Remove support for the NetMHCcons prediction algorithm

docs/pvacbind/features.rst

@@ -141,12 +141,6 @@ for a variety of additional algorithms.
      - Presentation
      - yes
      - no
-   * - NetMHCcons
-     - 1.1
-     - MHC Class I
-     - Binding
-     - yes
-     - yes
    * - NetMHCpan
      - 4.1
      - MHC Class I

docs/pvacfuse/features.rst

@@ -141,12 +141,6 @@ for a variety of additional algorithms.
      - Presentation
      - yes
      - no
-   * - NetMHCcons
-     - 1.1
-     - MHC Class I
-     - Binding
-     - yes
-     - yes
    * - NetMHCpan
      - 4.1
      - MHC Class I

docs/pvacseq/features.rst

@@ -145,12 +145,6 @@ for a variety of additional algorithms.
      - Presentation
      - yes
      - no
-   * - NetMHCcons
-     - 1.1
-     - MHC Class I
-     - Binding
-     - yes
-     - yes
    * - NetMHCpan
      - 4.1
      - MHC Class I

docs/pvacseq/frequently_asked_questions.rst

@@ -78,13 +78,7 @@ There are a number of factors that determine the number of IEDB calls to be made
 
   **Speedup suggestion**: Reduce the number of prediction algorithms,
   epitope lengths, and/or HLA-alleles to the ones that will be the most
-  meaningful for your analysis. For example, the NetMHCcons method is
-  already a consensus method between NetMHC, NetMHCpan, and PickPocket.
-  If NetMHCcons is chosen, you may want to omit the underlying prediction
-  methods. Likewise, if you want to run NetMHC, NetMHCpan, and PickPocket
-  individually, you may want to skip NetMHCcons. Note that PickPocket is no
-  longer a prediction algorithm supported by IEDB, so it's been removed as
-  an algorithm in pVACtools.
+  meaningful for your analysis.
 
 - ``--downstream-sequence-length`` parameter value
 

docs/pvacsplice/features.rst

@@ -143,12 +143,6 @@ for a variety of additional algorithms.
      - Presentation
      - yes
      - no
-   * - NetMHCcons
-     - 1.1
-     - MHC Class I
-     - Binding
-     - yes
-     - yes
    * - NetMHCpan
      - 4.1
      - MHC Class I

pvactools/lib/aggregate_all_epitopes.py

@@ -134,7 +134,7 @@ def get_best_mut_line(self, df, key):
 
     def determine_used_ic50_algorithms(self):
         headers = pd.read_csv(self.input_file, delimiter="\t", nrows=0).columns.tolist()
-        potential_algorithms = ["NetMHCpan", "NetMHC", "NetMHCcons", "SMM", "SMMPMBEC", "MHCflurry", "MHCnuggetsI", "NetMHCIIpan", "SMMalign", "NNalign", "MHCnuggetsII", "TLBind"]
+        potential_algorithms = ["NetMHCpan", "NetMHC", "SMM", "SMMPMBEC", "MHCflurry", "MHCnuggetsI", "NetMHCIIpan", "SMMalign", "NNalign", "MHCnuggetsII", "TLBind"]
         prediction_algorithms = []
         for algorithm in potential_algorithms:
             if "{} MT IC50 Score".format(algorithm) in headers or "{} IC50 Score".format(algorithm) in headers:
@@ -152,7 +152,7 @@ def determine_used_binding_score_algorithms(self):
 
     def determine_used_binding_percentile_algorithms(self):
         headers = pd.read_csv(self.input_file, delimiter="\t", nrows=0).columns.tolist()
-        potential_algorithms = ["NetMHCpan", "NetMHC", "NetMHCcons", "SMM", "SMMPMBEC", "MHCflurry", "MHCnuggetsI", "NetMHCIIpan", "SMMalign", "NNalign", "MHCnuggetsII", "MixMHCpred", "TLBind"]
+        potential_algorithms = ["NetMHCpan", "NetMHC", "SMM", "SMMPMBEC", "MHCflurry", "MHCnuggetsI", "NetMHCIIpan", "SMMalign", "NNalign", "MHCnuggetsII", "MixMHCpred", "TLBind"]
         prediction_algorithms = []
         for algorithm in potential_algorithms:
             if "{} MT Percentile".format(algorithm) in headers or "{} Percentile".format(algorithm) in headers:

pvactools/lib/prediction_class.py

@@ -829,11 +829,6 @@ class SMM(IEDBMHCI):
     def iedb_prediction_method(self):
         return 'smm'
 
-class NetMHCcons(IEDBMHCI):
-    @property
-    def iedb_prediction_method(self):
-        return 'netmhccons'
-
 class MHCII(PredictionClass, metaclass=ABCMeta):
     @property
     def needs_epitope_length(self):