diff --git a/pvaccompare/compare_tools/comparison_router.py b/pvaccompare/compare_tools/comparison_router.py
index 5240e14..6ca97b9 100644
--- a/pvaccompare/compare_tools/comparison_router.py
+++ b/pvaccompare/compare_tools/comparison_router.py
@@ -16,7 +16,9 @@ def find_file(results_folder, subfolder, pattern):
     return files[0] if files else None
 
 
-def write_header(output_file, aggregated_columns, unaggregated_columns, reference_match_columns):
+def write_header(
+    output_file, aggregated_columns, unaggregated_columns, reference_match_columns
+):
     """
     Purpose:    Writes the report generation date and time to the top of the output file
     Modifies:   Nothing
@@ -44,7 +46,9 @@ def run_comparison(
     Returns:    None
     """
     output_file = output_file + "_" + prefix.replace("/", "_") + ".tsv"
-    write_header(output_file, aggregated_columns, unaggregated_columns, reference_match_columns)
+    write_header(
+        output_file, aggregated_columns, unaggregated_columns, reference_match_columns
+    )
 
     if "pVACseq" not in prefix:
         yml1_path = find_file(results_folder1, prefix + "/log", "inputs.yml")
diff --git a/pvaccompare/comparisons/compare_aggregated_tsv.py b/pvaccompare/comparisons/compare_aggregated_tsv.py
index ab17820..7716277 100644
--- a/pvaccompare/comparisons/compare_aggregated_tsv.py
+++ b/pvaccompare/comparisons/compare_aggregated_tsv.py
@@ -1,4 +1,5 @@
 from run_utils import *
+import logging
 
 
 class CompareAggregatedTSV:
@@ -12,23 +13,21 @@ def __init__(self, input_file1, input_file2, output_file, columns_to_compare):
         self.ID_replacement_cols = ["Gene", "AA Change"]
         self.columns_to_compare = columns_to_compare
 
-    def check_id(self, cols1_to_drop, cols2_to_drop):
+    def check_id(self):
         """
         Purpose:    Replace ID with Gene-AA_change if needed
         Modifies:   self.contains_id, self.replaced_id
         Returns:    None
         """
-        if "ID" in cols1_to_drop or "ID" in cols2_to_drop:
+        if "ID" not in self.df1.columns or "ID" not in self.df2.columns:
             self.contains_id = False
-
-        if not self.contains_id:
             can_replace = True
             for col in self.ID_replacement_cols:
                 if col not in self.df1.columns or col not in self.df2.columns:
                     can_replace = False
             if can_replace:
                 self.combine_gene_and_AA_change()
-                print("\u2022", "Replaced ID with Gene and AA Change")
+                logging.info("\u2022 Replaced ID with Gene and AA Change")
                 self.replaced_id = True
 
     def combine_gene_and_AA_change(self):
diff --git a/pvaccompare/comparisons/compare_reference_matches_tsv.py b/pvaccompare/comparisons/compare_reference_matches_tsv.py
index 9c1dd65..d2bebb7 100644
--- a/pvaccompare/comparisons/compare_reference_matches_tsv.py
+++ b/pvaccompare/comparisons/compare_reference_matches_tsv.py
@@ -1,4 +1,5 @@
 from run_utils import *
+import logging
 
 
 class CompareReferenceMatchesTSV:
@@ -53,15 +54,15 @@ def check_duplicate_ids(self):
 
         if max_hits_file1 > 1 or max_hits_file2 > 1:
             if max_hits_file1 > 1 and max_hits_file2 > 1:
-                print(
+                logging.error(
                     "ERROR: Duplicate unique records were found in both files. Writing number of hits only."
                 )
             elif max_hits_file1 > 1:
-                print(
+                logging.error(
                     "ERROR: Duplicate unique records were found in file 1. Writing number of hits only."
                 )
             else:
-                print(
+                logging.error(
                     "ERROR: Duplicate unique records were found in file 2. Writing number of hits only."
                 )
             return True
diff --git a/pvaccompare/run.py b/pvaccompare/run.py
index dc3df20..8c0b55e 100644
--- a/pvaccompare/run.py
+++ b/pvaccompare/run.py
@@ -5,7 +5,7 @@
 logging.basicConfig(level=logging.DEBUG, format="%(message)s")
 
 # TODO: Speed up identical file comparison
-# TODO: Implement tests
+# TODO: Add line numbers to output differences
 
 
 def define_parser():
diff --git a/pvaccompare/run_utils.py b/pvaccompare/run_utils.py
index d5463e1..1f2485a 100644
--- a/pvaccompare/run_utils.py
+++ b/pvaccompare/run_utils.py
@@ -37,35 +37,30 @@ def check_column_formatting(df1, df2):
                 break
 
 
-def output_dropped_cols(cols1_to_drop, cols2_to_drop):
+def output_dropped_cols(df1, df2, original_columns):
     """
     Purpose:    Outputs the dropped comparison columns to the terminal and creates a columns dropped message for the generated report
     Modifies:   Nothing
     Returns:    String columns_dropped_message
     """
     columns_dropped_message = ""
-    for col in cols1_to_drop:
-        if col in cols2_to_drop:
+    for col in original_columns:
+        if col not in df1.columns and col not in df2.columns:
             logging.info(
-                "\u2022 Comparison dropped: '%s' is not present in either file", col
+                "\u2022 Column dropped: '%s' is not present in either file", col
             )
             columns_dropped_message += (
-                f"Comparison dropped: '{col}' is not present in either file\n"
-            )
-        else:
-            logging.info(
-                "\u2022 Comparison dropped: '%s' is only present in file 1", col
+                f"Column dropped: '{col}' is not present in either file\n"
             )
+        elif col not in df1.columns:
+            logging.info("\u2022 Column dropped: '%s' is only present in file 2", col)
             columns_dropped_message += (
-                f"Comparison dropped: '{col}' is only present in file 1\n"
-            )
-    for col in cols2_to_drop:
-        if col not in cols1_to_drop:
-            logging.info(
-                "\u2022 Comparison dropped: '%s' is only present in file 2", col
+                f"Column dropped: '{col}' is only present in file 2\n"
             )
+        elif col not in df2.columns:
+            logging.info("\u2022 Column dropped: '%s' is only present in file 1", col)
             columns_dropped_message += (
-                f"Comparison dropped: '{col}' is only present in file 2\n"
+                f"Column dropped: '{col}' is only present in file 1\n"
             )
     return columns_dropped_message
 
@@ -93,63 +88,6 @@ def load_tsv_files(input_file1, input_file2):
     return df1, df2
 
 
-def make_rows_equal(df1, df2):
-    """
-    Purpose:    Add 'dummy data' to make the two dataframes have an equal number of rows
-    Modifies:   One of the two dataframes depending on which is smaller
-    Returns:    Two dataframes
-    """
-    num_rows_to_add = abs(df1.shape[0] - df2.shape[0])
-    if df1.shape[0] > df2.shape[0]:
-        dummy_data = pd.DataFrame(
-            np.nan, index=range(num_rows_to_add), columns=df2.columns
-        )
-        df2 = pd.concat([df2, dummy_data], ignore_index=True)
-    else:
-        dummy_data = pd.DataFrame(
-            np.nan, index=range(num_rows_to_add), columns=df1.columns
-        )
-        df1 = pd.concat([df1, dummy_data], ignore_index=True)
-    return df1, df2
-
-
-def drop_useless_columns(df1, df2, columns_to_compare):
-    """
-    Purpose:    First removes columns that are not included in the comparison, excluding 'ID', then removes columns not present
-                in both files
-    Modifies:   df1 and df2
-    Returns:    Two lists containing the columns dropped in the corresponding dataframes
-    """
-    columns_to_keep = set(["ID"])
-    if "ID" not in df1.columns or "ID" not in df2.columns:
-        columns_to_keep.update(["Gene", "AA Change"])
-
-    # Drop columns that are not in columns_to_compare and not 'ID'
-    cols1_to_drop = [
-        col
-        for col in df1.columns
-        if (col not in columns_to_compare) and (col not in columns_to_keep)
-    ]
-    cols2_to_drop = [
-        col
-        for col in df2.columns
-        if (col not in columns_to_compare) and (col not in columns_to_keep)
-    ]
-
-    df1.drop(columns=cols1_to_drop, inplace=True)
-    df2.drop(columns=cols2_to_drop, inplace=True)
-
-    # Drop columns that are not present in both dataframes
-    common_cols = set(df1.columns).intersection(set(df2.columns))
-    cols1_to_drop = [col for col in df1.columns if col not in common_cols]
-    cols2_to_drop = [col for col in df2.columns if col not in common_cols]
-
-    df1.drop(columns=cols1_to_drop, inplace=True)
-    df2.drop(columns=cols2_to_drop, inplace=True)
-
-    return cols1_to_drop, cols2_to_drop
-
-
 def check_columns_to_compare(df1, df2, columns_to_compare):
     """
     Purpose:    Add columns present in both dataframes to columns_to_keep
@@ -220,7 +158,12 @@ def get_file_differences(
     Modifies:   Nothing
     Returns:    Dictionary of differences and a dictionary of unique variants
     """
-    merged_df = pd.merge(df1, df2, on="ID", suffixes=("_file1", "_file2"))
+    df1_selected = df1[["ID"] + columns_to_compare]
+    df2_selected = df2[["ID"] + columns_to_compare]
+
+    merged_df = pd.merge(
+        df1_selected, df2_selected, on="ID", suffixes=("_file1", "_file2")
+    )
 
     differences = {}
     for col in columns_to_compare:
@@ -242,9 +185,7 @@ def get_file_differences(
             )
 
             # Mask for rows where one value is NaN and the other is not
-            nan_mask = (
-                merged_df[col_file1].isna() & ~merged_df[col_file2].isna()
-            ) | (
+            nan_mask = (merged_df[col_file1].isna() & ~merged_df[col_file2].isna()) | (
                 ~merged_df[col_file1].isna() & merged_df[col_file2].isna()
             )
 
diff --git a/pvaccompare/runners/run_compare_aggregated_tsv.py b/pvaccompare/runners/run_compare_aggregated_tsv.py
index cca98e6..2939159 100755
--- a/pvaccompare/runners/run_compare_aggregated_tsv.py
+++ b/pvaccompare/runners/run_compare_aggregated_tsv.py
@@ -14,23 +14,20 @@ def main(input_file1, input_file2, output_file, columns_to_compare):
         input_file1, input_file2, output_file, columns_to_compare
     )
     check_column_formatting(comparer.df1, comparer.df2)
+    comparer.check_id()
 
-    cols1_to_drop, cols2_to_drop = drop_useless_columns(
+    columns_dropped_message = output_dropped_cols(
         comparer.df1, comparer.df2, comparer.columns_to_compare
     )
-    columns_dropped_message = output_dropped_cols(cols1_to_drop, cols2_to_drop)
     comparer.columns_to_compare = check_columns_to_compare(
         comparer.df1, comparer.df2, comparer.columns_to_compare
     )
-    comparer.check_id(cols1_to_drop, cols2_to_drop)
 
     common_variants = get_common_variants(comparer.df1, comparer.df2)
     unique_variants_file1, unique_variants_file2 = get_unique_variants(
         comparer.df1, comparer.df2, common_variants
     )
 
-    if comparer.df1.shape != comparer.df2.shape:
-        comparer.df1, comparer.df2 = make_rows_equal(comparer.df1, comparer.df2)
     differences, unique_variants = get_file_differences(
         comparer.df1,
         comparer.df2,
diff --git a/pvaccompare/runners/run_compare_reference_matches_tsv.py b/pvaccompare/runners/run_compare_reference_matches_tsv.py
index ab0d31d..6fae098 100644
--- a/pvaccompare/runners/run_compare_reference_matches_tsv.py
+++ b/pvaccompare/runners/run_compare_reference_matches_tsv.py
@@ -14,23 +14,19 @@ def main(input_file1, input_file2, output_file, columns_to_compare):
         input_file1, input_file2, output_file, columns_to_compare
     )
     check_column_formatting(comparer.df1, comparer.df2)
-
     comparer.create_id_column()
-    common_variants = get_common_variants(comparer.df1, comparer.df2)
-    unique_variants_file1, unique_variants_file2 = get_unique_variants(
-        comparer.df1, comparer.df2, common_variants
-    )
 
-    cols1_to_drop, cols2_to_drop = drop_useless_columns(
+    columns_dropped_message = output_dropped_cols(
         comparer.df1, comparer.df2, comparer.columns_to_compare
     )
-    columns_dropped_message = output_dropped_cols(cols1_to_drop, cols2_to_drop)
     comparer.columns_to_compare = check_columns_to_compare(
         comparer.df1, comparer.df2, comparer.columns_to_compare
     )
 
-    if comparer.df1.shape != comparer.df2.shape:
-        comparer.df1, comparer.df2 = make_rows_equal(comparer.df1, comparer.df2)
+    common_variants = get_common_variants(comparer.df1, comparer.df2)
+    unique_variants_file1, unique_variants_file2 = get_unique_variants(
+        comparer.df1, comparer.df2, common_variants
+    )
 
     if comparer.check_duplicate_ids():
         differences_summary = generate_differences_summary(
diff --git a/pvaccompare/runners/run_compare_unaggregated_tsv.py b/pvaccompare/runners/run_compare_unaggregated_tsv.py
index 3ae3f17..ee9ea00 100644
--- a/pvaccompare/runners/run_compare_unaggregated_tsv.py
+++ b/pvaccompare/runners/run_compare_unaggregated_tsv.py
@@ -8,31 +8,26 @@ def main(input_file1, input_file2, output_file, columns_to_compare):
     Modifies:   Nothing
     Returns:    None
     """
-    id_format = (
-        "Chromosome-Start-Stop-Reference-Variant-HLA_Allele-Sub_peptide_Position-Mt_Epitope_Seq-Index"
-    )
+    id_format = "Chromosome-Start-Stop-Reference-Variant-HLA_Allele-Sub_peptide_Position-Mt_Epitope_Seq-Index"
 
     comparer = CompareUnaggregatedTSV(
         input_file1, input_file2, output_file, columns_to_compare
     )
     check_column_formatting(comparer.df1, comparer.df2)
-
     comparer.create_id_column()
-    common_variants = get_common_variants(comparer.df1, comparer.df2)
-    unique_variants_file1, unique_variants_file2 = get_unique_variants(
-        comparer.df1, comparer.df2, common_variants
-    )
 
-    cols1_to_drop, cols2_to_drop = drop_useless_columns(
+    columns_dropped_message = output_dropped_cols(
         comparer.df1, comparer.df2, comparer.columns_to_compare
     )
-    columns_dropped_message = output_dropped_cols(cols1_to_drop, cols2_to_drop)
     comparer.columns_to_compare = check_columns_to_compare(
         comparer.df1, comparer.df2, comparer.columns_to_compare
     )
 
-    if comparer.df1.shape != comparer.df2.shape:
-        comparer.df1, comparer.df2 = make_rows_equal(comparer.df1, comparer.df2)
+    common_variants = get_common_variants(comparer.df1, comparer.df2)
+    unique_variants_file1, unique_variants_file2 = get_unique_variants(
+        comparer.df1, comparer.df2, common_variants
+    )
+
     differences, unique_variants = get_file_differences(
         comparer.df1,
         comparer.df2,
diff --git a/pvaccompare/tests/test_compare_aggregated_tsv.py b/pvaccompare/tests/test_compare_aggregated_tsv.py
index 2c51935..1605a84 100644
--- a/pvaccompare/tests/test_compare_aggregated_tsv.py
+++ b/pvaccompare/tests/test_compare_aggregated_tsv.py
@@ -6,6 +6,7 @@
 
 # To run the tests navigate to pvaccompare/ and run the following:
 # python -m unittest tests/test_compare_aggregated_tsv.py
+# python -m unittest discover -s tests
 class TestRunCompareAggregatedTSV(unittest.TestCase):
     def setUp(self):
         self.input_file1 = tempfile.NamedTemporaryFile(delete=False, suffix=".tsv")
@@ -74,3 +75,38 @@ def test_different_files(self):
         ) as expected_file:
             expected_output = expected_file.read().strip()
         self.assertEqual(sanitized_output.strip(), expected_output)
+
+    def test_missing_id(self):
+        with open("tests/test_data/aggregated_input1.tsv", "r") as f:
+            content1 = f.read()
+        with open("tests/test_data/aggregated_input3.tsv", "r") as f:
+            content2 = f.read()
+
+        self.input_file1.write(content1.encode())
+        self.input_file2.write(content2.encode())
+        self.input_file1.close()
+        self.input_file2.close()
+
+        with self.assertLogs(level="INFO") as log:
+            main(
+                self.input_file1.name,
+                self.input_file2.name,
+                self.output_file.name,
+                self.columns_to_compare,
+            )
+        self.assertIn("INFO:root:• Replaced ID with Gene and AA Change", log.output)
+
+        self.output_file.seek(0)
+        output_content = self.output_file.read().decode()
+        sanitized_output = "\n".join(
+            [
+                line
+                for line in output_content.splitlines()
+                if not line.startswith("File 1:") and not line.startswith("File 2:")
+            ]
+        )
+        with open(
+            "tests/test_data/aggregated_id_change_output.tsv", "r"
+        ) as expected_file:
+            expected_output = expected_file.read().strip()
+        self.assertEqual(sanitized_output.strip(), expected_output)
diff --git a/pvaccompare/tests/test_compare_json.py b/pvaccompare/tests/test_compare_json.py
index 87f4834..b684a1b 100644
--- a/pvaccompare/tests/test_compare_json.py
+++ b/pvaccompare/tests/test_compare_json.py
@@ -6,6 +6,7 @@
 
 # To run the tests navigate to pvaccompare/ and run the following:
 # python -m unittest tests/test_compare_json.py
+# python -m unittest discover -s tests
 class TestRunCompareJSON(unittest.TestCase):
     def setUp(self):
         self.input_file1 = tempfile.NamedTemporaryFile(delete=False, suffix=".json")
diff --git a/pvaccompare/tests/test_compare_reference_matches_tsv.py b/pvaccompare/tests/test_compare_reference_matches_tsv.py
index 19f56d0..d8df5a6 100644
--- a/pvaccompare/tests/test_compare_reference_matches_tsv.py
+++ b/pvaccompare/tests/test_compare_reference_matches_tsv.py
@@ -6,6 +6,7 @@
 
 # To run the tests navigate to pvaccompare/ and run the following:
 # python -m unittest tests/test_compare_reference_matches_tsv.py
+# python -m unittest discover -s tests
 class TestRunCompareReferenceMatchesTSV(unittest.TestCase):
     def setUp(self):
         self.input_file1 = tempfile.NamedTemporaryFile(delete=False, suffix=".tsv")
@@ -82,11 +83,16 @@ def test_duplicate_records(self):
         self.input_file1.close()
         self.input_file2.close()
 
-        main(
-            self.input_file1.name,
-            self.input_file2.name,
-            self.output_file.name,
-            self.columns_to_compare,
+        with self.assertLogs(level="INFO") as log:
+            main(
+                self.input_file1.name,
+                self.input_file2.name,
+                self.output_file.name,
+                self.columns_to_compare,
+            )
+        self.assertIn(
+            "ERROR:root:ERROR: Duplicate unique records were found in file 2. Writing number of hits only.",
+            log.output,
         )
 
         self.output_file.seek(0)
diff --git a/pvaccompare/tests/test_compare_unaggregated_tsv.py b/pvaccompare/tests/test_compare_unaggregated_tsv.py
index caf8609..2f67834 100644
--- a/pvaccompare/tests/test_compare_unaggregated_tsv.py
+++ b/pvaccompare/tests/test_compare_unaggregated_tsv.py
@@ -6,6 +6,7 @@
 
 # To run the tests navigate to pvaccompare/ and run the following:
 # python -m unittest tests/test_compare_unaggregated_tsv.py
+# python -m unittest discover -s tests
 class TestRunCompareUnaggregatedTSV(unittest.TestCase):
     def setUp(self):
         self.input_file1 = tempfile.NamedTemporaryFile(delete=False, suffix=".tsv")
@@ -79,3 +80,44 @@ def test_different_files(self):
         ) as expected_file:
             expected_output = expected_file.read().strip()
         self.assertEqual(sanitized_output.strip(), expected_output)
+
+    def test_columns_missing(self):
+        with open("tests/test_data/unaggregated_input1.tsv", "r") as f:
+            content1 = f.read()
+        with open("tests/test_data/unaggregated_input3.tsv", "r") as f:
+            content2 = f.read()
+
+        self.input_file1.write(content1.encode())
+        self.input_file2.write(content2.encode())
+        self.input_file1.close()
+        self.input_file2.close()
+
+        with self.assertLogs(level="INFO") as log:
+            main(
+                self.input_file1.name,
+                self.input_file2.name,
+                self.output_file.name,
+                self.columns_to_compare,
+            )
+        expected_logs = [
+            "INFO:root:• Column dropped: 'Median MT IC50 Score' is only present in file 1",
+            "INFO:root:• Column dropped: 'Median WT IC50 Score' is only present in file 1",
+        ]
+
+        for expected_log in expected_logs:
+            self.assertIn(expected_log, log.output)
+
+        self.output_file.seek(0)
+        output_content = self.output_file.read().decode()
+        sanitized_output = "\n".join(
+            [
+                line
+                for line in output_content.splitlines()
+                if not line.startswith("File 1:") and not line.startswith("File 2:")
+            ]
+        )
+        with open(
+            "tests/test_data/unaggregated_col_dropped_output.tsv", "r"
+        ) as expected_file:
+            expected_output = expected_file.read().strip()
+        self.assertEqual(sanitized_output.strip(), expected_output)
diff --git a/pvaccompare/tests/test_compare_yml.py b/pvaccompare/tests/test_compare_yml.py
index bfa7378..6c8a6cb 100644
--- a/pvaccompare/tests/test_compare_yml.py
+++ b/pvaccompare/tests/test_compare_yml.py
@@ -6,6 +6,7 @@
 
 # To run the tests navigate to pvaccompare/ and run the following:
 # python -m unittest tests/test_compare_yml.py
+# python -m unittest discover -s tests
 class TestRunCompareYML(unittest.TestCase):
     def setUp(self):
         self.input_file1 = tempfile.NamedTemporaryFile(delete=False, suffix=".yml")
diff --git a/pvaccompare/tests/test_data/aggregated_id_change_output.tsv b/pvaccompare/tests/test_data/aggregated_id_change_output.tsv
new file mode 100644
index 0000000..9786bf6
--- /dev/null
+++ b/pvaccompare/tests/test_data/aggregated_id_change_output.tsv
@@ -0,0 +1,22 @@
+
+============================== AGGREGATED TSV COMPARISON ==============================
+
+
+
+/* Differences Summary */
+-----------------------------
+Total number of variants: 18
+Number of common variants: 18
+Number of variants unique to file 1: 0
+Number of variants unique to file 2: 0
+-----
+Number of differences in Best Peptide: 1
+
+
+============[ DIFFERENCES IN BEST PEPTIDE ]============
+
+
+ID Format: 'Gene (AA_Change)'
+
+ID	File 1	File 2
+SIX4 (E23Q):	QENGMQSA	->	QENCMQSA
\ No newline at end of file
diff --git a/pvaccompare/tests/test_data/aggregated_input3.tsv b/pvaccompare/tests/test_data/aggregated_input3.tsv
new file mode 100644
index 0000000..e58907c
--- /dev/null
+++ b/pvaccompare/tests/test_data/aggregated_input3.tsv
@@ -0,0 +1,19 @@
+Gene	AA Change	Num Passing Transcripts	Best Peptide	Best Transcript	TSL	Allele	Pos	Prob Pos	Num Passing Peptides	IC50 MT	IC50 WT	%ile MT	%ile WT	RNA Expr	RNA VAF	Allele Expr	RNA Depth	DNA VAF	Tier	Ref Match	Evaluation
+ADAR	E806V	15	AERMGFTVV	ENST00000368474.9	1	HLA-B*45:01	8	None	6	76.110	61.796	0.100	0.125	131.831	0.348	45.877	1233.0	0.302	Pass	False	Pending
+KIF1C	S433F	1	TEFQIGPEEA	ENST00000320785.10	1	HLA-B*45:01	3	None	2	152.101	166.309	0.350	0.473	121.452	0.298	36.193	1679.0	0.316	Pass	False	Pending
+OSTC	F9L	3	YRVPLLVL	ENST00000361564.9	1	HLA-C*06:02	5	None	3	282.169	272.915	0.232	0.202	173.862	0.484	84.149	1028.0	0.462	Pass	False	Pending
+ARID1B	G910A	9	SPGGQMHAA	ENST00000346085.10	1	HLA-B*82:02	9	None	3	345.587	6116.264	0.820	2.6	39.753	1.0	39.753	163.0	1.000	Pass	False	Pending
+MSH6	D1255N	6	VENYSQNVA	ENST00000234420.11	1	HLA-B*45:01	3	None	5	66.513	332.806	0.254	0.665	48.826	0.338	16.503	352.0	0.318	Pass	False	Pending
+RPRD1A	Q21H	4	SELSNSQHSV	ENST00000399022.9	1	HLA-B*45:01	8	None	2	368.846	354.639	0.430	0.415	57.244	0.5	28.622	602.0	0.417	Pass	False	Pending
+SURF1	N89K	1	RRKWKLKLI	ENST00000371974.8	1	HLA-C*06:02	7	None	3	212.834	161.921	0.451	0.332	23.353	0.762	17.795	563.0	0.700	Pass	False	Pending
+MAU2	S111R	1	VKFEAARLL	ENST00000262815.13	1	HLA-C*06:02	7	None	3	212.075	484.278	0.424	1.251	34.845	0.445	15.506	238.0	0.345	Pass	False	Pending
+SIX4	E23Q	2	QENCMQSA	ENST00000216513.5	1	HLA-B*45:01	6	None	2	222.019	324.687	0.230	0.405	15.015	0.987	14.82	76.0	1.000	Pass	False	Pending
+ZNF548	D12Y	9	VVFEYVAIY	ENST00000366197.9	1	HLA-A*29:02	5	None	12	41.063	75.492	0.115	0.26	13.433	0.378	5.078	127.0	0.468	Pass	False	Pending
+MYBBP1A	E653G	3	VEVLVGILLA	ENST00000254718.9	1	HLA-B*45:01	6	None	1	331.249	403.175	0.730	1.03	47.489	0.319	15.149	420.0	0.352	Pass	False	Pending
+UQCC1	W44S	7	SRTSQSPQM	ENST00000374385.10	1	HLA-C*06:02	6	None	1	365.305	752.965	0.594	1.064	47.720	0.327	15.604	581.0	0.434	Pass	False	Pending
+SORBS3	R336P	1	APSLSPHKM	ENST00000240123.12	1	HLA-B*82:02	2	None	2	250.848	24191.463	0.370	22.0	23.107	0.374	8.642	195.0	0.464	Pass	True	Pending
+ADPRHL1	H220N	4	QENWFYFEA	ENST00000375418.8	1	HLA-B*45:01	3	None	7	11.080	11.608	0.027	0.033	2.743	0.917	2.515	24.0	0.517	Pass	False	Pending
+SETD6	R185H	4	DLANIHSEY	ENST00000219315.9	1	HLA-A*29:02	6	None	2	103.731	277.9	0.483	0.94	11.621	0.34	3.951	100.0	0.335	Pass	False	Pending
+ATP7A	D870H	13	HESLITGEA	ENST00000341514.11	1	HLA-B*45:01	1	None	1	164.310	360.223	0.400	0.75	5.152	0.951	4.9	41.0	0.982	Pass	False	Pending
+ZBTB3	S405F	2	EPLYLSFEY	ENST00000394807.5	1	HLA-A*29:02	7	None	3	382.420	2333.53	1.167	2.8	13.703	0.601	8.236	148.0	0.670	Pass	False	Pending
+ZNF25	E21K	1	KEKWKLLTPA	ENST00000302609.8	1	HLA-B*45:01	3	None	2	377.257	137.86	0.640	0.47	13.089	0.471	6.165	187.0	0.485	Pass	False	Pending
\ No newline at end of file
diff --git a/pvaccompare/tests/test_data/unaggregated_col_dropped_output.tsv b/pvaccompare/tests/test_data/unaggregated_col_dropped_output.tsv
new file mode 100644
index 0000000..5105408
--- /dev/null
+++ b/pvaccompare/tests/test_data/unaggregated_col_dropped_output.tsv
@@ -0,0 +1,26 @@
+
+
+============================== UNAGGREGATED TSV COMPARISON ==============================
+
+
+
+Column dropped: 'Median MT IC50 Score' is only present in file 1
+Column dropped: 'Median WT IC50 Score' is only present in file 1
+
+/* Differences Summary */
+-----------------------------
+Total number of variants: 18
+Number of common variants: 18
+Number of variants unique to file 1: 0
+Number of variants unique to file 2: 0
+-----
+Number of differences in Biotype: 1
+
+
+============[ DIFFERENCES IN BIOTYPE ]============
+
+
+ID Format: Chromosome-Start-Stop-Reference-Variant-HLA_Allele-Sub_peptide_Position-Mt_Epitope_Seq-Index
+
+ID	File 1	File 2
+chr1-16006133-16006134-G-T-HLA-A*29:02-3-VTPMGWGC-1.SRARP.ENST00000329454.2.missense.100G/W:	other	->	protein_coding
\ No newline at end of file
diff --git a/pvaccompare/tests/test_data/unaggregated_input3.tsv b/pvaccompare/tests/test_data/unaggregated_input3.tsv
new file mode 100644
index 0000000..39e462d
--- /dev/null
+++ b/pvaccompare/tests/test_data/unaggregated_input3.tsv
@@ -0,0 +1,19 @@
+Chromosome	Start	Stop	Reference	Variant	Transcript	Transcript Support Level	Transcript Length	Biotype	Ensembl Gene ID	Variant Type	Mutation	Protein Position	Gene Name	HGVSc	HGVSp	HLA Allele	Peptide Length	Sub-peptide Position	Mutation Position	MT Epitope Seq	WT Epitope Seq	Best MT IC50 Score Method	Best MT IC50 Score	Corresponding WT IC50 Score	Corresponding Fold Change	Best MT Percentile Method	Best MT Percentile	Corresponding WT Percentile	Tumor DNA Depth	Tumor DNA VAF	Tumor RNA Depth	Tumor RNA VAF	Normal Depth	Normal VAF	Gene Expression	Transcript Expression	Median Fold Change	Median MT Percentile	Median WT Percentile	MHCflurryEL Processing WT Score	MHCflurryEL Processing MT Score	MHCflurryEL Presentation WT Score	MHCflurryEL Presentation MT Score	MHCflurryEL Presentation WT Percentile	MHCflurryEL Presentation MT Percentile	MHCflurry WT IC50 Score	MHCflurry MT IC50 Score	MHCflurry WT Percentile	MHCflurry MT Percentile	MHCnuggetsI WT IC50 Score	MHCnuggetsI MT IC50 Score	MHCnuggetsI WT Percentile	MHCnuggetsI MT Percentile	NetMHC WT IC50 Score	NetMHC MT IC50 Score	NetMHC WT Percentile	NetMHC MT Percentile	NetMHCcons WT IC50 Score	NetMHCcons MT IC50 Score	NetMHCcons WT Percentile	NetMHCcons MT Percentile	NetMHCpan WT IC50 Score	NetMHCpan MT IC50 Score	NetMHCpan WT Percentile	NetMHCpan MT Percentile	NetMHCpanEL WT Score	NetMHCpanEL MT Score	NetMHCpanEL WT Percentile	NetMHCpanEL MT Percentile	PickPocket WT IC50 Score	PickPocket MT IC50 Score	PickPocket WT Percentile	PickPocket MT Percentile	SMM WT IC50 Score	SMM MT IC50 Score	SMM WT Percentile	SMM MT Percentile	SMMPMBEC WT IC50 Score	SMMPMBEC MT IC50 Score	SMMPMBEC WT Percentile	SMMPMBEC MT Percentile	Index	Problematic Positions	cterm_7mer_gravy_score	max_7mer_gravy_score	difficult_n_terminal_residue	c_terminal_cysteine	c_terminal_proline	cysteine_count	n_terminal_asparagine	asparagine_proline_bond_count
+chr1	16006133	16006134	G	T	ENST00000329454.2	1	169	protein_coding	ENSG00000183888	missense	G/W	100	SRARP	ENST00000329454.2:c.298G>T	ENSP00000332162.2:p.Gly100Trp	HLA-A*29:02	8	1	8	TRVTPMGW	TRVTPMGG	MHCnuggetsI	5975.7	5979.4	1.001	NetMHCpanEL	19.0	94.0	177	1.0	0	0.0	140	0.007	0.082	0.082	1.131	47.5	89.755	0.0162521302700042	0.2146173901855945	0.0032409171573275	0.0073226566762991	99.28660326086955	31.64317934782608	33131.214717405455	29360.079756494662	79.69287499999999	34.4285	5979.4	5975.7	88.51	86.9	42736.34	39003.21	94.0	73.0	37739.56	27131.77	91.0	47.0	45107.12	37303.14	94.0	54.0	1e-06	0.000244	94.0	19.0	50000.0	50000.0	48.0	48.0	72218.75546109042	95202.86103673978	75.0	87.0	38366.307210597464	34431.028829451876	40.0	35.0	1.SRARP.ENST00000329454.2.missense.100G/W	None	-0.28571428571428564	-0.2571428571428571	False	False	False	0	False	0
+chr1	16006133	16006134	G	T	ENST00000329454.2	1	169	protein_coding	ENSG00000183888	missense	G/W	100	SRARP	ENST00000329454.2:c.298G>T	ENSP00000332162.2:p.Gly100Trp	HLA-A*29:02	8	2	7	RVTPMGWG	RVTPMGGG	MHCnuggetsI	5774.79	5915.31	1.024	MHCnuggetsI	16.79	51.61	177	1.0	0	0.0	140	0.007	0.082	0.082	1.0	45.5	68.725	0.0117710828781127	0.0056250356137752	0.003208284136529	0.0031380962516865	99.28660326086955	99.28660326086955	32937.3096681338	32952.3967604395	74.450375	74.450375	5915.31	5774.79	51.61	16.79	40195.52	35402.98	80.0	55.0	31569.21	28795.36	63.0	53.0	43232.57	36585.65	85.0	51.0	4e-06	3.5e-05	75.0	40.0	15881.048931852625	15881.048931852625	18.0	18.0	33392.581914282324	33392.581914282324	34.0	34.0	41015.68792124474	38190.03004120177	44.0	40.0	1.SRARP.ENST00000329454.2.missense.100G/W	None	0.3	0.3	False	False	False	0	False	0
+chr1	16006133	16006134	G	T	ENST00000329454.2	1	169	protein_coding	ENSG00000183888	missense	G/W	100	SRARP	ENST00000329454.2:c.298G>T	ENSP00000332162.2:p.Gly100Trp	HLA-A*29:02	8	3	6	VTPMGWGC	VTPMGGGC	MHCnuggetsI	5820.91	5880.83	1.01	SMM	8.1	8.1	177	1.0	0	0.0	140	0.007	0.082	0.082	1.06	47.0	58.0	0.0085067339241504	0.0201449580490589	0.0034462359996615	0.0036875480816715	99.28660326086955	99.28660326086955	30229.35802588496	29408.42162655613	41.302125	34.4285	5880.83	5820.91	37.0	22.61	41694.97	38124.12	88.0	68.0	34798.02	30726.73	78.0	60.0	43871.54	39574.11	88.0	65.0	6e-06	1.5e-05	68.0	53.0	50000.0	38984.65491118616	48.0	41.0	8504.523783121827	8504.523783121827	8.1	8.1	28903.470997020624	30616.10932414785	29.0	31.0	1.SRARP.ENST00000329454.2.missense.100G/W	8	0.05714285714285713	0.3	False	True	False	1	False	0
+chr1	16006133	16006134	G	T	ENST00000329454.2	1	169	protein_coding	ENSG00000183888	missense	G/W	100	SRARP	ENST00000329454.2:c.298G>T	ENSP00000332162.2:p.Gly100Trp	HLA-A*29:02	8	4	5	TPMGWGCL	TPMGGGCL	MHCnuggetsI	5948.47	5958.24	1.002	NetMHCpan	34.0	52.0	177	1.0	0	0.0	140	0.007	0.082	0.082	0.902	48.531	46.5	0.2750012651085853	0.1149642989039421	0.0085204094797548	0.0047828223289128	27.61739130434782	62.74467391304348	31261.296587367808	31746.638199036588	50.764875	59.0625	5958.24	5948.47	77.49000000000001	71.54	38875.14	36511.31	72.0	60.0	27875.69	23067.13	50.0	35.0	36835.88	31405.68	52.0	34.0	4.4e-05	4.4e-05	37.0	37.0	44872.50393235162	37333.42327399945	45.0	40.0	30314.47141744229	87026.19589953587	31.0	84.0	26850.353017500933	37320.7188256828	27.0	39.0	1.SRARP.ENST00000329454.2.missense.100G/W	7	0.7	0.7	False	False	False	1	False	0
+chr1	16006133	16006134	G	T	ENST00000329454.2	1	169	protein_coding	ENSG00000183888	missense	G/W	100	SRARP	ENST00000329454.2:c.298G>T	ENSP00000332162.2:p.Gly100Trp	HLA-A*29:02	8	5	4	PMGWGCLA	PMGGGCLA	MHCnuggetsI	5913.41	5926.68	1.002	SMM	16.0	19.0	177	1.0	0	0.0	140	0.007	0.082	0.082	1.104	43.0	NA	0.0053385943174362	0.0105953887104988	0.0033092989357108	0.0033669849259659	99.28660326086955	99.28660326086955	31160.68448475006	31201.132424958218	50.764875	50.764875	5926.68	5913.41	57.99	50.63	34767.17	31424.38	53.0	41.0	17505.31	14643.23	23.0	17.0	40266.92	35078.12	69.0	45.0	8e-06	1.1e-05	63.0	58.0	24747.820312174	23192.263360933746	28.0	27.0	20167.404078469328	16582.499772033992	19.0	16.0	27161.26545212926	23820.452104817577	27.0	23.0	1.SRARP.ENST00000329454.2.missense.100G/W	6	1.1857142857142857	1.1857142857142857	False	False	False	1	False	0
+chr1	16006133	16006134	G	T	ENST00000329454.2	1	169	protein_coding	ENSG00000183888	missense	G/W	100	SRARP	ENST00000329454.2:c.298G>T	ENSP00000332162.2:p.Gly100Trp	HLA-A*29:02	8	6	3	MGWGCLAQ	MGGGCLAQ	MHCnuggetsI	5868.16	5856.0	0.998	NetMHCcons	8.4	28.0	177	1.0	0	0.0	140	0.007	0.082	0.082	1.176	36.243	45.0	0.0028298906981945	0.0081132613122463	0.0034694289208414	0.0035029098623762	99.28660326086955	99.28660326086955	29406.2330596394	29681.66066528507	34.4285	36.485875	5856.0	5868.16	29.81	33.08	31778.6	27590.03	42.0	31.0	20040.43	8433.14	28.0	8.4	39303.57	30272.0	64.0	31.0	4e-06	1.9e-05	75.0	49.0	50000.0	33144.32235498932	48.0	36.0	94983.90053244469	94765.44362333506	87.0	86.0	38721.30628040024	36724.001799460595	41.0	38.0	1.SRARP.ENST00000329454.2.missense.100G/W	5	0.4142857142857142	1.1857142857142857	False	False	False	1	False	0
+chr1	16006133	16006134	G	T	ENST00000329454.2	1	169	protein_coding	ENSG00000183888	missense	G/W	100	SRARP	ENST00000329454.2:c.298G>T	ENSP00000332162.2:p.Gly100Trp	HLA-A*29:02	8	7	2	GWGCLAQA	GGGCLAQA	MHCnuggetsI	5902.91	5962.45	1.01	NetMHCcons	28.0	73.0	177	1.0	0	0.0	140	0.007	0.082	0.082	1.145	47.82	77.22	0.0125645361840724	0.0110534355044364	0.0032073908927789	0.0032773035754855	99.28660326086955	99.28660326086955	33042.14184992329	32137.008594616924	74.450375	63.8735	5962.45	5902.91	79.99000000000001	45.64	41579.64	34084.43	88.0	50.0	33686.63	20040.43	73.0	28.0	45866.52	39011.66	97.0	62.0	1e-06	7e-06	94.0	66.0	50000.0	42971.88692543735	48.0	44.0	58836.92946080371	33624.048234218884	63.0	34.0	33647.2829279429	28836.994858420832	34.0	29.0	1.SRARP.ENST00000329454.2.missense.100G/W	4	0.7285714285714285	0.7285714285714285	False	False	False	1	False	0
+chr1	16006133	16006134	G	T	ENST00000329454.2	1	169	protein_coding	ENSG00000183888	missense	G/W	100	SRARP	ENST00000329454.2:c.298G>T	ENSP00000332162.2:p.Gly100Trp	HLA-A*29:02	8	8	1	WGCLAQAR	GGCLAQAR	MHCnuggetsI	5829.43	5835.93	1.001	MHCnuggetsI	24.08	25.32	177	1.0	0	0.0	140	0.007	0.082	0.082	1.108	43.5	52.0	0.0127962455153465	0.0064251162111759	0.0035595753070329	0.0035029444827385	99.28660326086955	99.28660326086955	29695.87027952681	29499.406161513143	36.485875	36.485875	5835.93	5829.43	25.32	24.08	41985.61	36751.88	90.0	61.0	28640.0	23699.6	52.0	37.0	42022.88	36378.02	78.0	50.0	1.4e-05	1.7e-05	54.0	51.0	34986.781616071436	27278.95215511154	38.0	31.0	47495.23053304064	47495.23053304064	52.0	52.0	25819.629140564757	28903.470997020624	25.0	29.0	1.SRARP.ENST00000329454.2.missense.100G/W	3	0.21428571428571427	0.7285714285714285	False	False	False	1	False	0
+chr1	16972417	16972418	C	G	ENST00000375541.10	5	2017	protein_coding	ENSG00000058453	missense	S/C	2009	CROCC	ENST00000375541.10:c.6026C>G	ENSP00000364691.4:p.Ser2009Cys	HLA-A*29:02	8	1	8	RRSSAPFC	RRSSAPFS	MHCnuggetsI	5988.38	5974.96	0.998	SMM	26.0	35.0	85	0.988	56	1.0	80	0.0	11.753	2.147	1.049	79.5	77.5	0.0034668780863285	0.0055077373981475	0.0029955445029173	0.0030033801030101	100.0	99.28660326086955	34302.270674557265	34465.12217183127	92.988625	92.988625	5974.96	5988.38	86.56	91.97	42409.75	42891.54	92.0	94.0	33144.32	34798.02	70.0	78.0	43266.26	42430.88	85.0	81.0	9e-06	7e-06	62.0	66.0	50000.0	50000.0	48.0	48.0	34725.61932176095	26647.102961758024	35.0	26.0	40921.354456751775	37320.71882568288	44.0	39.0	2.CROCC.ENST00000375541.10.missense.2009S/C	8	-0.08571428571428578	-0.08571428571428578	False	True	False	1	False	0
+chr1	16972417	16972418	C	G	ENST00000375541.10	5	2017	protein_coding	ENSG00000058453	missense	S/C	2009	CROCC	ENST00000375541.10:c.6026C>G	ENSP00000364691.4:p.Ser2009Cys	HLA-A*29:02	8	2	7	RSSAPFCP	RSSAPFSP	SMM	5219.754	5219.754	1.0	SMM	3.8	3.8	85	0.988	56	1.0	80	0.0	11.753	2.147	0.989	64.937	62.044	0.0170750990509986	0.0583108253777027	0.0033191040526975	0.0038410274517063	99.28660326086955	99.28660326086955	32422.454083719957	32400.048899462992	69.087125	63.8735	5952.44	5984.02	73.97	90.36	40729.2	40369.42	83.0	81.0	34798.02	35559.24	78.0	81.0	37478.3	37014.88	55.0	53.0	1.7e-05	7e-06	51.0	66.0	37739.55580632669	38150.10646105872	40.0	41.0	5219.753570100537	5219.753570100537	3.8	3.8	32281.224489863776	27098.796275090655	33.0	27.0	2.CROCC.ENST00000375541.10.missense.2009S/C	7	0.32857142857142846	0.32857142857142846	False	False	True	1	False	0
+chr1	16972417	16972418	C	G	ENST00000375541.10	5	2017	protein_coding	ENSG00000058453	missense	S/C	2009	CROCC	ENST00000375541.10:c.6026C>G	ENSP00000364691.4:p.Ser2009Cys	HLA-A*29:02	8	3	6	SSAPFCPP	SSAPFSPP	SMM	3031.447	3031.447	1.0	SMM	1.4	1.4	85	0.988	56	1.0	80	0.0	11.753	2.147	0.996	49.0	48.128	0.098813459277153	0.0869735516607761	0.004635326890338	0.0044954204267487	62.74467391304348	62.74467391304348	30918.610070190254	30567.20717676396	47.25637500000001	44.13500000000001	5932.86	5946.47	61.69	70.28	35544.62	36266.01	56.0	59.0	28026.9	28640.0	50.0	52.0	36156.32	35315.37	49.0	46.0	4e-05	1.6e-05	38.0	52.0	42509.44689711614	37333.42327399945	44.0	40.0	3031.447141744229	3031.447141744229	1.4	1.4	11666.752908720136	13897.926162143007	12.0	13.0	2.CROCC.ENST00000375541.10.missense.2009S/C	6	0.21428571428571416	0.32857142857142846	False	False	True	1	False	0
+chr1	16972417	16972418	C	G	ENST00000375541.10	5	2017	protein_coding	ENSG00000058453	missense	S/C	2009	CROCC	ENST00000375541.10:c.6026C>G	ENSP00000364691.4:p.Ser2009Cys	HLA-A*29:02	8	4	5	SAPFCPPS	SAPFSPPS	MHCnuggetsI	5830.52	5778.11	0.991	MHCnuggetsI	24.28	17.11	85	1.988	56	1.0	80	0.0	11.753	2.147	1.061	58.5	59.0	0.0144078508019447	0.004819992929697	0.0034351568937058	0.0033663533299563	99.28660326086955	99.28660326086955	30989.07336299006	30557.53944594346	47.25637500000001	44.13500000000001	5778.11	5830.52	17.11	24.28	39211.81	39389.99	74.0	75.0	32434.8	30396.07	67.0	59.0	40793.12	38535.52	71.0	60.0	1.7e-05	1.1e-05	51.0	58.0	50000.0	50000.0	48.0	48.0	129613.428914014	75622.32054801435	95.0	78.0	44355.75745387479	36893.512089950294	49.0	38.0	2.CROCC.ENST00000375541.10.missense.2009S/C	5	0.21428571428571425	0.21428571428571425	False	False	False	1	False	0
+chr1	16972417	16972418	C	G	ENST00000375541.10	5	2017	protein_coding	ENSG00000058453	missense	S/C	2009	CROCC	ENST00000375541.10:c.6026C>G	ENSP00000364691.4:p.Ser2009Cys	HLA-A*29:02	8	5	4	APFCPPSG	APFSPPSG	MHCnuggetsI	5967.48	5960.33	0.999	SMMPMBEC	28.0	38.0	85	0.988	56	1.0	80	0.0	11.753	2.147	1.043	71.5	66.718	0.0246490016579628	0.0181423835456371	0.0035155383202336	0.0034432383999441	99.28660326086955	99.28660326086955	31407.36925018084	31337.333104871108	54.695	50.764875	5960.33	5967.48	78.74	82.78	43189.57	42831.7	96.0	94.0	35559.24	35367.39	81.0	80.0	43967.07	42784.93	89.0	82.0	3.1e-05	8e-06	42.0	63.0	34610.27293937596	33869.36732649195	38.0	37.0	40146.71361559038	42721.844998681605	43.0	46.0	36639.53893914137	27793.932598641266	38.0	28.0	2.CROCC.ENST00000375541.10.missense.2009S/C	4	-0.10000000000000007	0.21428571428571425	False	False	False	1	False	0
+chr1	16972417	16972418	C	G	ENST00000375541.10	5	2017	protein_coding	ENSG00000058453	missense	S/C	2009	CROCC	ENST00000375541.10:c.6026C>G	ENSP00000364691.4:p.Ser2009Cys	HLA-A*29:02	8	6	3	PFCPPSGP	PFSPPSGP	MHCnuggetsI	5929.88	5945.11	1.003	SMMPMBEC	34.0	36.0	85	0.988	53	1.0	80	0.0	11.753	2.147	0.966	60.44	49.5	0.0018312484025955	0.0018649138510227	0.0033118231499674	0.0033387002836613	99.28660326086955	99.28660326086955	30740.90574033748	30489.060159375287	47.25637500000001	44.13500000000001	5945.11	5929.88	69.42	59.88	34307.16	38026.88	51.0	68.0	31229.48	30893.41	62.0	61.0	42124.85	42832.63	79.0	83.0	2.1e-05	6e-06	48.0	68.0	50000.0	50000.0	48.0	48.0	37553.46371440002	40054.37873654026	39.0	43.0	34749.6152167138	33492.68770563393	36.0	34.0	2.CROCC.ENST00000375541.10.missense.2009S/C	3	-0.10000000000000007	-0.10000000000000007	False	False	True	1	False	0
+chr1	16972417	16972418	C	G	ENST00000375541.10	5	2017	NA	ENSG00000058453	missense	S/C	2009	CROCC	ENST00000375541.10:c.6026C>G	ENSP00000364691.4:p.Ser2009Cys	HLA-A*29:02	8	7	2	FCPPSGPP	FSPPSGPP	MHCnuggetsI	5917.49	5920.34	1.0	MHCflurry	30.892	26.709	85	0.988	56	1.0	80	0.0	11.753	2.147	0.793	66.5	51.175	0.0529707707464695	0.0080852657556533	0.0043629698384238	0.0036376750593724	62.74467391304348	99.28660326086955	27854.61522894876	28543.35284723345	26.708874999999995	30.891874999999995	5920.34	5917.49	54.35	52.77	38187.68	41120.62	69.0	85.0	34053.09	36931.66	75.0	87.0	40197.7	43352.96	68.0	85.0	2.3e-05	4e-06	46.0	75.0	50000.0	50000.0	48.0	48.0	9608.376226408696	53536.50303731279	9.0	58.0	24040.86003443855	33109.30007978277	23.0	34.0	2.CROCC.ENST00000375541.10.missense.2009S/C	2	-0.7285714285714286	-0.10000000000000007	False	False	True	1	False	0
+chr1	16972417	16972418	C	G	ENST00000375541.10	5	2017	protein_coding	ENSG00000058453	missense	S/C	2009	CROCC	ENST00000375541.10:c.6026C>G	ENSP00000364691.4:p.Ser2009Cys	HLA-A*29:02	8	8	1	CPPSGPPE	SPPSGPPE	MHCnuggetsI	5956.84	5940.46	0.997	SMM	16.0	16.0	85	0.988	56	2.0	80	0.0	11.753	2.147	1.024	64.0	NA	0.0085133202373981	0.0027647912502288	0.0038300747966599	0.0036987774902234	99.28660326086955	99.28660326086955	27105.18088685827	27517.78152066251	24.47925	25.544875	5940.46	5956.84	66.44	76.64999999999999	43314.5	43255.05	96.0	96.0	34423.54	34237.82	76.0	75.0	44965.32	43553.72	93.0	87.0	2.3e-05	1.5e-05	46.0	53.0	50000.0	50000.0	48.0	48.0	17007.872833250163	17007.872833250163	16.0	16.0	35233.030518194704	33802.59172937616	36.0	34.0	2.CROCC.ENST00000375541.10.missense.2009S/C	1	-1.5857142857142856	-0.7285714285714286	True	False	False	1	False	0
+chr1	22576425	22576426	C	A	ENST00000166244.8	2	1005	protein_coding	ENSG00000070886	missense	N/K	123	EPHA8	ENST00000166244.8:c.369C>A	ENSP00000166244.3:p.Asn123Lys	HLA-A*29:02	8	1	8	GTCKETFK	GTCKETFN	MHCnuggetsI	5946.43	5984.91	1.006	PickPocket	6.8	32.0	114	0.272	0	0.0	82	0.0	0.001	0.001	1.186	32.0	70.0	0.0097893625497818	0.0387631803750991	0.0032783537149027	0.0041438699747915	99.28660326086955	62.74467391304348	31978.79473422675	27896.932977370703	59.0625	27.987124999999995	5984.91	5946.43	90.7	70.25	41635.48	41129.06	88.0	85.0	30893.41	20701.6	61.0	29.0	44260.61	36649.84	90.0	51.0	3e-06	6.1e-05	79.0	33.0	28795.3614360886	7945.936747596634	32.0	6.8	24640.52744948151	23913.877487907896	24.0	23.0	30545.694226700492	30970.6271616828	31.0	31.0	3.EPHA8.ENST00000166244.8.missense.123N/K	3	-1.0571428571428572	-0.5571428571428572	False	False	False	1	False	0
+chr1	22576425	22576426	C	A	ENST00000374644.8	1	495	protein_coding	ENSG00000070886	missense	N/K	123	EPHA8	ENST00000374644.8:c.369C>A	ENSP00000363775.4:p.Asn123Lys	HLA-A*29:02	8	1	8	GTCKETFK	GTCKETFN	MHCnuggetsI	5946.43	5984.91	1.006	PickPocket	6.8	32.0	114	0.272	0	0.0	82	0.0	0.001	0.0	1.186	32.0	70.0	0.0097893625497818	0.0387631803750991	0.0032783537149027	0.0041438699747915	99.28660326086955	62.74467391304348	31978.79473422675	27896.932977370703	59.0625	27.987124999999995	5984.91	5946.43	90.7	70.25	41635.48	41129.06	88.0	85.0	30893.41	20701.6	61.0	29.0	44260.61	36649.84	90.0	51.0	3e-06	6.1e-05	79.0	33.0	28795.3614360886	7945.936747596634	32.0	6.8	24640.52744948151	23913.877487907896	24.0	23.0	30545.694226700492	30970.6271616828	31.0	31.0	4.EPHA8.ENST00000374644.8.missense.123N/K	3	-1.0571428571428572	-0.5571428571428572	False	False	False	1	False	0
\ No newline at end of file