docs

docs/commandline_tools.rst

@@ -18,4 +18,38 @@ See also the :ref:`tutorial <commandline_tutorial>`.
 .. autoprogram:: mhcflurry.downloads_command:parser
     :prog: mhcflurry-downloads
 
+.. _mhcflurry-class1-train-allele-specific-models:
+
+.. autoprogram:: mhcflurry.train_allele_specific_models_command:parser
+    :prog: mhcflurry-class1-train-allele-specific-models
+
+.. _mhcflurry-class1-select-allele-specific-models:
+
+.. autoprogram:: mhcflurry.select_allele_specific_models_command:parser
+    :prog: mhcflurry-class1-select-allele-specific-models
+
+.. _mhcflurry-class1-train-pan-allele-models:
+
+.. autoprogram:: mhcflurry.train_pan_allele_models_command:parser
+    :prog: mhcflurry-class1-train-pan-allele-models
+
+.. _mhcflurry-class1-select-pan-allele-models:
+
+.. autoprogram:: mhcflurry.select_pan_allele_models_command:parser
+    :prog: mhcflurry-class1-select-pan-allele-models
+
+.. _mhcflurry-class1-train-processing-models:
+
+.. autoprogram:: mhcflurry.train_processing_models_command:parser
+    :prog: mhcflurry-class1-train-processing-models
+
+.. _mhcflurry-class1-select-processing-models:
+
+.. autoprogram:: mhcflurry.select_processing_models_command:parser
+    :prog: mhcflurry-class1-select-processing-models
+
+.. _mhcflurry-class1-train-presentation-models:
+
+.. autoprogram:: mhcflurry.train_presentation_models_command:parser
+    :prog: mhcflurry-class1-train-presentation-models
 

docs/commandline_tutorial.rst

@@ -33,7 +33,7 @@ as well as an antigen processing (AP) predictor.
 .. note::
 
     The code we use for *generating* the downloads is in the
-    ``downloads_generation`` directory in the repository.
+    ``downloads_generation`` directory in the repository (https://github.com/openvax/mhcflurry/tree/master/downloads-generation)
 
 
 Generating predictions
@@ -99,7 +99,7 @@ sequences:
 .. literalinclude:: /example.fasta
 
 Here's the ``mhcflurry-predict-scan`` invocation to scan the proteins for
-binders to either of two MHC I genotypes:
+binders to either of two MHC I genotypes (using a 100 nM threshold):
 
 .. command-output::
     mhcflurry-predict-scan
@@ -107,8 +107,8 @@ binders to either of two MHC I genotypes:
         --alleles
             HLA-A*02:01,HLA-A*03:01,HLA-B*57:01,HLA-B*45:01,HLA-C*02:02,HLA-C*07:02
             HLA-A*01:01,HLA-A*02:06,HLA-B*44:02,HLA-B*07:02,HLA-C*01:02,HLA-C*03:01
-        --results-filtered affinity_percentile
-        --threshold-affinity-percentile 1.0
+        --results-filtered affinity
+        --threshold-affinity 100
     :nostderr:
 
 See the :ref:`mhcflurry-predict-scan` docs for more options.

docs/example.fasta

 >protein1
-MDSKGSSQKGSRLLLLLVVSNLLLCQGVVSTPVCPNGPGNCQV
-EMFNEFDKRYAQGKGFITMALNSCHTSSLPTPEDKEQAQQTHH
+MSSSSTPVCPNGPGNCQV
 >protein2
-VTEVRGMKGAPDAILSRAIEIEEENKRLLEGMEMIFGQVIPGA
-ARYSAFYNLLHCLRRDSSKIDTYLKLLNCRIIYNNNC
+MVENKRLLEGMEMIFGQVIPGA

docs/intro.rst

@@ -2,7 +2,7 @@ Introduction and setup
 =======================
 
 MHCflurry is an open source package for peptide/MHC I binding affinity prediction. It
-attempts to provide competitive accuracy with a fast and documented implementation.
+aims to provide competitive accuracy with a fast and documented implementation.
 
 You can download pre-trained MHCflurry models fit to mass spec-identified MHC I
 ligands and peptide/MHC affinity measurements deposited in IEDB (plus a few other
@@ -63,7 +63,7 @@ tensorflow.
 
 .. code-block:: shell
 
-    $ conda create -q -n mhcflurry-env python=3.6 tensorflow
+    $ conda create -q -n mhcflurry-env python=3.6 'tensorflow<2.0.0'
     $ source activate mhcflurry-env
 
 Then continue as above: