CONTEXT', 'IMPACT'],
+                                                    keep = 'first',
+                                                    maintain_order = True)
+
+        with open(customized_output_annotation_file, 'ab' if not write_header else 'wb') as f:
+            chr_data.write_csv(f, separator="\t", include_header=write_header)
+        write_header = False
+
+    # Write added regions
+    if added_regions_list:
+        added_regions_df = pl.concat(added_regions_list, how="diagonal")
+        cols_to_drop = [c for c in ["__idx"] if c in added_regions_df.columns]
+        if cols_to_drop:
+            added_regions_df = added_regions_df.drop(cols_to_drop)
+        added_regions_df = added_regions_df.unique(
+                                                subset = ['CHROM', 'POS', 'REF', 'ALT', 'MUT_ID',
+                                                            'GENE', 'CONTEXT_MUT', 'CONTEXT', 'IMPACT'],
+                                                keep = 'first',
+                                                maintain_order = True)
+        added_regions_df.write_csv('added_regions.tsv' if simple else 'added_regions.rich.tsv',
+                                    separator="\t")
 
 
 
@@ -128,4 +200,3 @@ def main(vep_output_file, custom_regions_file, customized_output_annotation_file
 
 if __name__ == '__main__':
     main()
-
diff --git a/bin/panel_postprocessing_annotation.py b/bin/panel_postprocessing_annotation.py
index 9cf3f173..45925dba 100755
--- a/bin/panel_postprocessing_annotation.py
+++ b/bin/panel_postprocessing_annotation.py
@@ -76,6 +76,10 @@ def VEP_annotation_to_single_row(df_annotation, keep_genes = False):
     return returned_df
 
 
+def safe_transform_context(row, chosen_assembly):
+    if pd.isna(row["POS"]) or pd.isna(row["CHROM"]) or pd.isna(row["REF"]) or pd.isna(row["ALT"]):
+        return "UNKNOWN"
+    return transform_context(row["CHROM"], row["POS"], f'{row["REF"]}/{row["ALT"]}', chosen_assembly)
 
 
 def VEP_annotation_to_single_row_only_canonical(df_annotation, keep_genes = False):
@@ -126,11 +130,6 @@ def VEP_annotation_to_single_row_only_canonical(df_annotation, keep_genes = Fals
     # we return the dataframe with all the original columns of the VEP file
     return returned_df
 
-
-
-
-
-
 def vep2summarizedannotation_panel(VEP_output_file, all_possible_sites_annotated_file,
                                     assembly = 'hg38',
                                     using_canonical = True
@@ -138,12 +137,12 @@ def vep2summarizedannotation_panel(VEP_output_file, all_possible_sites_annotated
     """
     Process VEP output and summarize annotations for a panel.
     """
+    chosen_assembly = assembly_name2function[assembly]
     all_possible_sites = pd.read_csv(VEP_output_file, sep = "\t",
                                         header = None, na_values = custom_na_values)
     print("All possible sites loaded")
     all_possible_sites.columns = ['CHROM', 'POS', 'REF', 'ALT', 'MUT_ID', 'Feature', 'Consequence', 'Protein_position',
                                    'Amino_acids', 'STRAND', 'SYMBOL', 'CANONICAL', 'ENSP']
-
     if using_canonical:
         annotated_variants = VEP_annotation_to_single_row_only_canonical(all_possible_sites, keep_genes= True)
         if annotated_variants is not None:
@@ -170,12 +169,10 @@ def vep2summarizedannotation_panel(VEP_output_file, all_possible_sites_annotated
 
     # add context type to all SNVs
     # remove context from the other substitution types
-    chosen_assembly = assembly_name2function[assembly]
-    annotated_variants["CONTEXT_MUT"] = annotated_variants.apply(lambda x: transform_context(x["CHROM"], x["POS"], f'{x["REF"]}/{x["ALT"]}', chosen_assembly) , axis = 1)
+    annotated_variants["CONTEXT_MUT"] = annotated_variants.apply(lambda row: safe_transform_context(row, chosen_assembly), axis = 1)
     print("Context added")
 
     annotated_variants["CONTEXT"] = annotated_variants["CONTEXT_MUT"].apply(lambda x: x[:3])
-
     annotated_variants_reduced = annotated_variants[['CHROM', 'POS', 'REF', 'ALT', 'MUT_ID', 'STRAND', 'SYMBOL', 'Consequence_broader', 'Feature', 'Protein_position', 'Amino_acids', 'CONTEXT_MUT', 'CONTEXT']]
     annotated_variants_reduced.columns = ['CHROM', 'POS', 'REF', 'ALT', 'MUT_ID', 'STRAND', 'GENE', 'IMPACT', 'Feature', 'Protein_position', 'Amino_acids', 'CONTEXT_MUT', 'CONTEXT']
     annotated_variants_reduced = annotated_variants_reduced.sort_values(by = ['CHROM', 'POS', 'REF', 'ALT'] )
diff --git a/bin/sites_table_from_positions.py b/bin/sites_table_from_positions.py
index 892371fa..93046b1b 100755
--- a/bin/sites_table_from_positions.py
+++ b/bin/sites_table_from_positions.py
@@ -19,7 +19,7 @@ def get_non_ref(l, letters = {"A", "C", "G", "T"}):
 
 
 # -- Main function -- #
-def generate_all_sites_4VEP(input_positions, genome, output_file_with_sites):
+def generate_all_sites_4VEP(input_positions, genome, output_prefix, chunk_size=0):
 
     # read CHROM,POS positions file; check dtypes
     positions_df = pd.read_csv(input_positions, sep = "\t", header = 0,
@@ -37,20 +37,30 @@ def generate_all_sites_4VEP(input_positions, genome, output_file_with_sites):
     positions_df["MUTATION"] = positions_df["SEQ"].astype(str) + "/" + positions_df["ALT"].astype(str)
     positions_df["STRAND"] = "+"
 
-    # save
-    positions_df[['CHROM', 'POS', 'POS', 'MUTATION', 'STRAND']].to_csv(output_file_with_sites,
-                                                                        header = False,
-                                                                        index = False,
-                                                                        sep = "\t")
+    # add chr prefix to CHROM
+    positions_df["CHROM"] = "chr" + positions_df["CHROM"]
+
+    output_df = positions_df[['CHROM', 'POS', 'POS', 'MUTATION', 'STRAND']]
+
+    # save as chunks or single file
+    if chunk_size > 0:
+        for i, start in enumerate(range(0, len(output_df), chunk_size)):
+            chunk = output_df.iloc[start:start + chunk_size]
+            chunk.to_csv(f"{output_prefix}.chunk{i}.tsv",
+                         header=False, index=False, sep="\t")
+    else:
+        output_df.to_csv(f"{output_prefix}.chunk1.tsv",
+                         header=False, index=False, sep="\t")
 
 
 
 @click.command()
 @click.option('--input-positions', required=True, type=click.Path(exists=True), help='Input positions file (TSV)')
 @click.option('--genome-assembly', required=True, type=click.Choice(['hg38', 'mm39']), help='Genome assembly (hg38, mm39)')
-@click.option('--output-file-with-sites', required=True, type=click.Path(), help='Output file for sites (TSV)')
-def main(input_positions, genome_assembly, output_file_with_sites):
-    generate_all_sites_4VEP(input_positions, genome_assembly, output_file_with_sites)
+@click.option('--output-prefix', required=True, type=str, help='Output file prefix (produces <prefix>.chunk<N>.tsv)')
+@click.option('--chunk-size', default=0, type=int, help='Number of rows per chunk (0 = no chunking)')
+def main(input_positions, genome_assembly, output_prefix, chunk_size):
+    generate_all_sites_4VEP(input_positions, genome_assembly, output_prefix, chunk_size)
 
 if __name__ == '__main__':
     main()
diff --git a/conf/base.config b/conf/base.config
index fc7920d7..c5a2d9d8 100644
--- a/conf/base.config
+++ b/conf/base.config
@@ -1,27 +1,28 @@
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    bbglab/deepCSA Nextflow base config file
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    A 'blank slate' config file, appropriate for general use on most high performance
-    compute environments. Assumes that all software is installed and available on
-    the PATH. Runs in `local` mode - all jobs will be run on the logged in environment.
-----------------------------------------------------------------------------------------
-*/
-
 process {
-
-    resourceLimits = [cpus: params.max_cpus, memory: params.max_memory, time: params.max_time]
-
-    // TODO nf-core: Check the defaults for all processes
-    cpus           = { 1 }
-    memory         = { 6.GB * task.attempt }
-    time           = { 15.min * task.attempt }
-
-
-
-    errorStrategy  = { task.exitStatus in ((130..145) + 104) ? 'retry' : 'finish' }
-    maxRetries     = 3
-    maxErrors      = '-1'
+  // === RESOURCE LIMITS ===
+    resourceLimits = [ 
+        cpus: params.max_cpus, 
+        memory: params.max_memory, 
+        time: params.max_time 
+    ]
+
+    // === SENSIBLE DEFAULTS ===
+    // Most processes use minimal resources based on usage analysis
+    cpus   = { 1 }
+    memory = { 4.GB * task.attempt }
+    time   = { 360.min * task.attempt }
+
+    // === ERROR HANDLING ===
+    errorStrategy = { 
+        if (task.exitStatus in ((130..145) + 104)) {
+            sleep(Math.pow(2, task.attempt) * 200 as long)  // Exponential backoff
+            return 'retry'
+        } else {
+            return 'finish'
+        }
+    }
+    maxRetries = 3
+    maxErrors  = '-1'
 
     withLabel: error_ignore {
         errorStrategy = 'ignore'
@@ -31,90 +32,149 @@ process {
         maxRetries    = 2
     }
 
+    // === PANEL CREATION PROCESSES ===
+    // Large memory requirements for genomic position processing
+    withName: 'BBGTOOLS:DEEPCSA:CREATEPANELS:SITESFROMPOSITIONS' {
+        memory = { 8.GB * task.attempt }
+    }
 
-    // Process-specific resource requirements
-    // NOTE - Please try and re-use the labels below as much as possible.
-    //        These labels are used and recognised by default in DSL2 files hosted on nf-core/modules.
-    //        If possible, it would be nice to keep the same label naming convention when
-    //        adding in your local modules too.
-    withLabel: process_single {
-        cpus = { 1 }
+    // VEP annotation is CPU and memory intensive for large VCFs
+    withName: 'BBGTOOLS:DEEPCSA:CREATEPANELS:VCFANNOTATEPANEL:ENSEMBLVEP_VEP*' {
+        cpus   = { 24 * task.attempt }
+        memory = { 6.GB * task.attempt }
     }
-    withLabel: process_low {
+
+    withName: 'BBGTOOLS:DEEPCSA:CREATEPANELS:CUSTOMPROCESSING.*' {
+        memory = { 10.GB * task.attempt }
+    }
+
+    withName: 'BBGTOOLS:DEEPCSA:DEPTHS.*CONS' {
         cpus   = { 2 * task.attempt }
-        memory = { 12.GB * task.attempt }
+        memory = { 8.GB * task.attempt }
     }
-    withLabel: process_medium {
-        cpus   = { 6 * task.attempt }
-        memory = { 36.GB * task.attempt }
+
+    withName: 'BBGTOOLS:DEEPCSA:CREATEPANELS:CREATECAPTUREDPANELS*' {
+        memory = { 6.GB * task.attempt }
     }
-    withLabel: process_high {
-        cpus   = { 12 * task.attempt }
-        memory = { 72.GB * task.attempt }
-        time   = { 16.h * task.attempt }
+
+    // Large consensus panels require substantial memory
+    withName: 'BBGTOOLS:DEEPCSA:CREATEPANELS:CREATECONSENSUSPANELS.*' {
+        memory = { 18.GB * task.attempt }
     }
 
+    // === ANALYSIS PROCESSES ===
+    withName: 'BBGTOOLS:DEEPCSA:ANNOTATEDEPTHS*' {
+        memory = { 18.GB * task.attempt }
+    }
 
-    withLabel: process_low_memory {
-        memory = { 4.GB * task.attempt }
+    withName: '(BBGTOOLS:DEEPCSA:CREATEPANELS:DOMAINANNOTATION*)' {
+        cpus   = { 2 * task.attempt }
+        memory = { 10.GB * task.attempt }
     }
-    withLabel: memory_medium {
-        memory = { 8.GB * task.attempt }
+
+    withName: 'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:SUMANNOTATION*' {
+    memory = { 16.GB * task.attempt }
     }
-    withLabel: process_medium_high_memory {
-        memory = { 36.GB * task.attempt }
+
+    withName:'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:PLOTMAF*' {
+        memory = { 12.GB * task.attempt }
     }
-    withLabel: process_high_memory {
-        memory = { 200.GB * task.attempt }
+
+    withName:'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:WRITEMAF*' {
+        memory = { 12.GB * task.attempt }
     }
 
+    withName: 'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:SOMATICMUTATIONS*' {
+        cpus   = { 1 * task.attempt }
+        memory = { 1.GB * task.attempt }
+    }
 
+    withName: 'BBGTOOLS:DEEPCSA:OMEGANONPROT.*:SUBSETPANEL*' {
+        cpus   = { 2 * task.attempt }
+        memory = { 4.GB * task.attempt }
+    }
 
-    withLabel: time_minimal {
-        time = { 15.m * task.attempt }
+    withName: 'BBGTOOLS:DEEPCSA:CREATEPANELS:POSTPROCESSVEPPANEL*' {
+        cpus   = { 2 * task.attempt }
+        memory = { 8.GB * task.attempt }
     }
-    withLabel: time_low {
-        time = { 4.h * task.attempt }
+
+    withName: 'BBGTOOLS:DEEPCSA:MUTRATE.*:MUTRATE*' {
+        memory = { 8.GB * task.attempt }
     }
-    withLabel: time_medium {
-        time = { 8.h * task.attempt }
+
+    withName: 'BBGTOOLS:DEEPCSA:OMEGA.*:(PREPROCESSING|ESTIMATOR).*' {
+        memory = { 4.GB * task.attempt }
     }
-    withLabel: process_long {
-        time = { 20.h * task.attempt }
+
+    withName: 'BBGTOOLS:DEEPCSA:OMEGA.*:ESTIMATOR_DNDSCV*' {
+        memory = { 2.GB * task.attempt }
     }
 
+    withName: 'BBGTOOLS:DEEPCSA:OMEGA.*:ESTIMATOR_DRIVERMUTRATE*' {
+        memory = { 1.GB * task.attempt }
+    }
 
+    // Catch-all for other OMEGA estimators not explicitly configured
+    // Keep conservative settings until we have usage data
+    withName: 'BBGTOOLS:DEEPCSA:OMEGA.*:ESTIMATOR_(?!DNDSCV|DRIVERMUTRATE).*' {
+        memory = { 4.GB * task.attempt }
+    }
 
-    withLabel: cpu_single_fixed {
-        cpus = { 1 }
+    withName: 'BBGTOOLS:DEEPCSA:SIGNATURESNONPROT:SIGPROFILERASSIGNMENT*' {
+        memory = { 2.GB * task.attempt }
     }
-    withLabel: cpu_single {
-        cpus = { 1 * task.attempt }
+
+    // === UTILITY PROCESSES ===
+    withName: 'BBGTOOLS:DEEPCSA:CUSTOM_DUMPSOFTWAREVERSIONS*' {
+        cache = false
     }
-    withLabel: process_low_fixed_cpus {
-        cpus = { 2 }
+
+    withName: 'BBGTOOLS:DEEPCSA:DEPTHANALYSIS:COMPUTEDEPTHS' {
+        cpus = { 4 * task.attempt }
+        memory = { 4.GB * task.attempt }
     }
-    withLabel: cpu_low {
-        cpus = { 2 * task.attempt }
+
+    withName: 'BBGTOOLS:DEEPCSA:MUTATEDCELLSVAF:MUTATEDGENOMESFROMVAFAM' {
+        errorStrategy = 'ignore'
+        maxRetries = 1
     }
-    withLabel: cpu_lowmed {
-        cpus = { 4 * task.attempt }
+
+    withName: 'COMPARE_SIGNATURES' {
+        errorStrategy = 'ignore'
+        maxRetries = 1
     }
-    withLabel: cpu_medium {
-        cpus = { 8 * task.attempt }
+
+    withName: 'BBGTOOLS:DEEPCSA:PLOTTINGQC:PLOTMUTATIONSPECIFIC' {
+        memory = { 24.GB * task.attempt }
     }
-    withLabel: cpu_medium_high {
-        cpus = { 12 }
+
+    withName: 'BBGTOOLS:DEEPCSA:ENRICHPANELS:DNA2PROTEINMAPPING' {
+        memory = { 30.GB * task.attempt }
     }
-    withLabel: cpu_high {
-        cpus = { 30 * task.attempt }
+
+    withName: 'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:FILTERNANOSEQSNP' {
+        memory = { 8.GB * task.attempt }
     }
-    withLabel: cpu_veryhigh {
-        cpus = { 50 * task.attempt }
+
+    withName: 'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:MERGEBATCH' {
+        memory = { 6.GB * task.attempt }
     }
 
+    withName: 'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:FILTERBATCH' {
+        memory = { 13.GB * task.attempt }
+    }
 
-    withName: CUSTOM_DUMPSOFTWAREVERSIONS {
-        cache = false
+    withName: 'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:CLEANMUTATIONS' {
+        memory = { 10.GB * task.attempt }
     }
-}
+
+
+    
+
+    
+    
+
+    
+    
+}
\ No newline at end of file
diff --git a/conf/exome.config b/conf/exome.config
new file mode 100644
index 00000000..ce7dbac5
--- /dev/null
+++ b/conf/exome.config
@@ -0,0 +1,275 @@
+process {
+    // === RESOURCE LIMITS ===
+    resourceLimits = [ 
+        cpus: params.max_cpus ?: 196, 
+        memory: params.max_memory ?: 968.GB, 
+        time: params.max_time ?: 30.d 
+    ]
+
+    // === SENSIBLE DEFAULTS ===
+    // Most processes use minimal resources based on usage analysis
+    cpus   = { 1 }
+    memory = { 2.GB * task.attempt }
+    time   = { 180.min * task.attempt }
+
+    // === ERROR HANDLING ===
+    errorStrategy = { 
+        if (task.exitStatus in ((130..145) + 104)) {
+            sleep(Math.pow(2, task.attempt) * 200 as long)  // Exponential backoff
+            return 'retry'
+        } else {
+            return 'finish'
+        }
+    }
+    maxRetries = 3
+    maxErrors  = '-1'
+
+    withLabel:error_ignore {
+        errorStrategy = 'ignore'
+    }
+    withLabel:error_retry {
+        errorStrategy = 'retry'
+        maxRetries    = 2
+    }
+
+    // === PANEL CREATION PROCESSES ===
+    // Large memory requirements for genomic position processing
+    withName: 'BBGTOOLS:cDEEPCSA:CREATEPANELS:SITESFROMPOSITIONS' {
+        memory = { 128.GB * task.attempt }
+    }
+
+    // VEP annotation is CPU and memory intensive for large VCFs
+    withName: 'BBGTOOLS:DEEPCSA:CREATEPANELS:VCFANNOTATEPANEL:ENSEMBLVEP_VEP*' {
+        cpus   = { 24 * task.attempt }
+        memory = { 8.GB * task.attempt }
+        time   = { 32.h * task.attempt }
+    }
+
+    withName: 'BBGTOOLS:DEEPCSA:CREATEPANELS:CUSTOMPROCESSING.*' {
+        memory = { 16.GB * task.attempt }
+        time   = { 1.h * task.attempt }
+    }
+
+    withName: 'BBGTOOLS:DEEPCSA:DEPTHS.*CONS' {
+        cpus   = { 2 * task.attempt }
+        memory = { 8.GB * task.attempt }
+    }
+
+    withName: 'BBGTOOLS:DEEPCSA:CREATEPANELS:CREATECAPTUREDPANELS*' {
+        memory = { 210.GB * task.attempt }
+    }
+
+    // Large consensus panels require substantial memory
+    withName: 'BBGTOOLS:DEEPCSA:CREATEPANELS:CREATECONSENSUSPANELS.*' {
+        memory = { 32.GB * task.attempt }
+        time   = { 10.min * task.attempt }
+    }
+
+    // === ANALYSIS PROCESSES ===
+    withName: 'BBGTOOLS:DEEPCSA:ANNOTATEDEPTHS*' {
+        memory = { 64.GB * task.attempt }
+        time   = { 3.h * task.attempt }
+    }
+
+    withName: '(BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:SUMANNOTATION*)' {
+        cpus   = { 2 * task.attempt }
+        memory = { 10.GB * task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:PLOTMAF' {
+        memory = { 32.GB * task.attempt }
+        time   = { 3.h * task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:PLOTNEEDLES' {
+        memory = { 2.GB * task.attempt }
+        time   = { 8.h * task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:PLOTSOMATICMAF' {
+        memory = { 32.GB * task.attempt }
+        time   = { 8.h * task.attempt }
+    }
+
+
+
+    withName: '(BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:SOMATICMUTATIONS*|BBGTOOLS:DEEPCSA:OMEGANONPROT.*:SUBSETPANEL*)' {
+        cpus   = { 2 * task.attempt }
+        memory = { 4.GB * task.attempt }
+        time   = { 360.min * task.attempt }
+    }
+
+    withName: 'BBGTOOLS:DEEPCSA:MUTRATE.*:MUTRATE*' {
+        memory = { 8.GB * task.attempt }
+    }
+
+    withName: '(BBGTOOLS:DEEPCSA:OMEGA.*:(PREPROCESSING|ESTIMATOR).*|BBGTOOLS:DEEPCSA:MULTIQC)' {
+        memory = { 4.GB * task.attempt }
+    }
+
+    //withName: 'BBGTOOLS:DEEPCSA:SIGNATURESNONPROT:SIGPROFILERASSIGNMENT*' {
+    //    memory = { 2.GB * task.attempt }
+    //}
+
+    withName: '(BBGTOOLS:DEEPCSA:MUTRATE.*:SUBSETMUTRATE|BBGTOOLS:DEEPCSA:OMEGA.*:SUBSETOMEGA.*|BBGTOOLS:DEEPCSA:MUTPROFILE.*:COMPUTEMATRIX|BBGTOOLS:DEEPCSA:SIGNATURES.*:MATRIXCONCATWGS|BBGTOOLS:DEEPCSA:SYNMUTRATE|BBGTOOLS:DEEPCSA:SYNMUTREADSRATE|BBGTOOLS:DEEPCSA:SIGNATURES.*:SIGPROFILERASSIGNMENT|BBGTOOLS:DEEPCSA:OMEGA.*:GROUPGENES|BBGTOOLS:DEEPCSA:SIGNATURES.*:SIGPROBS|BBGTOOLS:DEEPCSA:MUTS2SIGS|BBGTOOLS:DEEPCSA:CUSTOM_DUMPSOFTWAREVERSIONS|BBGTOOLS:DEEPCSA:TABLE2GROUP|BBGTOOLS:DEEPCSA:INPUT_CHECK:SAMPLESHEET_CHECK|BBGTOOLS:DEEPCSA:DEPTHANALYSIS:COMPUTEDEPTHS)' {
+        memory = { 500.MB * task.attempt }
+    }
+
+    withName: '(BBGTOOLS:DEEPCSA:MUTPROFILE.*:COMPUTEPROFILE)' {
+        memory = { 1.GB * task.attempt }
+    }
+
+    // === UTILITY PROCESSES ===
+    withName: 'BBGTOOLS:DEEPCSA:CUSTOM_DUMPSOFTWAREVERSIONS*' {
+        cache = false
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:CREATEPANELS:SORTPANELRICH' {
+        memory = { 512.MB * task.attempt }  
+        time = { 3.h * task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:CREATEPANELS:SORTPANELCOMPACT' {
+        memory = { 512.MB * task.attempt }  
+        time = { 3.h * task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:ENRICHPANELS:DNA2PROTEINMAPPING' {
+        memory = { 948.GB * task.attempt }
+        errorStrategy = 'ignore'
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:PLOTDEPTHSEXONSCONS:DEPTHSSUMMARY' {
+        memory = { 948.GB * task.attempt }  
+        errorStrategy = 'ignore' 
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:PLOTTINGSUMMARY:PLOTSATURATIONPROPORTIONS' {
+        memory = {764.GB * task.attempt }
+        time   = {  24.h   * task.attempt }
+        errorStrategy = 'ignore' 
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:CREATEPANELS:CREATECONSENSUSPANELSSYNONYMOUS' {
+        memory = { 40.GB * task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:CREATEPANELS:DOMAINANNOTATION' {
+        memory = { 300.GB * task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:CREATEPANELS:CREATECONSENSUSPANELSALL' {
+        memory = { 256.GB * task.attempt }
+        cpus = {  4* task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:CREATEPANELS:CREATECONSENSUSPANELSNONPROTAFFECT' {
+        memory = { 200.GB * task.attempt }
+        time   = {  1.h   * task.attempt }
+
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:CREATEPANELS:POSTPROCESSVEPPANEL' {
+        memory = { 52.GB * task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:CREATEPANELS:CREATECONSENSUSPANELSINTRONS' {
+        memory = { 200.GB * task.attempt }
+        time   = {  1.h   * task.attempt }  
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:CREATEPANELS:COMPAREPANELPROPORTIONS' {
+        memory = { 64.GB * task.attempt }
+        time   = {  1.h   * task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:ONCODRIVE3D:ONCODRIVE3D_RUN' {
+        memory = { 8.GB * task.attempt }  
+        time = { 3.h * task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:CREATEPANELS:CREATECONSENSUSPANELSPROTAFFECT' {
+        memory = { 64.GB * task.attempt }  
+        time = { 3.h * task.attempt }
+    }
+
+    withName:'BBGTOOLS:DEEPCSA:CREATEPANELS:CREATECONSENSUSPANELSEXONS' {
+        memory = { 96.GB * task.attempt }  
+        time = { 3.h * task.attempt }
+        cpus   = { 2 * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:FILTEREXONS' {
+        cpus = { 1* task.attempt }
+        memory = { 8.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:FILTERPANEL' {
+        cpus = { 1* task.attempt }
+        memory = { 16.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:FILTERNANOSEQSNP' {
+        cpus = { 1* task.attempt }
+        memory = { 10.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:FILTERNANOSEQNOISE' {
+        cpus = { 1* task.attempt }
+        memory = { 16.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:WRITEMAF' {
+        cpus = { 1* task.attempt }
+        memory = { 6.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:CLEANMUTATIONS' {
+        cpus = { 1* task.attempt }
+        memory = { 6.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:MUTPROFILE.*:COMPUTETRINUC' {
+        memory = { 12.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:DNDS:PREPROCESSDEPTHS' {
+        memory = { 40.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:DNDS:DNDSRUN' {
+        memory = { 5.GB * task.attempt }
+    }
+
+    withName:  'RELATIVEMUTABILITY.*' {
+        memory = { 50.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:ONCODRIVEFMLALL:ONCODRIVEFMLSNVS' {
+        memory = { 64.GB * task.attempt }
+        time   = {  32.h   * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:PLOTTINGQC:PLOTMUTATIONSPECIFIC' {
+        memory = { 32.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:ONCODRIVE3D:ONCODRIVE3D_PREPROCESSING' {
+        memory = { 8.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:MERGEBATCH' {
+        memory = { 4.GB * task.attempt }
+    }
+
+    withName:  'BBGTOOLS:DEEPCSA:MUT_PREPROCESSING:FILTERBATCH ' {
+        cpus = { 1* task.attempt }
+        memory = { 8.GB * task.attempt }
+        time   = {  1.h   * task.attempt }
+  }
+
+    
+    
+    
+    
+    }
\ No newline at end of file
diff --git a/conf/modules.config b/conf/modules.config
index 5670d8bd..04ae8fa4 100644
--- a/conf/modules.config
+++ b/conf/modules.config
@@ -241,6 +241,10 @@ process {
         ext.withingene = params.plot_depths
     }
 
+    withName: PLOTMUTATIONSPECIFIC {
+        ext.when = params.plot_mutation_specific_qc
+    }
+
     withName: '.*PLOTDEPTHSALLCONS:DEPTHSSUMMARY' {
         ext.prefix = { ".all_cons" }
     }
@@ -719,6 +723,10 @@ process {
     withLabel : bbgregressions {
         container = "docker.io/rblancomi/bbgregressions:dev"
     }
+
+    withName: SITESFROMPOSITIONS {
+        ext.chunk_size = params.panel_sites_chunk_size ?: 0
+    }
 }
 
 includeConfig 'tools/panels.config'
diff --git a/modules/local/annotatedepth/main.nf b/modules/local/annotatedepth/main.nf
index 65151da3..9cb82455 100644
--- a/modules/local/annotatedepth/main.nf
+++ b/modules/local/annotatedepth/main.nf
@@ -1,7 +1,5 @@
 process ANNOTATE_DEPTHS {
     tag "${meta.id}"
-    label 'process_low'
-    label 'time_low'
 
     label 'deepcsa_core'
     
diff --git a/modules/local/bbgtools/omega/preprocess/main.nf b/modules/local/bbgtools/omega/preprocess/main.nf
index 82972a56..03ab46c3 100644
--- a/modules/local/bbgtools/omega/preprocess/main.nf
+++ b/modules/local/bbgtools/omega/preprocess/main.nf
@@ -1,9 +1,5 @@
 process OMEGA_PREPROCESS {
     tag "$meta.id"
-    label 'cpu_single_fixed'
-    label 'time_low'
-    label 'process_high_memory'
-
 
     container 'docker.io/bbglab/omega:0.2.1'
 
diff --git a/modules/local/combine_sbs/main.nf b/modules/local/combine_sbs/main.nf
index 89e63a4c..0e1b8e3f 100644
--- a/modules/local/combine_sbs/main.nf
+++ b/modules/local/combine_sbs/main.nf
@@ -1,7 +1,6 @@
 process SIGNATURES_PROBABILITIES {
 
     tag "${meta.id}"
-    label 'process_low'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/computedepths/main.nf b/modules/local/computedepths/main.nf
index 1f4135dd..6279a636 100644
--- a/modules/local/computedepths/main.nf
+++ b/modules/local/computedepths/main.nf
@@ -1,6 +1,5 @@
 process COMPUTEDEPTHS {
     tag "$meta.id"
-    label 'process_high'
 
     conda "${moduleDir}/environment.yml"
     container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
diff --git a/modules/local/computemutdensity/main.nf b/modules/local/computemutdensity/main.nf
index 78d1fc0d..2e13ee83 100644
--- a/modules/local/computemutdensity/main.nf
+++ b/modules/local/computemutdensity/main.nf
@@ -1,6 +1,5 @@
 process MUTATION_DENSITY {
     tag "$meta.id"
-    label 'process_single'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/createpanels/captured/main.nf b/modules/local/createpanels/captured/main.nf
index a8779227..8b0f886c 100644
--- a/modules/local/createpanels/captured/main.nf
+++ b/modules/local/createpanels/captured/main.nf
@@ -1,11 +1,11 @@
 process CREATECAPTUREDPANELS {
     tag "$meta.id"
-    label 'process_single'
-    label 'process_medium_high_memory'
-
-    container "community.wave.seqera.io/library/bedtools_pybedtools_pandas_pip_pruned:78080da05d53636d"
-
 
+    conda "python=3.10.17 bioconda::pybedtools=0.12.0 conda-forge::polars=1.30.0 conda-forge::click=8.2.1 conda-forge::gcc_linux-64=15.1.0 conda-forge::gxx_linux-64=15.1.0"
+    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
+        'docker://bbglab/deepcsa_bed:latest' :
+        'bbglab/deepcsa_bed:latest' }"
+        
     input:
     tuple val(meta), path(compact_captured_panel_annotation)
 
@@ -36,7 +36,8 @@ process CREATECAPTUREDPANELS {
         bedtools merge \\
             -i <(
                 tail -n +2 \$captured_panel | \\
-                awk -F'\\t' '{print \$1, \$2-1, \$2}' OFS='\\t' | uniq
+                awk -F'\\t' '{print \$1, \$2-1, \$2}' OFS='\\t' | \\
+                sort -k1,1 -k2,2n | uniq
             ) > \${captured_panel%.tsv}.bed;
     done
 
diff --git a/modules/local/createpanels/consensus/main.nf b/modules/local/createpanels/consensus/main.nf
index 42d9701b..b93f1d0e 100644
--- a/modules/local/createpanels/consensus/main.nf
+++ b/modules/local/createpanels/consensus/main.nf
@@ -1,6 +1,5 @@
 process CREATECONSENSUSPANELS {
     tag "$meta.id"
-    label 'process_single'
 
     conda "python=3.10.17 bioconda::pybedtools=0.12.0 conda-forge::polars=1.30.0 conda-forge::click=8.2.1 conda-forge::gcc_linux-64=15.1.0 conda-forge::gxx_linux-64=15.1.0"
     container 'docker://bbglab/deepcsa_bed:latest'
diff --git a/modules/local/dna2protein/main.nf b/modules/local/dna2protein/main.nf
index 12b20423..9e67d616 100644
--- a/modules/local/dna2protein/main.nf
+++ b/modules/local/dna2protein/main.nf
@@ -1,7 +1,5 @@
 process DNA_2_PROTEIN_MAPPING {
     tag "$meta.id"
-    label 'process_single'
-    label 'process_medium_high_memory'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/filterbed/main.nf b/modules/local/filterbed/main.nf
index d9049f4c..fed28bd6 100644
--- a/modules/local/filterbed/main.nf
+++ b/modules/local/filterbed/main.nf
@@ -1,7 +1,6 @@
 process FILTERBED {
 
     tag "$meta.id"
-    label 'process_high'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/filtermaf/main.nf b/modules/local/filtermaf/main.nf
index 7c0f8a98..cd97c876 100644
--- a/modules/local/filtermaf/main.nf
+++ b/modules/local/filtermaf/main.nf
@@ -1,9 +1,6 @@
 process FILTER_BATCH {
     tag "$meta.id"
 
-    label 'process_high_memory'
-    label 'time_low'
-
     label 'deepcsa_core'
 
     input:
diff --git a/modules/local/group_genes/main.nf b/modules/local/group_genes/main.nf
index 7f174f9e..cf1aee15 100644
--- a/modules/local/group_genes/main.nf
+++ b/modules/local/group_genes/main.nf
@@ -1,6 +1,5 @@
 process GROUP_GENES {
     tag "groups"
-    label 'process_low'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/mergemafs/main.nf b/modules/local/mergemafs/main.nf
index df0e1731..ad189f92 100644
--- a/modules/local/mergemafs/main.nf
+++ b/modules/local/mergemafs/main.nf
@@ -1,9 +1,6 @@
 process MERGE_BATCH {
     tag "$meta.id"
 
-    label 'process_high_memory'
-    label 'time_low'
-
     label 'deepcsa_core'
 
     input:
diff --git a/modules/local/mutations2sbs/main.nf b/modules/local/mutations2sbs/main.nf
index 5617bdd6..0ddf149d 100644
--- a/modules/local/mutations2sbs/main.nf
+++ b/modules/local/mutations2sbs/main.nf
@@ -1,7 +1,6 @@
 process MUTATIONS_2_SIGNATURES {
 
     tag "${meta.id}"
-    label 'process_low'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/plot/depths_summary/main.nf b/modules/local/plot/depths_summary/main.nf
index 085ea8fa..f9c475c4 100644
--- a/modules/local/plot/depths_summary/main.nf
+++ b/modules/local/plot/depths_summary/main.nf
@@ -1,9 +1,6 @@
 process PLOT_DEPTHS {
 
     tag "$meta.id"
-    label 'process_single'
-    label 'time_low'
-    label 'process_high_memory'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/plot/mutations_summary/main.nf b/modules/local/plot/mutations_summary/main.nf
index deeb12b7..330dd9a1 100644
--- a/modules/local/plot/mutations_summary/main.nf
+++ b/modules/local/plot/mutations_summary/main.nf
@@ -1,7 +1,6 @@
 process PLOT_MUTATIONS {
 
     tag "$meta.id"
-    label 'process_low'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/plot/needles/main.nf b/modules/local/plot/needles/main.nf
index a08092b6..bd0fbe90 100644
--- a/modules/local/plot/needles/main.nf
+++ b/modules/local/plot/needles/main.nf
@@ -1,7 +1,6 @@
 process PLOT_NEEDLES {
 
     tag "$meta.id"
-    label 'process_low'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/plot/qc/mutation_specific/main.nf b/modules/local/plot/qc/mutation_specific/main.nf
index 90c71d05..d2cf97a1 100644
--- a/modules/local/plot/qc/mutation_specific/main.nf
+++ b/modules/local/plot/qc/mutation_specific/main.nf
@@ -13,6 +13,9 @@ process PLOT_MUTATION_SPECIFIC {
     path("**.tsv")      , optional : true,  emit: tables
     path "versions.yml" , topic: versions
 
+    when:
+    task.ext.when == null || task.ext.when
+
     script:
     def prefix = task.ext.prefix ?: ""
     prefix = "${meta.id}${prefix}"
diff --git a/modules/local/process_annotation/domain/main.nf b/modules/local/process_annotation/domain/main.nf
index 92e34593..0645dcfc 100644
--- a/modules/local/process_annotation/domain/main.nf
+++ b/modules/local/process_annotation/domain/main.nf
@@ -2,10 +2,6 @@ process DOMAIN_ANNOTATION {
 
     tag "${meta.id}"
 
-    label 'cpu_low'
-    label 'time_low'
-    label 'process_high_memory'
-
     label 'deepcsa_core'
 
     input:
diff --git a/modules/local/process_annotation/mutations/main.nf b/modules/local/process_annotation/mutations/main.nf
index 82a1d394..d5f063d5 100644
--- a/modules/local/process_annotation/mutations/main.nf
+++ b/modules/local/process_annotation/mutations/main.nf
@@ -1,10 +1,6 @@
 process SUMMARIZE_ANNOTATION {
     tag "$meta.id"
 
-    label 'cpu_low'
-    label 'process_high_memory'
-    label 'time_low'
-
     label 'deepcsa_core'
 
     input:
diff --git a/modules/local/process_annotation/mutations_custom/main.nf b/modules/local/process_annotation/mutations_custom/main.nf
index 1959dbb6..1eb8127e 100644
--- a/modules/local/process_annotation/mutations_custom/main.nf
+++ b/modules/local/process_annotation/mutations_custom/main.nf
@@ -1,10 +1,6 @@
 process CUSTOM_MUTATION_PROCESSING {
     tag "$meta.id"
 
-    label 'cpu_low'
-    label 'process_high_memory'
-    label 'time_low'
-
     label 'deepcsa_core'
 
     input:
diff --git a/modules/local/process_annotation/panel/main.nf b/modules/local/process_annotation/panel/main.nf
index 93088f7e..29f97a06 100644
--- a/modules/local/process_annotation/panel/main.nf
+++ b/modules/local/process_annotation/panel/main.nf
@@ -2,10 +2,6 @@ process POSTPROCESS_VEP_ANNOTATION {
 
     tag "${meta.id}"
 
-    label 'cpu_low'
-    label 'time_low'
-    label 'process_high_memory'
-
     label 'deepcsa_core'
 
 
diff --git a/modules/local/process_annotation/panelcustom/main.nf b/modules/local/process_annotation/panelcustom/main.nf
index e680ef8e..f1f81bcb 100644
--- a/modules/local/process_annotation/panelcustom/main.nf
+++ b/modules/local/process_annotation/panelcustom/main.nf
@@ -2,10 +2,6 @@ process CUSTOM_ANNOTATION_PROCESSING {
 
     tag "${meta.id}"
 
-    label 'cpu_low'
-    label 'time_low'
-    label 'process_high_memory'
-
     label 'deepcsa_core'
 
     input:
diff --git a/modules/local/samplesheet_check.nf b/modules/local/samplesheet_check.nf
index 6fdb517e..7cc15291 100644
--- a/modules/local/samplesheet_check.nf
+++ b/modules/local/samplesheet_check.nf
@@ -1,6 +1,5 @@
 process SAMPLESHEET_CHECK {
     tag "$samplesheet"
-    label 'process_single'
 
     conda "conda-forge::python=3.8.3"
     container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
diff --git a/modules/local/select_mutdensity/main.nf b/modules/local/select_mutdensity/main.nf
index 1c155684..10abbd8d 100644
--- a/modules/local/select_mutdensity/main.nf
+++ b/modules/local/select_mutdensity/main.nf
@@ -1,6 +1,5 @@
 process SELECT_MUTDENSITIES {
     tag "$meta.id"
-    label 'process_single'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/sig_matrix_concat/main.nf b/modules/local/sig_matrix_concat/main.nf
index ba1ba8fa..df774987 100644
--- a/modules/local/sig_matrix_concat/main.nf
+++ b/modules/local/sig_matrix_concat/main.nf
@@ -1,6 +1,5 @@
 process MATRIX_CONCAT {
     tag "$meta.id"
-    label 'process_low'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/sitesfrompositions/main.nf b/modules/local/sitesfrompositions/main.nf
index 6f7b58be..693425b0 100644
--- a/modules/local/sitesfrompositions/main.nf
+++ b/modules/local/sitesfrompositions/main.nf
@@ -2,23 +2,19 @@ process SITESFROMPOSITIONS {
 
     tag "${meta.id}"
 
-    label 'cpu_single'
-    label 'time_low'
-    label 'process_low_memory'
-
     label 'deepcsa_core'
 
-
     input:
     tuple val(meta), path(depths)
 
     output:
-    tuple val(meta), path("*.sites4VEP.tsv")  , emit: annotated_panel_reg
-    path  "versions.yml"                      , topic: versions
+    tuple val(meta), path("*.sites4VEP.chunk*.tsv")  , emit: annotated_panel_reg
+    path  "versions.yml"                             , topic: versions
 
 
     script:
     def assembly = task.ext.assembly ?: "hg38"
+    def chunk_size = task.ext.chunk_size ?: 0
 
     // TODO
     // see if there is a better way to filter out chromosomes
@@ -30,11 +26,11 @@ process SITESFROMPOSITIONS {
     sites_table_from_positions.py \\
         --input-positions captured_positions.tsv \\
         --genome-assembly ${assembly} \\
-        --output-file-with-sites captured_positions.sites4VEP.tmp.tsv;
+        --output-prefix captured_positions.sites4VEP \\
+        --chunk-size ${chunk_size};
 
     rm captured_positions.tsv
 
-    awk '{print "chr"\$0}' captured_positions.sites4VEP.tmp.tsv > captured_positions.sites4VEP.tsv
     cat <<-END_VERSIONS > versions.yml
     "${task.process}":
         python: \$(python --version | sed 's/Python //g')
diff --git a/modules/local/sortpanel/main.nf b/modules/local/sortpanel/main.nf
new file mode 100644
index 00000000..e7dc683f
--- /dev/null
+++ b/modules/local/sortpanel/main.nf
@@ -0,0 +1,37 @@
+process SORT_MERGED_PANEL {
+
+    tag "${meta.id}"
+
+    container "docker.io/bbglab/deepcsa-core:0.0.2-alpha"
+
+    input:
+    tuple val(meta), path(panel)
+
+    output:
+    tuple val(meta), path("*.sorted.tsv") , emit: sorted
+    path "versions.yml"                   , topic: versions
+
+    script:
+    // Sort by chromosome (field 1) and position (field 2). Assumes header in first line.
+    // Using version sort for chromosome (handles chr1 chr2 chr10) after stripping 'chr' if present.
+    """
+    echo "[SORT_MERGED_PANEL] Sorting panel for ${meta.id}"
+    head -n 1 ${panel} > sorted.tmp
+    tail -n +2 ${panel} | awk 'BEGIN{OFS="\\t"} {sub(/^chr/,"",\$1); print}' | sort -k1,1V -k2,2n | awk 'BEGIN{OFS="\\t"} {print "chr"\$0}' >> sorted.tmp
+    mv sorted.tmp ${panel.getBaseName()}.sorted.tsv
+
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        bash: \$(bash --version | head -n 1 | sed 's/^.*version //; s/ .*//')
+    END_VERSIONS
+    """
+
+    stub:
+    """
+    touch ${panel.getBaseName()}.sorted.tsv
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        bash: \$(bash --version | head -n 1 | sed 's/^.*version //; s/ .*//')
+    END_VERSIONS
+    """
+}
diff --git a/modules/local/subsetmaf/main.nf b/modules/local/subsetmaf/main.nf
index 380bfa40..ed9467ef 100644
--- a/modules/local/subsetmaf/main.nf
+++ b/modules/local/subsetmaf/main.nf
@@ -1,7 +1,6 @@
 process SUBSET_MAF {
 
     tag "$meta.id"
-    label 'process_low'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/table2groups/main.nf b/modules/local/table2groups/main.nf
index b85aafb5..8a8159a4 100644
--- a/modules/local/table2groups/main.nf
+++ b/modules/local/table2groups/main.nf
@@ -1,7 +1,5 @@
 process TABLE_2_GROUP {
-
     tag "groups"
-    label 'process_low'
 
     label 'deepcsa_core'
 
diff --git a/modules/local/vcf2maf/main.nf b/modules/local/vcf2maf/main.nf
index 287696c9..a05f01ae 100644
--- a/modules/local/vcf2maf/main.nf
+++ b/modules/local/vcf2maf/main.nf
@@ -1,9 +1,6 @@
 process VCF2MAF {
     tag "$meta.id"
 
-    label 'cpu_low'
-    label 'process_high_memory'
-
     label 'deepcsa_core'
 
     input:
diff --git a/modules/local/writemaf/main.nf b/modules/local/writemaf/main.nf
index 3aef4415..f7f2404d 100644
--- a/modules/local/writemaf/main.nf
+++ b/modules/local/writemaf/main.nf
@@ -1,8 +1,7 @@
 process WRITE_MAFS {
 
     tag "${meta.id}"
-    label 'process_high_memory'
-
+    
     label 'deepcsa_core'
 
     input:
diff --git a/modules/nf-core/ensemblvep/vep/main.nf b/modules/nf-core/ensemblvep/vep/main.nf
index ef7a2a1a..e7da9e22 100644
--- a/modules/nf-core/ensemblvep/vep/main.nf
+++ b/modules/nf-core/ensemblvep/vep/main.nf
@@ -1,6 +1,5 @@
 process ENSEMBLVEP_VEP {
     tag "$meta.id"
-    label 'process_high'
 
     conda params.vep_cache_version == 108 ? 'bioconda::ensembl-vep=108.2' : 
             params.vep_cache_version == 102 ? 'bioconda::ensembl-vep=102.0' :  
diff --git a/modules/nf-core/ensemblvep/veppanel/main.nf b/modules/nf-core/ensemblvep/veppanel/main.nf
index 6ee8b5d1..eea60f95 100644
--- a/modules/nf-core/ensemblvep/veppanel/main.nf
+++ b/modules/nf-core/ensemblvep/veppanel/main.nf
@@ -43,10 +43,18 @@ process ENSEMBLVEP_VEP {
     def reference = fasta ? "--fasta $fasta" : ""
 
     """
+
+    # Convert input TSV to VEP format, to make vep --fork more efficient
+    awk 'BEGIN { OFS="\t" }
+    {
+     split(\$4, a, "/");
+     print \$1, \$2, ".", a[1], a[2];
+    }' ${vcf} > ${vcf}.vep
+
     # this is to ensure that we will be able to match the tab and vcf files afterwards
     # the structure of the ID is the following:
     vep \\
-        -i ${vcf} \\
+        -i ${vcf}.vep \\
         -o ${prefix}.${file_extension}.gz \\
         $args \\
         $compress_cmd \\
@@ -79,4 +87,4 @@ process ENSEMBLVEP_VEP {
         ensemblvep: \$( echo \$(vep --help 2>&1) | sed 's/^.*Versions:.*ensembl-vep : //;s/ .*\$//')
     END_VERSIONS
     """
-}
\ No newline at end of file
+}
diff --git a/modules/nf-core/multiqc/main.nf b/modules/nf-core/multiqc/main.nf
index 783d1027..ee0f3097 100644
--- a/modules/nf-core/multiqc/main.nf
+++ b/modules/nf-core/multiqc/main.nf
@@ -1,5 +1,4 @@
 process MULTIQC {
-    label 'process_single'
 
     conda "bioconda::multiqc=1.20"
     container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
diff --git a/modules/nf-core/tabix/bgziptabixquery/main.nf b/modules/nf-core/tabix/bgziptabixquery/main.nf
index 5b58af7b..7942d322 100644
--- a/modules/nf-core/tabix/bgziptabixquery/main.nf
+++ b/modules/nf-core/tabix/bgziptabixquery/main.nf
@@ -3,8 +3,6 @@ process TABIX_BGZIPTABIX_QUERY {
     // cache false
     
     tag "$meta.id"
-    label 'process_high'
-    label 'process_high_memory'
 
     conda "${moduleDir}/environment.yml"
     container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
diff --git a/nextflow.config b/nextflow.config
index 9c503dac..c485f68b 100644
--- a/nextflow.config
+++ b/nextflow.config
@@ -81,6 +81,7 @@ params {
     confidence_level                         = 'med'
     pileup_all_duplex                        = false
     plot_depths                              = false
+    plot_mutation_specific_qc                = true
     store_depths                             = false
 
     use_custom_depths                        = false
@@ -107,6 +108,7 @@ params {
     min_muts_per_sample                      = 0
     selected_genes                           = ''
     panel_with_canonical                     = true
+    panel_sites_chunk_size                   = 1000000  // 0 means no chunking (default), set to positive integer to enable chunking
 
     germline_threshold                       = 0.3
     mutation_depth_threshold                 = 100
@@ -183,9 +185,9 @@ params {
 
     // Max resource options
     // Defaults only, expecting to be overwritten
-    max_memory                  = 256.GB
-    max_cpus                    = 56
-    max_time                    = 240.h
+    max_memory                  = 950.GB
+    max_cpus                    = 196
+    max_time                    = 30.d
 
     validate_params             = true
 }
@@ -323,6 +325,10 @@ profiles {
     mice {
         includeConfig 'conf/mice.config'
     }
+
+    exome {
+        includeConfig 'conf/exome.config'
+    }
 }
 
 
diff --git a/nextflow_schema.json b/nextflow_schema.json
index a172e69d..68661011 100644
--- a/nextflow_schema.json
+++ b/nextflow_schema.json
@@ -310,6 +310,13 @@
                     "description": "Do you want to plot the depths within each gene?",
                     "fa_icon": "fas fa-book"
                 },
+                "plot_mutation_specific_qc": {
+                    "type": "boolean",
+                    "description": "Do you want to generate mutation-specific QC plots (VAF vs depth)?",
+                    "fa_icon": "fas fa-book",
+                    "hidden": true,
+                    "default": true
+                },
                 "oncodrivefml": {
                     "type": "boolean",
                     "description": "Do you want to run oncodrivefml?",
@@ -527,6 +534,21 @@
                 }
             }
         },
+        "parallel_processing_parameters": {
+            "title": "Parallel processing and chunking options",
+            "type": "object",
+            "fa_icon": "fas fa-tasks",
+            "description": "Parameters to control parallel processing, chunking, and memory management during panel creation and annotation.",
+            "properties": {
+                "panel_sites_chunk_size": {
+                    "type": "integer",
+                    "description": "Number of sites per chunk for parallel VEP annotation (0 = no chunking)",
+                    "default": 1000000,
+                    "fa_icon": "fas fa-cut",
+                    "help_text": "When set to a positive integer, splits the sites file into chunks for parallel processing through VEP annotation. Set to 0 to disable chunking (process as single file). Recommended values: 100000-500000 for large datasets."
+                }
+            }
+        },
         "filtering_parameters": {
             "title": "Profile computation options",
             "type": "object",
@@ -1080,6 +1102,9 @@
         {
             "$ref": "#/$defs/profile_computation_config"
         },
+        {
+            "$ref": "#/$defs/parallel_processing_parameters"
+        },
         {
             "$ref": "#/$defs/filtering_parameters"
         },
diff --git a/subworkflows/local/createpanels/main.nf b/subworkflows/local/createpanels/main.nf
index c71c4d5a..0d059296 100644
--- a/subworkflows/local/createpanels/main.nf
+++ b/subworkflows/local/createpanels/main.nf
@@ -1,7 +1,9 @@
 include { SITESFROMPOSITIONS                                                        } from '../../../modules/local/sitesfrompositions/main'
 include { VCF_ANNOTATE_ENSEMBLVEP       as VCFANNOTATEPANEL                         } from '../../../subworkflows/nf-core/vcf_annotate_ensemblvep_panel/main'
 
-include { POSTPROCESS_VEP_ANNOTATION    as POSTPROCESSVEPPANEL                      } from '../../../modules/local/process_annotation/panel/main'
+include { POSTPROCESS_VEP_ANNOTATION    as POSTPROCESSVEPPANEL          } from '../../../modules/local/process_annotation/panel/main'
+include { SORT_MERGED_PANEL              as SORTPANELCOMPACT             } from '../../../modules/local/sortpanel/main'
+include { SORT_MERGED_PANEL              as SORTPANELRICH                } from '../../../modules/local/sortpanel/main'
 
 include { CUSTOM_ANNOTATION_PROCESSING  as CUSTOMPROCESSING                         } from '../../../modules/local/process_annotation/panelcustom/main'
 include { CUSTOM_ANNOTATION_PROCESSING  as CUSTOMPROCESSINGRICH                     } from '../../../modules/local/process_annotation/panelcustom/main'
@@ -38,10 +40,16 @@ workflow CREATE_PANELS {
     // Create all possible sites and mutations per site of the captured panel
     SITESFROMPOSITIONS(depths)
 
-    // Create a tuple for VEP annotation (mandatory)
-    SITESFROMPOSITIONS.out.annotated_panel_reg.map{ it -> [[ id : "captured_panel"],  it[1]] }.set{ sites_annotation }
+    // Flatten chunks and create tuples for VEP annotation
+    SITESFROMPOSITIONS.out.annotated_panel_reg
+        .transpose()
+        .map{ _meta, chunk -> 
+            def chunk_id = chunk.name.tokenize('.').find{ token -> token.startsWith('chunk') }
+            [[ id : "captured_panel_${chunk_id}"], chunk] 
+        }
+        .set{ sites_annotation }
 
-    // Annotate all possible mutations in the captured panel
+    // Annotate all possible mutations in the captured panel (per chunk)
     VCFANNOTATEPANEL(sites_annotation,
                     params.fasta,
                     params.vep_genome,
@@ -50,24 +58,44 @@ workflow CREATE_PANELS {
                     params.vep_cache,
                     [])
 
-    // Postprocess annotations to get one annotation per mutation
+    // Postprocess annotations to get one annotation per mutation (per chunk)
     POSTPROCESSVEPPANEL(VCFANNOTATEPANEL.out.tab)
 
+    // Collect and merge all chunks using collectFile
+    POSTPROCESSVEPPANEL.out.compact_panel_annotation
+        .map{ it -> it[1] }
+        .collectFile(name: 'captured_panel.vep.annotation.tsv', keepHeader: true, skip: 1)
+        .map{ file -> [[ id : "captured_panel"], file] }
+        .set{ merged_compact_unsorted }
+
+    POSTPROCESSVEPPANEL.out.rich_panel_annotation
+        .map{ it -> it[1] }
+        .collectFile(name: 'captured_panel.vep.annotation.rich.tsv', keepHeader: true, skip: 1)
+        .map{ file -> [[ id : "captured_panel"], file] }
+        .set{ merged_rich_unsorted }
+
+    // Sort merged panels to ensure genomic order
+    SORTPANELCOMPACT(merged_compact_unsorted)
+    SORTPANELRICH(merged_rich_unsorted)
+
+    merged_compact = SORTPANELCOMPACT.out.sorted
+    merged_rich = SORTPANELRICH.out.sorted
+
     if (params.customize_annotation) {
         custom_annotation_tsv = file(params.custom_annotation_tsv, checkIfExists: true)
 
         // Update specific regions based on user preferences
-        CUSTOMPROCESSING(POSTPROCESSVEPPANEL.out.compact_panel_annotation, custom_annotation_tsv)
+        CUSTOMPROCESSING(merged_compact, custom_annotation_tsv)
         complete_annotated_panel = CUSTOMPROCESSING.out.custom_panel_annotation
 
-        CUSTOMPROCESSINGRICH(POSTPROCESSVEPPANEL.out.rich_panel_annotation, custom_annotation_tsv)
+        CUSTOMPROCESSINGRICH(merged_rich, custom_annotation_tsv)
         rich_annotated = CUSTOMPROCESSINGRICH.out.custom_panel_annotation
 
         added_regions = CUSTOMPROCESSINGRICH.out.added_regions
 
     } else {
-        complete_annotated_panel = POSTPROCESSVEPPANEL.out.compact_panel_annotation
-        rich_annotated = POSTPROCESSVEPPANEL.out.rich_panel_annotation
+        complete_annotated_panel = merged_compact
+        rich_annotated = merged_rich
         added_regions = channel.empty()
     }
 
@@ -147,6 +175,6 @@ workflow CREATE_PANELS {
     domains_panel_bed           = DOMAINANNOTATION.out.domains_bed.first()
     domains_in_panel            = DOMAINANNOTATION.out.domains_tsv.first()
 
-    postprocessed_panel         = POSTPROCESSVEPPANEL.out.compact_panel_annotation.first()
-    postprocessed_panel_rich    = POSTPROCESSVEPPANEL.out.rich_panel_annotation.first()
+    postprocessed_panel         = merged_compact.first()
+    postprocessed_panel_rich    = merged_rich.first()
 }
diff --git a/tests/deepcsa.nf.test b/tests/deepcsa.nf.test
index 5b54a022..6bf7c50b 100644
--- a/tests/deepcsa.nf.test
+++ b/tests/deepcsa.nf.test
@@ -1,132 +1,141 @@
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    TEST SUITE: deepCSA Pipeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    Description: Comprehensive test suite for the deepCSA pipeline
-    
-    Purpose: Validates pipeline functionality across different input configurations
-    and parameter combinations to ensure robust variant calling performance.
-    
-    Test Categories:
-    1. BASIC FUNCTIONALITY
-       - Normal run: Standard single-file processing
-    
-    2. OMEGA ANALYSIS
-       - Run with omega analysis enabled
-    
-    3. MAF INPUT VALIDATION
-       - Validate that pipeline fails when --input_maf is provided without --use_custom_depths
-    
-    
-    Test Data: Located in tests/test_data/input/
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*/
-
 nextflow_pipeline {
     name "Test DEEPCSA Pipeline"
     script "main.nf"
     config "./nextflow.config"
 
-        /*
-    ========================================================================================
-        TEST 1: BASIC FUNCTIONALITY - Standard Processing
-    ========================================================================================
-    Purpose: Validates core pipeline functionality with default parameters
-    
-    Input: MAF file and depth table for all samples.
-    Parameters: 
-        - plot_depths = true (enables depth plotting)
-        - profileall  = true (enables mutational profile generation)
-        - signatures = false (disables signature analysis, should not affect other outputs)
-    
-    Expected Behavior:
-        - Processes each VCF and BAM file correctly
-        - Generates mutational profiles and depth plots
-        - Does not generate outputs for features that are disabled (e.g. mutdensity, omega, oncodrivefml, oncodrive3d)
-    
-    Success Criteria:
-        - Pipeline completes successfully without errors
-        - Mutational profile output matches expected snapshot
-    ========================================================================================
-    */
-
-
-    test("TEST 1. Basic functionality - MAF-based processing") {
+
+    test("Minimal features test run") {
         tag "normal"
         
         when {
             params {
-                // Input: Sample name metadata for the pipeline
                 input = "${projectDir}/tests/test_data/input.csv"
-                outdir = "$outputDir" // nf-test built-in: unique temp dir per test run
+                outdir = "${projectDir}/tests_results_normal"
                 wgs_trinuc_counts = "${projectDir}/assets/trinucleotide_counts/trinuc_counts.homo_sapiens.tsv"
-                // Test data fetched directly from the DeepClone_protocol repository
+                plot_depths = true
+                plot_mutation_specific_qc = false
+                profileall  = true
+                signatures = false
+                cosmic_ref_signatures = null
+                indel_ref_signatures = null
+            }
+        }
+
+        then {
+            assert workflow.success : "Pipeline should complete without errors"
+            assert path("${params.outdir}/mutational_profile").exists()
+            assert !path("${params.outdir}/mutdensity").exists()
+            assert !path("${params.outdir}/selection/omega").exists()
+            assert !path("${params.outdir}/oncodrivefml").exists()
+            assert !path("${params.outdir}/oncodrive3d").exists()            
+        }
+    }
+
+    test("MAF-based minimal features test run") {
+        tag "normal_maf"
+
+        when {
+            params {
+                input               = "${projectDir}/tests/test_data/input_maf.csv"
+                outdir              = "$outputDir"
+                wgs_trinuc_counts   = "${projectDir}/assets/trinucleotide_counts/trinuc_counts.homo_sapiens.tsv"
                 input_maf           = 'https://raw.githubusercontent.com/bbglab/DeepClone_protocol/main/test_datasets/deepCSA/testdata/maf/all_samples.somatic.mutations.maf'
                 use_custom_depths   = true
                 custom_depths_table = 'https://raw.githubusercontent.com/bbglab/DeepClone_protocol/main/test_datasets/deepCSA/testdata/depth/all_samples_indv.depths.tsv.gz'
                 plot_depths = true
+                plot_mutation_specific_qc = false
                 profileall  = true
                 signatures = false
+                cosmic_ref_signatures = null
+                indel_ref_signatures = null
             }
         }
 
         then {
             assert workflow.success : "Pipeline should complete without errors"
-            // Check output directory structure
-            assert path("${outputDir}/mutational_profile").exists()
-            // Check that directories that should not exist in minimal features run, in fact do not exist.
-            assert !path("${outputDir}/mutdensity").exists()
-            assert !path("${outputDir}/omega").exists()
-            assert !path("${outputDir}/oncodrivefml").exists()
-            assert !path("${outputDir}/oncodrive3d").exists()  
-
-            // Snapshot of stable output          
+            assert path("${params.outdir}/mutational_profile").exists()
+            assert !path("${params.outdir}/mutdensity").exists()
+            assert !path("${params.outdir}/selection/omega").exists()
+            assert !path("${params.outdir}/oncodrivefml").exists()
+            assert !path("${params.outdir}/oncodrive3d").exists()
+
             assert snapshot(
-                path("${outputDir}/mutational_profile/all_samples.all.profile.tsv")
+                path("${params.outdir}/mutational_profile/all_samples.all.profile.tsv")
             ).match()
         }
     }
 
-    /*
-    ========================================================================================
-        TEST 2: OMEGA ANALYSIS - Enabling Omega for driver gene analysis
-    ========================================================================================
-    Purpose: Validates correct execution of omega analysis and output generation when enabled
-    
-    Input: Same VCF and BAM files as Test 1, but with omega analysis enabled
-    Parameters: 
-        - omega = true (enables omega analysis)
-        - omega_plot = true (enables omega plot generation)
-        - plot_depths = true (enables depth plotting, should work with omega enabled)
-        - profileall = true (enables mutational profile generation, should work with omega enabled
-
-    Expected Behavior:
-        - Executes omega analysis and generates expected output files in the omega directory
-        - Generates mutational profiles and depth plots as in Test 1
-        - Does not generate outputs for features that are disabled (e.g. mutdensity, oncodrivefml, oncodrive3d)
-    
-    Success Criteria:
-        - Omega output file (all_omegas.tsv) is generated with expected structure and content
-        - Mutational profile output is consistent with Test 1 (snapshot match)
-    
-    ========================================================================================
-    */
-    test("TEST 2. Omega analysis test run") {
+    test("Omega analysis test run") {
         tag "omega"
         
         when {
             params {
-                // Input: Sample name metadata for the pipeline
                 input = "${projectDir}/tests/test_data/input.csv"
-                outdir = "$outputDir"
+                outdir = "${projectDir}/tests_results_omega"
                 wgs_trinuc_counts = "${projectDir}/assets/trinucleotide_counts/trinuc_counts.homo_sapiens.tsv"
-                // Test data fetched directly from the DeepClone_protocol repository
+                plot_depths = true
+                plot_mutation_specific_qc = false
+                profileall  = true
+                signatures = false
+                cosmic_ref_signatures = null
+                indel_ref_signatures = null
+                omega = true
+                omega_plot = true
+            }
+        }
+
+        then {
+            assert workflow.success : "Pipeline should complete without errors"
+            assert path("${params.outdir}/mutational_profile").exists()
+            assert path("${params.outdir}/selection/omega").exists() : "Omega directory should exist"
+            assert path("${params.outdir}/selection/omega/all_omegas.tsv").exists() : "Omega results file should exist"
+            assert !path("${params.outdir}/oncodrivefml").exists()
+            assert !path("${params.outdir}/oncodrive3d").exists()
+            
+            // Validate omega output structure
+            def omegaFile = path("${params.outdir}/selection/omega/all_omegas.tsv")
+            def lines = omegaFile.readLines()
+
+            // Filter out empty lines and the header (collectFile keepHeader is non-deterministic)
+            def expectedHeader = "gene\tsample\timpact\tmutations\tdnds\tpvalue\tlower\tupper"
+            def dataLines = lines.findAll { it && it != expectedHeader }
+            assert dataLines.size() == 38 : "Omega output should contain 38 data rows, found ${dataLines.size()}"
+
+            // Sort by key columns (gene, sample, impact) to avoid floating-point differences affecting order
+            // Round numeric columns (dnds, pvalue, lower, upper) to 2 decimals for deterministic comparison
+            def sortedRounded = dataLines.sort { line ->
+                def cols = line.split('\t')
+                "${cols[0]}\t${cols[1]}\t${cols[2]}"
+            }.collect { line ->
+                def cols = line.split('\t')
+                (4..7).each { i -> cols[i] = String.format("%.2f", cols[i] as Double) }
+                cols.join('\t')
+            }
+            def sortedContent = [expectedHeader] + sortedRounded
+
+            // Snapshot both files
+            assert snapshot(path("${params.outdir}/mutational_profile/all_samples.all.profile.tsv")).match("mutational_profile")
+            assert snapshot(sortedContent.join('\n')).match("omega_results")
+        }
+    }
+
+    test("MAF-based omega analysis test run") {
+        tag "omega_maf"
+
+        when {
+            params {
+                input               = "${projectDir}/tests/test_data/input_maf.csv"
+                outdir              = "$outputDir"
+                wgs_trinuc_counts   = "${projectDir}/assets/trinucleotide_counts/trinuc_counts.homo_sapiens.tsv"
                 input_maf           = 'https://raw.githubusercontent.com/bbglab/DeepClone_protocol/main/test_datasets/deepCSA/testdata/maf/all_samples.somatic.mutations.maf'
                 use_custom_depths   = true
                 custom_depths_table = 'https://raw.githubusercontent.com/bbglab/DeepClone_protocol/main/test_datasets/deepCSA/testdata/depth/all_samples_indv.depths.tsv.gz'
                 plot_depths = true
+                plot_mutation_specific_qc = false
                 profileall  = true
                 signatures = false
+                cosmic_ref_signatures = null
+                indel_ref_signatures = null
                 omega = true
                 omega_plot = true
             }
@@ -134,16 +143,14 @@ nextflow_pipeline {
 
         then {
             assert workflow.success : "Pipeline should complete without errors"
-            // Validate that all folders are created
-            assert path("${outputDir}/mutational_profile").exists()
-            assert path("${outputDir}/selection/omega").exists() : "Omega directory should exist"
-            assert path("${outputDir}/selection/omega/all_omegas.tsv").exists() : "Omega results file should exist"
-            assert !path("${outputDir}/selection/oncodrivefml").exists()
-            assert !path("${outputDir}/selection/oncodrive3d").exists()
-            
+            assert path("${params.outdir}/mutational_profile").exists()
+            assert path("${params.outdir}/selection/omega").exists() : "Omega directory should exist"
+            assert path("${params.outdir}/selection/omega/all_omegas.tsv").exists() : "Omega results file should exist"
+            assert !path("${params.outdir}/selection/oncodrivefml").exists()
+            assert !path("${params.outdir}/selection/oncodrive3d").exists()
+
             // Validate omega output structure
-            def omegaFile = path("${outputDir}/selection/omega/all_omegas.tsv")
-            
+            def omegaFile = path("${params.outdir}/selection/omega/all_omegas.tsv")
             def lines = omegaFile.readLines()
             def header = lines[0].split('\t')
 
@@ -164,42 +171,82 @@ nextflow_pipeline {
             def expectedSamples = ["P19_0002_BDO_01", "P19_0002_BTR_01", "P19_0003_BDO_01"].toSet()
             assert expectedSamples.every { it in foundSamples } : "Missing samples in omega output: ${expectedSamples - foundSamples}"
 
-            // Only snapshot the profile file - omega has non-deterministic floating point values
             assert snapshot(
-                path("${outputDir}/mutational_profile/all_samples.all.profile.tsv")
+                path("${params.outdir}/mutational_profile/all_samples.all.profile.tsv")
             ).match()
+        }
+    }
+
+    test("Mutation density test run") {
+        tag "mutdensity"
+
+        when {
+            params {
+                input = "${projectDir}/tests/test_data/input.csv"
+                outdir = "${projectDir}/tests_results_mutdensity"
+                wgs_trinuc_counts = "${projectDir}/assets/trinucleotide_counts/trinuc_counts.homo_sapiens.tsv"
+                plot_depths = true
+                plot_mutation_specific_qc = false
+                profileall  = true
+                mutationdensity = true
+                signatures = false
+                cosmic_ref_signatures = null
+                indel_ref_signatures = null
+            }
+        }
+
+        then {
+            assert workflow.success : "Pipeline should complete without errors"
+
+            // Mutation density outputs should exist
+            assert path("${params.outdir}/mutdensity").exists() : "Mutation density directory should exist"
+            assert path("${params.outdir}/mutdensity/all_mutdensities.tsv").exists() : "Consolidated mutdensities file should exist"
+
+            // Mutational profile should also exist (profileall=true)
+            assert path("${params.outdir}/mutational_profile").exists()
+
+            // Analyses not requested should NOT exist
+            assert !path("${params.outdir}/selection/omega").exists()
+            assert !path("${params.outdir}/oncodrivefml").exists()
+            assert !path("${params.outdir}/oncodrive3d").exists()
+
+            // Validate all_mutdensities.tsv structure and content
+            def mutdensFile = path("${params.outdir}/mutdensity/all_mutdensities.tsv")
+            def lines = mutdensFile.readLines()
+
+            // Check header contains expected columns
+            def header = lines.first()
+            def expectedColumns = ["SAMPLE_ID", "GENE", "REGIONS", "MUTTYPES", "DEPTH",
+                                   "N_MUTS", "N_MUTATED", "MUTDENSITY_MB", "MUTDENSITY_MB_ADJUSTED",
+                                   "MUTREADSDENSITY_MB", "MUTREADSDENSITY_MB_ADJUSTED"]
+            expectedColumns.each { col ->
+                assert header.contains(col) : "Header should contain column ${col}"
+            }
+
+            // Filter out empty lines and duplicate headers from collectFile
+            def dataLines = lines.findAll { it && it != header }
+            assert dataLines.size() > 0 : "Mutation density file should contain data rows"
+
+            // Verify all 4 region types are present (all, protein_affecting, non_protein_affecting, synonymous)
+            def allContent = lines.join('\n')
+            def expectedRegions = ["all", "protein_affecting", "non_protein_affecting", "synonymous"]
+            expectedRegions.each { region ->
+                assert allContent.contains(region) : "Mutation density should include REGIONS=${region}"
+            }
 
-            //TODO Include omega output snapshot when stable
+            // Sort data lines for deterministic snapshot comparison
+            def sortedContent = [header] + dataLines.sort()
+            assert snapshot(sortedContent.join('\n')).match("mutdensity_results")
         }
     }
 
-    /*
-    ========================================================================================
-        TEST 3: MAF INPUT VALIDATION - Ensuring proper validation of MAF input parameters
-    ========================================================================================
-    Purpose: Validates correct execution when --input_maf is provided without --use_custom_depths,
-    which should result in a pipeline failure due to missing depth information for MAF-based analysis
-    
-    Input: Samplesheet with --input_maf parameter set but --use_custom_depths set to false
-    Parameters: 
-        - input_maf = path to MAF file (e.g. test_mutations.maf)
-        - use_custom_depths = false (should trigger validation failure)
-
-    Expected Behavior:
-        - Pipeline should fail due to missing depth information for MAF-based analysis
-    
-    Success Criteria:
-        - Pipeline fails with an appropriate error message
-    
-    ========================================================================================
-    */
-    test("TEST 3. Should fail when --input_maf is provided without --use_custom_depths") {
+    test("MAF input validation - fails when --input_maf is provided without --use_custom_depths") {
         tag "input_maf_validation"
 
         when {
             params {
                 input             = "${projectDir}/tests/test_data/input.csv"
-                outdir            = "$outputDir"
+                outdir            = "${projectDir}/tests_results_maf_validation"
                 input_maf         = "${projectDir}/tests/test_data/test_mutations.maf"
                 use_custom_depths = false
                 wgs_trinuc_counts = "${projectDir}/assets/trinucleotide_counts/trinuc_counts.homo_sapiens.tsv"
@@ -221,7 +268,7 @@ nextflow_pipeline {
     //     when {
     //         params {
     //             input                = "${projectDir}/tests/test_data/input.csv"
-    //             outdir               = "${params.outdir}/tests_results_maf_input"
+    //             outdir               = "${projectDir}/tests_results_maf_input"
     //             input_maf            = "${projectDir}/tests/test_data/test_mutations.maf"
     //             use_custom_depths    = true
     //             custom_depths_table  = "${projectDir}/tests/test_data/precomputed_depths.tsv"
@@ -234,7 +281,7 @@ nextflow_pipeline {
     //     then {
     //         assert workflow.success : "Pipeline should complete with MAF input and precomputed depths"
     //         assert path("${params.outdir}/input_vcfs").exists() : "Per-sample VCFs should be published"
-    //         assert path("${params.outdir}/computeprofile").exists()
+    //         assert path("${params.outdir}/mutational_profile").exists()
     //     }
     // }
 
diff --git a/tests/deepcsa.nf.test.snap b/tests/deepcsa.nf.test.snap
index 89448046..dd6941a9 100644
--- a/tests/deepcsa.nf.test.snap
+++ b/tests/deepcsa.nf.test.snap
@@ -1,22 +1,53 @@
 {
-    "TEST 2. Omega analysis test run": {
+    "MAF-based omega analysis test run": {
         "content": [
             "all_samples.all.profile.tsv:md5,347c786848dbab840f61f36f1e051f05"
         ],
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "25.04.6"
+        },
+        "timestamp": "2026-04-10T07:31:01.791674344"
+    },
+    "mutdensity_results": {
+        "content": [
+            "SAMPLE_ID\tGENE\tREGIONS\tMUTTYPES\tDEPTH\tN_MUTS\tN_MUTATED\tMUTDENSITY_MB\tMUTDENSITY_MB_ADJUSTED\tMUTREADSDENSITY_MB\tMUTREADSDENSITY_MB_ADJUSTED\nB1\tALL_GENES\tall\tDELETION-INSERTION\t11814683\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB1\tALL_GENES\tall\tDELETION-INSERTION-SNV\t11814683\t11.0\t12.0\t0.9310448701839906\t0.9310448701839906\t1.0156853129279897\t1.0156853129279897\nB1\tALL_GENES\tall\tSNV\t11814683\t11.0\t12.0\t0.9310448701839906\t0.9310448701839906\t1.0156853129279897\t1.0156853129279897\nB1\tALL_GENES\tall\tall_types\t11814683\t11.0\t12.0\t0.9310448701839906\t0.9310448701839906\t1.0156853129279897\t1.0156853129279897\nB1\tALL_GENES\tnon_protein_affecting\tDELETION-INSERTION\t7986662\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB1\tALL_GENES\tnon_protein_affecting\tDELETION-INSERTION-SNV\t7986662\t7.0\t8.0\t0.8764612800691953\t0.9366011973509708\t1.0016700343647946\t1.0704013684011096\nB1\tALL_GENES\tnon_protein_affecting\tSNV\t7986662\t7.0\t8.0\t0.8764612800691953\t0.9366011973509708\t1.0016700343647946\t1.0704013684011096\nB1\tALL_GENES\tnon_protein_affecting\tall_types\t7986662\t7.0\t8.0\t0.8764612800691953\t0.9366011973509708\t1.0016700343647946\t1.0704013684011096\nB1\tALL_GENES\tprotein_affecting\tDELETION-INSERTION\t3856684\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB1\tALL_GENES\tprotein_affecting\tDELETION-INSERTION-SNV\t3856684\t3.0\t3.0\t0.7778703155353147\t0.8426217558009126\t0.7778703155353147\t0.8426217558009126\nB1\tALL_GENES\tprotein_affecting\tSNV\t3856684\t3.0\t3.0\t0.7778703155353147\t0.8426217558009126\t0.7778703155353147\t0.8426217558009126\nB1\tALL_GENES\tprotein_affecting\tall_types\t3856684\t3.0\t3.0\t0.7778703155353147\t0.8426217558009126\t0.7778703155353147\t0.8426217558009126\nB1\tALL_GENES\tsynonymous\tDELETION-INSERTION\t1442366\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB1\tALL_GENES\tsynonymous\tDELETION-INSERTION-SNV\t1442366\t3.0\t3.0\t2.0799159159325717\t3.227416689617185\t2.0799159159325717\t3.227416689617185\nB1\tALL_GENES\tsynonymous\tSNV\t1442366\t3.0\t3.0\t2.0799159159325717\t3.227416689617185\t2.0799159159325717\t3.227416689617185\nB1\tALL_GENES\tsynonymous\tall_types\t1442366\t3.0\t3.0\t2.0799159159325717\t3.227416689617185\t2.0799159159325717\t3.227416689617185\nB1\tTP53\tall\tDELETION-INSERTION\t11814683\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB1\tTP53\tall\tDELETION-INSERTION-SNV\t11814683\t11.0\t12.0\t0.9310448701839906\t0.9310448701839906\t1.0156853129279897\t1.0156853129279897\nB1\tTP53\tall\tSNV\t11814683\t11.0\t12.0\t0.9310448701839906\t0.9310448701839906\t1.0156853129279897\t1.0156853129279897\nB1\tTP53\tall\tall_types\t11814683\t11.0\t12.0\t0.9310448701839906\t0.9310448701839906\t1.0156853129279897\t1.0156853129279897\nB1\tTP53\tnon_protein_affecting\tDELETION-INSERTION\t7986662\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB1\tTP53\tnon_protein_affecting\tDELETION-INSERTION-SNV\t7986662\t7.0\t8.0\t0.8764612800691953\t0.9366011973509707\t1.0016700343647946\t1.0704013684011093\nB1\tTP53\tnon_protein_affecting\tSNV\t7986662\t7.0\t8.0\t0.8764612800691953\t0.9366011973509707\t1.0016700343647946\t1.0704013684011093\nB1\tTP53\tnon_protein_affecting\tall_types\t7986662\t7.0\t8.0\t0.8764612800691953\t0.9366011973509707\t1.0016700343647946\t1.0704013684011093\nB1\tTP53\tprotein_affecting\tDELETION-INSERTION\t3856684\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB1\tTP53\tprotein_affecting\tDELETION-INSERTION-SNV\t3856684\t3.0\t3.0\t0.7778703155353147\t0.8426217558009125\t0.7778703155353147\t0.8426217558009125\nB1\tTP53\tprotein_affecting\tSNV\t3856684\t3.0\t3.0\t0.7778703155353147\t0.8426217558009125\t0.7778703155353147\t0.8426217558009125\nB1\tTP53\tprotein_affecting\tall_types\t3856684\t3.0\t3.0\t0.7778703155353147\t0.8426217558009125\t0.7778703155353147\t0.8426217558009125\nB1\tTP53\tsynonymous\tDELETION-INSERTION\t1442366\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB1\tTP53\tsynonymous\tDELETION-INSERTION-SNV\t1442366\t3.0\t3.0\t2.0799159159325717\t3.227416689617185\t2.0799159159325717\t3.227416689617185\nB1\tTP53\tsynonymous\tSNV\t1442366\t3.0\t3.0\t2.0799159159325717\t3.227416689617185\t2.0799159159325717\t3.227416689617185\nB1\tTP53\tsynonymous\tall_types\t1442366\t3.0\t3.0\t2.0799159159325717\t3.227416689617185\t2.0799159159325717\t3.227416689617185\nB10\tALL_GENES\tall\tDELETION-INSERTION\t12163378\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB10\tALL_GENES\tall\tDELETION-INSERTION-SNV\t12163378\t4.0\t1927.0\t0.328856013518613\t0.328856013518613\t158.4263845125918\t158.4263845125918\nB10\tALL_GENES\tall\tSNV\t12163378\t4.0\t1927.0\t0.328856013518613\t0.328856013518613\t158.4263845125918\t158.4263845125918\nB10\tALL_GENES\tall\tall_types\t12163378\t4.0\t1927.0\t0.328856013518613\t0.328856013518613\t158.4263845125918\t158.4263845125918\nB10\tALL_GENES\tnon_protein_affecting\tDELETION-INSERTION\t8642489\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB10\tALL_GENES\tnon_protein_affecting\tDELETION-INSERTION-SNV\t8642489\t3.0\t6.0\t0.34712222370199136\t0.36685879197476007\t0.6942444474039827\t0.7337175839495201\nB10\tALL_GENES\tnon_protein_affecting\tSNV\t8642489\t3.0\t6.0\t0.34712222370199136\t0.36685879197476007\t0.6942444474039827\t0.7337175839495201\nB10\tALL_GENES\tnon_protein_affecting\tall_types\t8642489\t3.0\t6.0\t0.34712222370199136\t0.36685879197476007\t0.6942444474039827\t0.7337175839495201\nB10\tALL_GENES\tprotein_affecting\tDELETION-INSERTION\t3482133\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB10\tALL_GENES\tprotein_affecting\tDELETION-INSERTION-SNV\t3482133\t1.0\t1921.0\t0.28718030012064444\t0.3109802460164727\t551.673356531758\t597.393052597644\nB10\tALL_GENES\tprotein_affecting\tSNV\t3482133\t1.0\t1921.0\t0.28718030012064444\t0.3109802460164727\t551.673356531758\t597.393052597644\nB10\tALL_GENES\tprotein_affecting\tall_types\t3482133\t1.0\t1921.0\t0.28718030012064444\t0.3109802460164727\t551.673356531758\t597.393052597644\nB10\tTP53\tall\tDELETION-INSERTION\t12163378\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB10\tTP53\tall\tDELETION-INSERTION-SNV\t12163378\t4.0\t1927.0\t0.328856013518613\t0.328856013518613\t158.4263845125918\t158.4263845125918\nB10\tTP53\tall\tSNV\t12163378\t4.0\t1927.0\t0.328856013518613\t0.328856013518613\t158.4263845125918\t158.4263845125918\nB10\tTP53\tall\tall_types\t12163378\t4.0\t1927.0\t0.328856013518613\t0.328856013518613\t158.4263845125918\t158.4263845125918\nB10\tTP53\tnon_protein_affecting\tDELETION-INSERTION\t8642489\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB10\tTP53\tnon_protein_affecting\tDELETION-INSERTION-SNV\t8642489\t3.0\t6.0\t0.34712222370199136\t0.3668587919747601\t0.6942444474039827\t0.7337175839495202\nB10\tTP53\tnon_protein_affecting\tSNV\t8642489\t3.0\t6.0\t0.34712222370199136\t0.3668587919747601\t0.6942444474039827\t0.7337175839495202\nB10\tTP53\tnon_protein_affecting\tall_types\t8642489\t3.0\t6.0\t0.34712222370199136\t0.3668587919747601\t0.6942444474039827\t0.7337175839495202\nB10\tTP53\tprotein_affecting\tDELETION-INSERTION\t3482133\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB10\tTP53\tprotein_affecting\tDELETION-INSERTION-SNV\t3482133\t1.0\t1921.0\t0.28718030012064444\t0.3109802460164726\t551.673356531758\t597.3930525976439\nB10\tTP53\tprotein_affecting\tSNV\t3482133\t1.0\t1921.0\t0.28718030012064444\t0.3109802460164726\t551.673356531758\t597.3930525976439\nB10\tTP53\tprotein_affecting\tall_types\t3482133\t1.0\t1921.0\t0.28718030012064444\t0.3109802460164726\t551.673356531758\t597.3930525976439\nB11\tALL_GENES\tall\tDELETION-INSERTION\t14195739\t1\t1\t0.07044367327407189\t0.07044367327407189\t0.07044367327407189\t0.07044367327407189\nB11\tALL_GENES\tall\tDELETION-INSERTION-SNV\t14195739\t6\t8\t0.42266203964443133\t0.42266203964443133\t0.5635493861925751\t0.5635493861925751\nB11\tALL_GENES\tall\tSNV\t14195739\t5\t7\t0.35221836637035947\t0.35221836637035947\t0.4931057129185032\t0.4931057129185032\nB11\tALL_GENES\tall\tall_types\t14195739\t6\t8\t0.42266203964443133\t0.42266203964443133\t0.5635493861925751\t0.5635493861925751\nB11\tALL_GENES\tnon_protein_affecting\tDELETION-INSERTION\t10046729\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB11\tALL_GENES\tnon_protein_affecting\tDELETION-INSERTION-SNV\t10046729\t2.0\t2.0\t0.19906976688631692\t0.21061098879682924\t0.19906976688631692\t0.21061098879682924\nB11\tALL_GENES\tnon_protein_affecting\tSNV\t10046729\t2.0\t2.0\t0.19906976688631692\t0.21061098879682924\t0.19906976688631692\t0.21061098879682924\nB11\tALL_GENES\tnon_protein_affecting\tall_types\t10046729\t2.0\t2.0\t0.19906976688631692\t0.21061098879682924\t0.19906976688631692\t0.21061098879682924\nB11\tALL_GENES\tprotein_affecting\tDELETION-INSERTION\t4130487\t1\t1\t0.2421022024763666\t0.26215631961888763\t0.2421022024763666\t0.26215631961888763\nB11\tALL_GENES\tprotein_affecting\tDELETION-INSERTION-SNV\t4130487\t4\t6\t0.9684088099054664\t1.0486252784755505\t1.4526132148581996\t1.5729379177133258\nB11\tALL_GENES\tprotein_affecting\tSNV\t4130487\t3\t5\t0.7263066074290998\t0.7864689588566629\t1.210511012381833\t1.3107815980944382\nB11\tALL_GENES\tprotein_affecting\tall_types\t4130487\t4\t6\t0.9684088099054664\t1.0486252784755505\t1.4526132148581996\t1.5729379177133258\nB11\tALL_GENES\tsynonymous\tDELETION-INSERTION\t1542423\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB11\tALL_GENES\tsynonymous\tDELETION-INSERTION-SNV\t1542423\t1.0\t1.0\t0.6483305811700162\t1.0081918952125672\t0.6483305811700162\t1.0081918952125672\nB11\tALL_GENES\tsynonymous\tSNV\t1542423\t1.0\t1.0\t0.6483305811700162\t1.0081918952125672\t0.6483305811700162\t1.0081918952125672\nB11\tALL_GENES\tsynonymous\tall_types\t1542423\t1.0\t1.0\t0.6483305811700162\t1.0081918952125672\t0.6483305811700162\t1.0081918952125672\nB11\tTP53\tall\tDELETION-INSERTION\t14195739\t1\t1\t0.07044367327407189\t0.07044367327407189\t0.07044367327407189\t0.07044367327407189\nB11\tTP53\tall\tDELETION-INSERTION-SNV\t14195739\t6\t8\t0.42266203964443133\t0.42266203964443133\t0.5635493861925751\t0.5635493861925751\nB11\tTP53\tall\tSNV\t14195739\t5\t7\t0.35221836637035947\t0.35221836637035947\t0.4931057129185032\t0.4931057129185032\nB11\tTP53\tall\tall_types\t14195739\t6\t8\t0.42266203964443133\t0.42266203964443133\t0.5635493861925751\t0.5635493861925751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LL_GENES\tall\tSNV\t7204555\t2.0\t2.0\t0.2776021558583424\t0.27760215585834247\t0.2776021558583424\t0.27760215585834247\nB8\tALL_GENES\tall\tall_types\t7204555\t2.0\t2.0\t0.2776021558583424\t0.27760215585834247\t0.2776021558583424\t0.27760215585834247\nB8\tALL_GENES\tprotein_affecting\tDELETION-INSERTION\t2014236\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB8\tALL_GENES\tprotein_affecting\tDELETION-INSERTION-SNV\t2014236\t2.0\t2.0\t0.9929323078328458\t1.0759716696659376\t0.9929323078328458\t1.0759716696659376\nB8\tALL_GENES\tprotein_affecting\tSNV\t2014236\t2.0\t2.0\t0.9929323078328458\t1.0759716696659376\t0.9929323078328458\t1.0759716696659376\nB8\tALL_GENES\tprotein_affecting\tall_types\t2014236\t2.0\t2.0\t0.9929323078328458\t1.0759716696659376\t0.9929323078328458\t1.0759716696659376\nB8\tTP53\tall\tDELETION-INSERTION\t7204555\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB8\tTP53\tall\tDELETION-INSERTION-SNV\t7204555\t2.0\t2.0\t0.2776021558583424\t0.2776021558583424\t0.2776021558583424\t0.2776021558583424\nB8\tTP53\tall\tSNV\t7204555\t2.0\t2.0\t0.2776021558583424\t0.2776021558583424\t0.2776021558583424\t0.2776021558583424\nB8\tTP53\tall\tall_types\t7204555\t2.0\t2.0\t0.2776021558583424\t0.2776021558583424\t0.2776021558583424\t0.2776021558583424\nB8\tTP53\tprotein_affecting\tDELETION-INSERTION\t2014236\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB8\tTP53\tprotein_affecting\tDELETION-INSERTION-SNV\t2014236\t2.0\t2.0\t0.9929323078328458\t1.0759716696659376\t0.9929323078328458\t1.0759716696659376\nB8\tTP53\tprotein_affecting\tSNV\t2014236\t2.0\t2.0\t0.9929323078328458\t1.0759716696659376\t0.9929323078328458\t1.0759716696659376\nB8\tTP53\tprotein_affecting\tall_types\t2014236\t2.0\t2.0\t0.9929323078328458\t1.0759716696659376\t0.9929323078328458\t1.0759716696659376\nB9\tALL_GENES\tall\tDELETION-INSERTION\t15057948\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB9\tALL_GENES\tall\tDELETION-INSERTION-SNV\t15057948\t2.0\t2.0\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\nB9\tALL_GENES\tall\tSNV\t15057948\t2.0\t2.0\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\nB9\tALL_GENES\tall\tall_types\t15057948\t2.0\t2.0\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\nB9\tALL_GENES\tnon_protein_affecting\tDELETION-INSERTION\t10688964\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB9\tALL_GENES\tnon_protein_affecting\tDELETION-INSERTION-SNV\t10688964\t1.0\t1.0\t0.09355443614554226\t0.09887430287848804\t0.09355443614554226\t0.09887430287848804\nB9\tALL_GENES\tnon_protein_affecting\tSNV\t10688964\t1.0\t1.0\t0.09355443614554226\t0.09887430287848804\t0.09355443614554226\t0.09887430287848804\nB9\tALL_GENES\tnon_protein_affecting\tall_types\t10688964\t1.0\t1.0\t0.09355443614554226\t0.09887430287848804\t0.09355443614554226\t0.09887430287848804\nB9\tALL_GENES\tprotein_affecting\tDELETION-INSERTION\t4328385\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB9\tALL_GENES\tprotein_affecting\tDELETION-INSERTION-SNV\t4328385\t1.0\t1.0\t0.23103305274369076\t0.2500663092496694\t0.23103305274369076\t0.2500663092496694\nB9\tALL_GENES\tprotein_affecting\tSNV\t4328385\t1.0\t1.0\t0.23103305274369076\t0.2500663092496694\t0.23103305274369076\t0.2500663092496694\nB9\tALL_GENES\tprotein_affecting\tall_types\t4328385\t1.0\t1.0\t0.23103305274369076\t0.2500663092496694\t0.23103305274369076\t0.2500663092496694\nB9\tTP53\tall\tDELETION-INSERTION\t15057948\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB9\tTP53\tall\tDELETION-INSERTION-SNV\t15057948\t2.0\t2.0\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\nB9\tTP53\tall\tSNV\t15057948\t2.0\t2.0\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\nB9\tTP53\tall\tall_types\t15057948\t2.0\t2.0\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\t0.1328202222507343\nB9\tTP53\tnon_protein_affecting\tDELETION-INSERTION\t10688964\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB9\tTP53\tnon_protein_affecting\tDELETION-INSERTION-SNV\t10688964\t1.0\t1.0\t0.09355443614554226\t0.09887430287848802\t0.09355443614554226\t0.09887430287848802\nB9\tTP53\tnon_protein_affecting\tSNV\t10688964\t1.0\t1.0\t0.09355443614554226\t0.09887430287848802\t0.09355443614554226\t0.09887430287848802\nB9\tTP53\tnon_protein_affecting\tall_types\t10688964\t1.0\t1.0\t0.09355443614554226\t0.09887430287848802\t0.09355443614554226\t0.09887430287848802\nB9\tTP53\tprotein_affecting\tDELETION-INSERTION\t4328385\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nB9\tTP53\tprotein_affecting\tDELETION-INSERTION-SNV\t4328385\t1.0\t1.0\t0.23103305274369076\t0.2500663092496694\t0.23103305274369076\t0.2500663092496694\nB9\tTP53\tprotein_affecting\tSNV\t4328385\t1.0\t1.0\t0.23103305274369076\t0.2500663092496694\t0.23103305274369076\t0.2500663092496694\nB9\tTP53\tprotein_affecting\tall_types\t4328385\t1.0\t1.0\t0.23103305274369076\t0.2500663092496694\t0.23103305274369076\t0.2500663092496694\nall_samples\tALL_GENES\tall\tDELETION-INSERTION\t186204507\t3\t4\t0.016111317864072966\t0.016111317864072966\t0.02148175715209729\t0.02148175715209729\nall_samples\tALL_GENES\tall\tDELETION-INSERTION-SNV\t186204507\t90\t2032\t0.48333953592218903\t0.48333953592218903\t10.912732633265424\t10.912732633265424\nall_samples\tALL_GENES\tall\tSNV\t186204507\t87\t2028\t0.4672282180581161\t0.4672282180581161\t10.891250876113327\t10.891250876113327\nall_samples\tALL_GENES\tall\tall_types\t186204507\t90\t2032\t0.48333953592218903\t0.48333953592218903\t10.912732633265424\t10.912732633265424\nall_samples\tALL_GENES\tnon_protein_affecting\tDELETION-INSERTION\t130752666\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nall_samples\tALL_GENES\tnon_protein_affecting\tDELETION-INSERTION-SNV\t130752666\t38.0\t45.0\t0.29062504928197797\t0.3078161669360301\t0.34416124257076336\t0.36451914505582517\nall_samples\tALL_GENES\tnon_protein_affecting\tSNV\t130752666\t38.0\t45.0\t0.29062504928197797\t0.3078161669360301\t0.34416124257076336\t0.36451914505582517\nall_samples\tALL_GENES\tnon_protein_affecting\tall_types\t130752666\t38.0\t45.0\t0.29062504928197797\t0.3078161669360301\t0.34416124257076336\t0.36451914505582517\nall_samples\tALL_GENES\tprotein_affecting\tDELETION-INSERTION\t54666233\t3\t4\t0.05487848412748689\t0.05944201542353863\t0.07317131216998252\t0.07925602056471817\nall_samples\tALL_GENES\tprotein_affecting\tDELETION-INSERTION-SNV\t54666233\t47\t1982\t0.8597629179972947\t0.9312582416354385\t36.25638518022634\t39.27135818981785\nall_samples\tALL_GENES\tprotein_affecting\tSNV\t54666233\t44\t1978\t0.8048844338698077\t0.8718162262118998\t36.183213868056356\t39.19210216925313\nall_samples\tALL_GENES\tprotein_affecting\tall_types\t54666233\t47\t1982\t0.8597629179972947\t0.9312582416354385\t36.25638518022634\t39.27135818981785\nall_samples\tALL_GENES\tsynonymous\tDELETION-INSERTION\t20382442\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nall_samples\tALL_GENES\tsynonymous\tDELETION-INSERTION-SNV\t20382442\t8.0\t8.0\t0.39249467752686357\t0.611616960811102\t0.39249467752686357\t0.611616960811102\nall_samples\tALL_GENES\tsynonymous\tSNV\t20382442\t8.0\t8.0\t0.39249467752686357\t0.611616960811102\t0.39249467752686357\t0.611616960811102\nall_samples\tALL_GENES\tsynonymous\tall_types\t20382442\t8.0\t8.0\t0.39249467752686357\t0.611616960811102\t0.39249467752686357\t0.611616960811102\nall_samples\tTP53\tall\tDELETION-INSERTION\t186204507\t3\t4\t0.016111317864072966\t0.016111317864072966\t0.02148175715209729\t0.02148175715209729\nall_samples\tTP53\tall\tDELETION-INSERTION-SNV\t186204507\t90\t2032\t0.48333953592218903\t0.48333953592218903\t10.912732633265424\t10.912732633265424\nall_samples\tTP53\tall\tSNV\t186204507\t87\t2028\t0.4672282180581161\t0.4672282180581161\t10.891250876113327\t10.891250876113327\nall_samples\tTP53\tall\tall_types\t186204507\t90\t2032\t0.48333953592218903\t0.48333953592218903\t10.912732633265424\t10.912732633265424\nall_samples\tTP53\tnon_protein_affecting\tDELETION-INSERTION\t130752666\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nall_samples\tTP53\tnon_protein_affecting\tDELETION-INSERTION-SNV\t130752666\t38.0\t45.0\t0.29062504928197797\t0.3078161669360301\t0.34416124257076336\t0.36451914505582517\nall_samples\tTP53\tnon_protein_affecting\tSNV\t130752666\t38.0\t45.0\t0.29062504928197797\t0.3078161669360301\t0.34416124257076336\t0.36451914505582517\nall_samples\tTP53\tnon_protein_affecting\tall_types\t130752666\t38.0\t45.0\t0.29062504928197797\t0.3078161669360301\t0.34416124257076336\t0.36451914505582517\nall_samples\tTP53\tprotein_affecting\tDELETION-INSERTION\t54666233\t3\t4\t0.05487848412748689\t0.05944201542353863\t0.07317131216998252\t0.07925602056471817\nall_samples\tTP53\tprotein_affecting\tDELETION-INSERTION-SNV\t54666233\t47\t1982\t0.8597629179972947\t0.9312582416354385\t36.25638518022634\t39.27135818981785\nall_samples\tTP53\tprotein_affecting\tSNV\t54666233\t44\t1978\t0.8048844338698077\t0.8718162262118998\t36.183213868056356\t39.19210216925313\nall_samples\tTP53\tprotein_affecting\tall_types\t54666233\t47\t1982\t0.8597629179972947\t0.9312582416354385\t36.25638518022634\t39.27135818981785\nall_samples\tTP53\tsynonymous\tDELETION-INSERTION\t20382442\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\nall_samples\tTP53\tsynonymous\tDELETION-INSERTION-SNV\t20382442\t8.0\t8.0\t0.39249467752686357\t0.611616960811102\t0.39249467752686357\t0.611616960811102\nall_samples\tTP53\tsynonymous\tSNV\t20382442\t8.0\t8.0\t0.39249467752686357\t0.611616960811102\t0.39249467752686357\t0.611616960811102\nall_samples\tTP53\tsynonymous\tall_types\t20382442\t8.0\t8.0\t0.39249467752686357\t0.611616960811102\t0.39249467752686357\t0.611616960811102"
+        ],
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "25.04.6"
+        },
+        "timestamp": "2026-03-19T18:37:42.110245706"
+    },
+    "mutational_profile": {
+        "content": [
+            "all_samples.all.profile.tsv:md5,9be4f1f6d5fdfae9a81d4aae1dd26487"
+        ],
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "25.04.6"
+        },
+        "timestamp": "2026-03-19T15:26:19.719365814"
+    },
+    "omega_results": {
+        "content": [
+            "gene\tsample\timpact\tmutations\tdnds\tpvalue\tlower\tupper\nALL_GENES\tB1\tmissense\t3\t0.36\t0.03\t0.09\t0.93\nALL_GENES\tB1\tnonsynonymous_splice\t3\t0.32\t0.02\t0.08\t0.84\nALL_GENES\tB11\tmissense\t3\t1.15\t0.81\t0.29\t3.00\nALL_GENES\tB11\tnonsynonymous_splice\t3\t1.02\t0.98\t0.25\t2.65\nALL_GENES\tB13\tmissense\t5\t0.69\t0.38\t0.25\t1.49\nALL_GENES\tB13\tnonsense\t1\t2.46\t0.44\t0.14\t11.00\nALL_GENES\tB13\tnonsynonymous_splice\t6\t0.75\t0.46\t0.30\t1.53\nALL_GENES\tB13\ttruncating\t1\t1.27\t0.82\t0.07\t5.66\nALL_GENES\tB4a\tmissense\t5\t1.83\t0.22\t0.65\t3.95\nALL_GENES\tB4a\tnonsense\t1\t5.94\t0.17\t0.34\t26.55\nALL_GENES\tB4a\tnonsynonymous_splice\t6\t1.94\t0.15\t0.77\t3.95\nALL_GENES\tB4a\ttruncating\t1\t2.84\t0.37\t0.16\t12.64\nALL_GENES\tB5\tmissense\t2\t0.70\t0.59\t0.12\t2.17\nALL_GENES\tB5\tnonsynonymous_splice\t2\t0.64\t0.49\t0.11\t1.98\nALL_GENES\tall_samples\tessential_splice\t2\t1.83\t0.44\t0.30\t5.75\nALL_GENES\tall_samples\tmissense\t39\t1.87\t0.00\t1.32\t2.57\nALL_GENES\tall_samples\tnonsense\t3\t2.52\t0.17\t0.62\t6.67\nALL_GENES\tall_samples\tnonsynonymous_splice\t44\t1.90\t0.00\t1.37\t2.57\nALL_GENES\tall_samples\ttruncating\t5\t2.19\t0.12\t0.78\t4.78\nTP53\tB1\tmissense\t3\t0.36\t0.03\t0.09\t0.93\nTP53\tB1\tnonsynonymous_splice\t3\t0.32\t0.02\t0.08\t0.84\nTP53\tB11\tmissense\t3\t1.15\t0.81\t0.29\t3.00\nTP53\tB11\tnonsynonymous_splice\t3\t1.02\t0.98\t0.25\t2.65\nTP53\tB13\tmissense\t5\t0.69\t0.38\t0.25\t1.49\nTP53\tB13\tnonsense\t1\t2.46\t0.44\t0.14\t11.00\nTP53\tB13\tnonsynonymous_splice\t6\t0.75\t0.46\t0.30\t1.53\nTP53\tB13\ttruncating\t1\t1.27\t0.82\t0.07\t5.66\nTP53\tB4a\tmissense\t5\t1.83\t0.22\t0.65\t3.95\nTP53\tB4a\tnonsense\t1\t5.94\t0.17\t0.34\t26.55\nTP53\tB4a\tnonsynonymous_splice\t6\t1.94\t0.15\t0.77\t3.95\nTP53\tB4a\ttruncating\t1\t2.84\t0.37\t0.16\t12.64\nTP53\tB5\tmissense\t2\t0.70\t0.59\t0.12\t2.17\nTP53\tB5\tnonsynonymous_splice\t2\t0.64\t0.49\t0.11\t1.98\nTP53\tall_samples\tessential_splice\t2\t1.83\t0.44\t0.30\t5.75\nTP53\tall_samples\tmissense\t39\t1.87\t0.00\t1.32\t2.57\nTP53\tall_samples\tnonsense\t3\t2.52\t0.17\t0.62\t6.67\nTP53\tall_samples\tnonsynonymous_splice\t44\t1.90\t0.00\t1.37\t2.57\nTP53\tall_samples\ttruncating\t5\t2.19\t0.12\t0.78\t4.78"
+        ],
         "timestamp": "2026-04-09T09:53:31.390601733",
         "meta": {
             "nf-test": "0.9.5",
-            "nextflow": "25.10.4"
-        }
+            "nextflow": "25.04.6"
+        },
+        "timestamp": "2026-03-21T11:23:44.3444377"
     },
-    "TEST 1. Basic functionality - MAF-based processing": {
+    "MAF-based minimal features test run": {
         "content": [
             "all_samples.all.profile.tsv:md5,347c786848dbab840f61f36f1e051f05"
         ],
-        "timestamp": "2026-04-09T09:41:14.087331014",
+        "timestamp": "2026-04-10T00:56:26.329412266",
         "meta": {
-            "nf-test": "0.9.5",
-            "nextflow": "25.10.4"
+            "nf-test": "0.9.2",
+            "nextflow": "25.04.6"
         }
     }
 }
\ No newline at end of file
diff --git a/tests/nextflow.config b/tests/nextflow.config
index af60dffd..b0e66740 100644
--- a/tests/nextflow.config
+++ b/tests/nextflow.config
@@ -8,14 +8,20 @@
 // wgs_trinuc_counts is overridden per-test with the data in assets/
 includeConfig '../conf/general_files_IRB.config'
 
-process {
-    executor      = 'slurm'
-    queue         = 'bbg_cpu_zen4,irb_cpu_zen4'
-    errorStrategy = 'retry'
-    maxRetries    = 2
+// config {
+//     testsDir = '.'
+//     workDir = System.getenv('NFT_WORKDIR') ?: '.nf-test'
+//     configFile = 'tests/nextflow.config'
+// }
 
-    withLabel:process_high_memory {
-        memory = { 20.GB * task.attempt }
+profiles {
+    submit_irbcluster {
+        process {
+            executor = 'slurm'
+            queue = 'bbg_cpu_zen4,irb_cpu_zen4'
+            errorStrategy = 'retry'
+            maxRetries = 2
+        }
     }
 }
 
@@ -30,12 +36,20 @@ singularity {
    runOptions = '-B /data/bbg'
 }
 
-params {
-    config_profile_name        = 'Test profile'
-    config_profile_description = 'Test dataset to check pipeline function'
+executor {
+   queueSize = 600
 }
 
 // Skip nf-schema file-existence validation for params that receive remote HTTP URLs in tests.
 validation {
     ignoreParams = ['input_maf', 'custom_depths_table']
-}
\ No newline at end of file
+}
+
+process {
+    // Override the 10m resourceLimits cap from the test profile to allow VEP to complete
+    resourceLimits = [ cpus: 1, memory: 10.GB, time: 60.min ]
+}
+
+params {
+    panel_sites_chunk_size                   = 0
+}
diff --git a/tests/test_data/input.csv b/tests/test_data/input.csv
index 841a1c18..7f91619b 100644
--- a/tests/test_data/input.csv
+++ b/tests/test_data/input.csv
@@ -1,4 +1,16 @@
-sample,vcf
-P19_0002_BDO_01,path/to/P19_0002_BDO_01.vcf
-P19_0002_BTR_01,path/to/P19_0002_BTR_01.vcf
-P19_0003_BDO_01,path/to/P19_0003_BDO_01.vcf
\ No newline at end of file
+sample,vcf,bam
+B10,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B10.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B10.sorted.bam
+B11,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B11.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B11.sorted.bam
+B12,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B12.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B12.sorted.bam
+B13,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B13.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B13.sorted.bam
+B1,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B1.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B1.sorted.bam
+B2a,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B2a.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B2a.sorted.bam
+B2b,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B2b.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B2b.sorted.bam
+B3,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B3.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B3.sorted.bam
+B4a,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B4a.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B4a.sorted.bam
+B4b,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B4b.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B4b.sorted.bam
+B5,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B5.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B5.sorted.bam
+B6,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B6.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B6.sorted.bam
+B7,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B7.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B7.sorted.bam
+B8,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B8.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B8.sorted.bam
+B9,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/mutations_vcf/B9.duplex.filtered.vcf,/data/bbg/datasets/pipelines/deepCSA/test_datasets/deepUMIcaller_outputs/sortbamduplexcons/B9.sorted.bam
diff --git a/tests/test_data/input_maf.csv b/tests/test_data/input_maf.csv
new file mode 100644
index 00000000..7c3b2e84
--- /dev/null
+++ b/tests/test_data/input_maf.csv
@@ -0,0 +1,4 @@
+sample,vcf
+P19_0002_BDO_01,path/to/P19_0002_BDO_01.vcf
+P19_0002_BTR_01,path/to/P19_0002_BTR_01.vcf
+P19_0003_BDO_01,path/to/P19_0003_BDO_01.vcf