diff --git a/docs/pvacbind/features.rst b/docs/pvacbind/features.rst
index eba3341ce..56323155f 100644
--- a/docs/pvacbind/features.rst
+++ b/docs/pvacbind/features.rst
@@ -46,7 +46,11 @@ and immunogenicity algorithms.
 By using the IEDB RESTful web interface, pVACbind leverages their extensive support of different prediction algorithms.
 
 In addition to IEDB-supported prediction algorithms, we've also added support
-for a variety of additional algorithms.
+for a variety of additional algorithms. The following table lists all prediction algorithms supported
+by pVACbind, whether the predictor is a MHC class I or class II algorithm, what type of scoring
+the predictor returns (binding, presentation, or immunogenicity), whether the algorithm supports
+percentile ranks natively, whether the predictor supports normalized percentile ranks (``--use-normalized-percentiles
+flag), and whether the ``select`` algorithm shortcut includes this predictor.
 
 .. list-table::
    :header-rows: 1
@@ -57,138 +61,161 @@ for a variety of additional algorithms.
      - Prediction Type
      - Supports Percentile Ranks?
      - Supports Normalized Percentile Ranks?
+     - Included On "select" Strategy?
    * - BigMHC_EL
      -
      - MHC Class I
      - Presentation
      - no
      - yes
+     - yes
    * - BigMHC_IM
      -
      - MHC Class I
      - Immunogenicity
      - no
      - yes
+     - yes
    * - DeepImmuno
      -
      - MHC Class I
      - Immunogenicity
      - no
      - yes
-   * - ImmuoScope_IM
+     - yes
+   * - ImmuScope_IM
      -
      - MHC Class II
      - Immunogenicity
      - no
      - no
+     - yes
    * - MHCflurry
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - MHCflurryEL
      -
      - MHC Class I
      - Presentation, Processing
      - yes (Presentation only)
      - yes (Presentation and Processing)
+     - yes (Presentation and Processing)
    * - MHCnuggetsI
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - MHCnuggetsII
      -
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
    * - MixMHC2pred
      -
      - MHC Class II
      - Presentation
      - yes
      - no
+     - yes
    * - MixMHCpred
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - NNalign
      - 2.3
      - MHC Class II
      - Binding
      - yes
      - no
+     - no
    * - NetMHC
      - 4.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - NetMHCIIpan
      - 4.0 (not supported by standalone IEDB), 4.1 (default), 4.2., 4.3
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
    * - NetMHCIIpanEL
      - 4.0 (not supported by standalone IEDB), 4.1 (default), 4.2., 4.3
      - MHC Class II
      - Presentation
      - yes
      - no
+     - yes
    * - NetMHCcons
      - 1.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - NetMHCpan
      - 4.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - NetMHCpanEL
      - 4.1
      - MHC Class I
      - Presentation
      - yes
      - yes
+     - yes
    * - PRIME
      -
      - MHC Class I
      - Immunogenicity
      - yes
      - yes
-   * - Pickpocket
+     - yes
+   * - PickPocket
      - 1.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - SMM
      - 1.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - SMMPMBEC
      - 1.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - SMMalign
      - 1.1
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
 
 **Calculation of normalized percentiles**
 
diff --git a/docs/pvacfuse/features.rst b/docs/pvacfuse/features.rst
index a8aef3dd7..d8c987040 100644
--- a/docs/pvacfuse/features.rst
+++ b/docs/pvacfuse/features.rst
@@ -46,7 +46,11 @@ and immunogenicity algorithms.
 By using the IEDB RESTful web interface, pVACfuse leverages their extensive support of different prediction algorithms.
 
 In addition to IEDB-supported prediction algorithms, we've also added support
-for a variety of additional algorithms.
+for a variety of additional algorithms. The following table lists all algorithms supported
+by pVACfuse, whether the algorithm is a MHC class I or class II algorithm, what type of scoring
+the algorithm returns (binding, presentation, or immunogenicity), whether it supports
+percentile ranks natively, supports normalized percentile ranks (``--use-normalized-percentiles``
+flag), and whether the ``select`` algorithm shortcut includes this algorithm.
 
 .. list-table::
    :header-rows: 1
@@ -57,138 +61,161 @@ for a variety of additional algorithms.
      - Prediction Type
      - Supports Percentile Ranks?
      - Supports Normalized Percentile Ranks?
+     - Included On "select" Strategy?
    * - BigMHC_EL
      -
      - MHC Class I
      - Presentation
      - no
      - yes
+     - yes
    * - BigMHC_IM
      -
      - MHC Class I
      - Immunogenicity
      - no
      - yes
+     - yes
    * - DeepImmuno
      -
      - MHC Class I
      - Immunogenicity
      - no
      - yes
-   * - ImmuoScope_IM
+     - yes
+   * - ImmuScope_IM
      -
      - MHC Class II
      - Immunogenicity
      - no
      - no
+     - yes
    * - MHCflurry
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - MHCflurryEL
      -
      - MHC Class I
      - Presentation, Processing
      - yes (Presentation only)
      - yes (Presentation and Processing)
+     - yes (Presentation and Processing)
    * - MHCnuggetsI
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - MHCnuggetsII
      -
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
    * - MixMHC2pred
      -
      - MHC Class II
      - Presentation
      - yes
      - no
+     - yes
    * - MixMHCpred
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - NNalign
      - 2.3
      - MHC Class II
      - Binding
      - yes
      - no
+     - no
    * - NetMHC
      - 4.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - NetMHCIIpan
      - 4.0 (not supported by standalone IEDB), 4.1 (default), 4.2., 4.3
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
    * - NetMHCIIpanEL
      - 4.0 (not supported by standalone IEDB), 4.1 (default), 4.2., 4.3
      - MHC Class II
      - Presentation
      - yes
      - no
+     - yes
    * - NetMHCcons
      - 1.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - NetMHCpan
      - 4.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - NetMHCpanEL
      - 4.1
      - MHC Class I
      - Presentation
      - yes
      - yes
+     - yes
    * - PRIME
      -
      - MHC Class I
      - Immunogenicity
      - yes
      - yes
-   * - Pickpocket
+     - yes
+   * - PickPocket
      - 1.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - SMM
      - 1.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - SMMPMBEC
      - 1.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - SMMalign
      - 1.1
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
 
 **Calculation of normalized percentiles**
 
diff --git a/docs/pvacseq/features.rst b/docs/pvacseq/features.rst
index 354bd3b1d..06e6e5f47 100644
--- a/docs/pvacseq/features.rst
+++ b/docs/pvacseq/features.rst
@@ -50,7 +50,11 @@ and immunogenicity algorithms.
 By using the IEDB RESTful web interface, pVACseq leverages their extensive support of different prediction algorithms.
 
 In addition to IEDB-supported prediction algorithms, we've also added support
-for a variety of additional algorithms.
+for a variety of additional algorithms. The following table lists all algorithms supported
+by pVACseq, whether the algorithm is a MHC class I or class II algorithm, what type of scoring
+the algorithm returns (binding, presentation, or immunogenicity), whether it supports
+percentile ranks natively, supports normalized percentile ranks (``--use-normalized-percentiles``
+flag), and whether the ``select`` algorithm shortcut includes this algorithm.
 
 .. list-table::
    :header-rows: 1
@@ -61,138 +65,161 @@ for a variety of additional algorithms.
      - Prediction Type
      - Supports Percentile Ranks?
      - Supports Normalized Percentile Ranks?
+     - Included On "select" Strategy?
    * - BigMHC_EL
      -
      - MHC Class I
      - Presentation
      - no
      - yes
+     - yes
    * - BigMHC_IM
      -
      - MHC Class I
      - Immunogenicity
      - no
      - yes
+     - yes
    * - DeepImmuno
      -
      - MHC Class I
      - Immunogenicity
      - no
      - yes
-   * - ImmuoScope_IM
+     - yes
+   * - ImmuScope_IM
      -
      - MHC Class II
      - Immunogenicity
      - no
      - no
+     - yes
    * - MHCflurry
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - MHCflurryEL
      -
      - MHC Class I
      - Presentation, Processing
      - yes (Presentation only)
      - yes (Presentation and Processing)
+     - yes (Presentation and Processing)
    * - MHCnuggetsI
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - MHCnuggetsII
      -
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
    * - MixMHC2pred
      -
      - MHC Class II
      - Presentation
      - yes
      - no
+     - yes
    * - MixMHCpred
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - NNalign
      - 2.3
      - MHC Class II
      - Binding
      - yes
      - no
+     - no
    * - NetMHC
      - 4.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - NetMHCIIpan
      - 4.0 (not supported by standalone IEDB), 4.1 (default), 4.2., 4.3
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
    * - NetMHCIIpanEL
      - 4.0 (not supported by standalone IEDB), 4.1 (default), 4.2., 4.3
      - MHC Class II
      - Presentation
      - yes
      - no
+     - yes
    * - NetMHCcons
      - 1.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - NetMHCpan
      - 4.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - NetMHCpanEL
      - 4.1
      - MHC Class I
      - Presentation
      - yes
      - yes
+     - yes
    * - PRIME
      -
      - MHC Class I
      - Immunogenicity
      - yes
      - yes
-   * - Pickpocket
+     - yes
+   * - PickPocket
      - 1.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - SMM
      - 1.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - SMMPMBEC
      - 1.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - SMMalign
      - 1.1
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
 
 **Calculation of normalized percentiles**
 
diff --git a/docs/pvacsplice/features.rst b/docs/pvacsplice/features.rst
index 03bac3e80..c721c24de 100644
--- a/docs/pvacsplice/features.rst
+++ b/docs/pvacsplice/features.rst
@@ -48,7 +48,11 @@ and immunogenicity algorithms.
 By using the IEDB RESTful web interface, pVACsplice leverages their extensive support of different prediction algorithms.
 
 In addition to IEDB-supported prediction algorithms, we've also added support
-for a variety of additional algorithms.
+for a variety of additional algorithms. The following table lists all algorithms supported
+by pVACsplice, whether the algorithm is a MHC class I or class II algorithm, what type of scoring
+the algorithm returns (binding, presentation, or immunogenicity), whether it supports
+percentile ranks natively, supports normalized percentile ranks (``--use-normalized-percentiles``
+flag), and whether the ``select`` algorithm shortcut includes this algorithm.
 
 .. list-table::
    :header-rows: 1
@@ -59,138 +63,161 @@ for a variety of additional algorithms.
      - Prediction Type
      - Supports Percentile Ranks?
      - Supports Normalized Percentile Ranks?
+     - Included On "select" Strategy?
    * - BigMHC_EL
      -
      - MHC Class I
      - Presentation
      - no
      - yes
+     - yes
    * - BigMHC_IM
      -
      - MHC Class I
      - Immunogenicity
      - no
      - yes
+     - yes
    * - DeepImmuno
      -
      - MHC Class I
      - Immunogenicity
      - no
      - yes
-   * - ImmuoScope_IM
+     - yes
+   * - ImmuScope_IM
      -
      - MHC Class II
      - Immunogenicity
      - no
      - no
+     - yes
    * - MHCflurry
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - MHCflurryEL
      -
      - MHC Class I
      - Presentation, Processing
      - yes (Presentation only)
      - yes (Presentation and Processing)
+     - yes (Presentation and Processing)
    * - MHCnuggetsI
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - MHCnuggetsII
      -
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
    * - MixMHC2pred
      -
      - MHC Class II
      - Presentation
      - yes
      - no
+     - yes
    * - MixMHCpred
      -
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - NNalign
      - 2.3
      - MHC Class II
      - Binding
      - yes
      - no
+     - no
    * - NetMHC
      - 4.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - NetMHCIIpan
      - 4.0 (not supported by standalone IEDB), 4.1 (default), 4.2., 4.3
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
    * - NetMHCIIpanEL
      - 4.0 (not supported by standalone IEDB), 4.1 (default), 4.2., 4.3
      - MHC Class II
      - Presentation
      - yes
      - no
+     - yes
    * - NetMHCcons
      - 1.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - NetMHCpan
      - 4.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - NetMHCpanEL
      - 4.1
      - MHC Class I
      - Presentation
      - yes
      - yes
+     - yes
    * - PRIME
      -
      - MHC Class I
      - Immunogenicity
      - yes
      - yes
-   * - Pickpocket
+     - yes
+   * - PickPocket
      - 1.1
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - SMM
      - 1.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - no
    * - SMMPMBEC
      - 1.0
      - MHC Class I
      - Binding
      - yes
      - yes
+     - yes
    * - SMMalign
      - 1.1
      - MHC Class II
      - Binding
      - yes
      - no
+     - yes
 
 **Calculation of normalized percentiles**
 
diff --git a/pvactools/lib/prediction_class.py b/pvactools/lib/prediction_class.py
index 95a6bff1a..70a007b15 100644
--- a/pvactools/lib/prediction_class.py
+++ b/pvactools/lib/prediction_class.py
@@ -190,9 +190,9 @@ def prediction_methods(cls):
         return sorted([prediction_class.__name__ for prediction_class in cls.prediction_classes()])
 
     @classmethod
-    def prediction_methods_with_all(cls):
+    def prediction_methods_with_shortcuts(cls):
         methods = cls.prediction_methods()
-        methods.extend(['all', 'all_class_i', 'all_class_ii'])
+        methods.extend(['all', 'all_class_i', 'all_class_ii', 'select', 'select_class_i', 'select_class_ii'])
         return methods
 
     @classmethod
@@ -355,6 +355,10 @@ class MHCI(PredictionClass, metaclass=ABCMeta):
     def needs_epitope_length(self):
         return True
 
+    @classmethod
+    def select_prediction_methods(cls):
+        return ["BigMHC_EL", "BigMHC_IM", "DeepImmuno", "MHCflurry", "MHCflurryEL", "MixMHCpred", "NetMHCpan", "NetMHCpanEL", "PRIME", "PickPocket", "SMMPMBEC"]
+
 class DeepImmuno(MHCI):
     def valid_allele_names(self):
         base_dir          = os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..'))
@@ -890,6 +894,10 @@ class MHCII(PredictionClass, metaclass=ABCMeta):
     def needs_epitope_length(self):
         return False
 
+    @classmethod
+    def select_prediction_methods(cls):
+        return ["ImmuScope_IM", "MHCnuggetsII", "MixMHC2pred", "NetMHCIIpan", "NetMHCIIpanEL", "SMMalign"]
+
 class MHCnuggetsII(MHCII, MHCnuggets):
     def valid_allele_names(self):
         return self.valid_allele_names_for_class('class_ii')
diff --git a/pvactools/lib/prediction_class_utils.py b/pvactools/lib/prediction_class_utils.py
index fc06ea757..04383b564 100644
--- a/pvactools/lib/prediction_class_utils.py
+++ b/pvactools/lib/prediction_class_utils.py
@@ -5,14 +5,25 @@
 def split_algorithms(prediction_algorithms):
     if 'all' in prediction_algorithms:
         return (sorted(MHCI.prediction_methods()), sorted(MHCII.prediction_methods()))
+
     class_i_prediction_algorithms = set()
     class_ii_prediction_algorithms = set()
+    if 'select' in prediction_algorithms:
+        class_i_prediction_algorithms.update(MHCI.select_prediction_methods())
+        class_ii_prediction_algorithms.update(MHCII.select_prediction_methods())
     if 'all_class_i' in prediction_algorithms:
-        class_i_prediction_algorithms = set(MHCI.prediction_methods())
-        prediction_algorithms.remove('all_class_i')
+        class_i_prediction_algorithms.update(MHCI.prediction_methods())
+    if 'select_class_i' in prediction_algorithms:
+        class_i_prediction_algorithms.update(MHCI.select_prediction_methods())
     if 'all_class_ii' in prediction_algorithms:
-        class_ii_prediction_algorithms = set(MHCII.prediction_methods())
-        prediction_algorithms.remove('all_class_ii')
+        class_ii_prediction_algorithms.update(MHCII.prediction_methods())
+    if 'select_class_ii' in prediction_algorithms:
+        class_ii_prediction_algorithms.update(MHCII.select_prediction_methods())
+
+    for shortcut in ['select', 'select_class_i', 'select_class_ii', 'all_class_i', 'all_class_ii']:
+        if shortcut in prediction_algorithms:
+            prediction_algorithms.remove(shortcut)
+
     for prediction_algorithm in prediction_algorithms:
         prediction_class = globals()[prediction_algorithm]
         prediction_class_object = prediction_class()
diff --git a/pvactools/lib/run_argument_parser.py b/pvactools/lib/run_argument_parser.py
index 759adde81..663eac258 100644
--- a/pvactools/lib/run_argument_parser.py
+++ b/pvactools/lib/run_argument_parser.py
@@ -33,7 +33,7 @@ def __init__(self, tool_name, input_file_help):
         )
         parser.add_argument(
             "prediction_algorithms",
-            choices=PredictionClass.prediction_methods_with_all(),
+            choices=PredictionClass.prediction_methods_with_shortcuts(),
             nargs="+",
             help="The epitope prediction algorithms to use. Multiple prediction algorithms can be specified, separated by spaces.",
         )
diff --git a/tests/test_run_utils.py b/tests/test_run_utils.py
index 7a239915f..39a73d2fa 100644
--- a/tests/test_run_utils.py
+++ b/tests/test_run_utils.py
@@ -21,6 +21,65 @@ def setUpClass(cls):
     def test_module_compiles(self):
         self.assertTrue(py_compile.compile(self.utils_path))
 
+    def test_split_algorithms(self):
+        #individual shortcuts
+        self.assertEqual(
+            split_algorithms(["all"]),
+            (['BigMHC_EL', 'BigMHC_IM', 'DeepImmuno', 'MHCflurry', 'MHCflurryEL', 'MHCnuggetsI', 'MixMHCpred', 'NetMHC', 'NetMHCcons', 'NetMHCpan', 'NetMHCpanEL', 'PRIME', 'PickPocket', 'SMM', 'SMMPMBEC', 'TLBind', 'TLImm'], ['ImmuScope_IM', 'MHCnuggetsII', 'MixMHC2pred', 'NNalign', 'NetMHCIIpan', 'NetMHCIIpanEL', 'SMMalign'])
+        )
+        self.assertEqual(
+            split_algorithms(["all_class_i"]),
+            (['BigMHC_EL', 'BigMHC_IM', 'DeepImmuno', 'MHCflurry', 'MHCflurryEL', 'MHCnuggetsI', 'MixMHCpred', 'NetMHC', 'NetMHCcons', 'NetMHCpan', 'NetMHCpanEL', 'PRIME', 'PickPocket', 'SMM', 'SMMPMBEC', 'TLBind', 'TLImm'], [])
+        )
+        self.assertEqual(
+            split_algorithms(["all_class_ii"]),
+            ([], ['ImmuScope_IM', 'MHCnuggetsII', 'MixMHC2pred', 'NNalign', 'NetMHCIIpan', 'NetMHCIIpanEL', 'SMMalign'])
+        )
+        self.assertEqual(
+            split_algorithms(["select"]),
+            (['BigMHC_EL', 'BigMHC_IM', 'DeepImmuno', 'MHCflurry', 'MHCflurryEL', 'MixMHCpred', 'NetMHCpan', 'NetMHCpanEL', 'PRIME', 'PickPocket', 'SMMPMBEC'], ['ImmuScope_IM', 'MHCnuggetsII', 'MixMHC2pred', 'NetMHCIIpan', 'NetMHCIIpanEL', 'SMMalign'])
+        )
+        self.assertEqual(
+            split_algorithms(["select_class_i"]),
+            (['BigMHC_EL', 'BigMHC_IM', 'DeepImmuno', 'MHCflurry', 'MHCflurryEL', 'MixMHCpred', 'NetMHCpan', 'NetMHCpanEL', 'PRIME', 'PickPocket', 'SMMPMBEC'], [])
+        )
+        self.assertEqual(
+            split_algorithms(["select_class_ii"]),
+            ([], ['ImmuScope_IM', 'MHCnuggetsII', 'MixMHC2pred', 'NetMHCIIpan', 'NetMHCIIpanEL', 'SMMalign'])
+        )
+
+        #Combining all and select gives you all
+        self.assertEqual(
+            split_algorithms(["all", "select"]),
+            split_algorithms(["all"])
+        )
+        self.assertEqual(
+            split_algorithms(["all_class_i", "select_class_i"]),
+            split_algorithms(["all_class_i"])
+        )
+        self.assertEqual(
+            split_algorithms(["all_class_ii", "select_class_ii"]),
+            split_algorithms(["all_class_ii"])
+        )
+
+        #Combining shortcuts with individual algorithms gives you the superset
+        self.assertEqual(
+            split_algorithms(["all_class_i", "NNalign"]),
+            (['BigMHC_EL', 'BigMHC_IM', 'DeepImmuno', 'MHCflurry', 'MHCflurryEL', 'MHCnuggetsI', 'MixMHCpred', 'NetMHC', 'NetMHCcons', 'NetMHCpan', 'NetMHCpanEL', 'PRIME', 'PickPocket', 'SMM', 'SMMPMBEC', 'TLBind', 'TLImm'], ['NNalign'])
+        )
+        self.assertEqual(
+            split_algorithms(["all_class_ii", "NetMHC"]),
+            (['NetMHC'], ['ImmuScope_IM', 'MHCnuggetsII', 'MixMHC2pred', 'NNalign', 'NetMHCIIpan', 'NetMHCIIpanEL', 'SMMalign'])
+        )
+        self.assertEqual(
+            split_algorithms(["select_class_i", "NetMHC"]),
+            (['BigMHC_EL', 'BigMHC_IM', 'DeepImmuno', 'MHCflurry', 'MHCflurryEL', 'MixMHCpred', 'NetMHC', 'NetMHCpan', 'NetMHCpanEL', 'PRIME', 'PickPocket', 'SMMPMBEC'], [])
+        )
+        self.assertEqual(
+            split_algorithms(["select_class_ii", "NNalign"]),
+            ([], ['ImmuScope_IM', 'MHCnuggetsII', 'MixMHC2pred', 'NNalign', 'NetMHCIIpan', 'NetMHCIIpanEL', 'SMMalign'])
+        )
+
     def test_combine_class_ii_alleles(self):
         self.assertEqual(
             sorted(combine_class_ii_alleles(["DQA1*06:02", "DQB1*06:43", "DQB1*06:44"])),