diff --git a/README.md b/README.md
index 9589280..c09cf92 100644
--- a/README.md
+++ b/README.md
@@ -15,7 +15,7 @@ to determine probabilistic annotations.
   <img align="center" src="resources/snekmer_workflow.svg">
 </p>
 
-There are three operation modes for Snekmer: `cluster`, `model`, and `search`.
+There are 5 operation modes for Snekmer: `cluster`, `model`, `search`, `learn`, and `apply`.
 
 **Cluster mode:** The user supplies files containing sequences in an appropriate format (e.g. FASTA).
 Snekmer applies the relevant workflow steps and outputs the resulting clustering results in tabular form (.CSV),
@@ -30,6 +30,16 @@ displays K-fold cross validation results in the form of figures (AUC ROC and PR
 and the models they wish to search their sequences against. Snekmer applies the relevant workflow steps
 and outputs a table for each file containing model annotation probabilities for the given sequences.
 
+**Learn mode:** The user supplies files containing sequences in an appropriate format (e.g. FASTA)
+as well as an annotation file. Snekmer generates a kmer counts matrix with the summed kmer distribution
+of each annotation recognized from the sequence ID. Snekmer then performs a self-evaluation to assess
+confidence levels. There are two outputs, a counts matrix, and a global confidence distribution.
+
+**Apply mode:** The user supplies files containing sequences in an appropriate format (e.g. FASTA)
+and the outputs received from Learn. Snekmer uses cosine distance to predict the annotation of each
+sequence from the kmer counts matrix. The output is a table for each file containing sequence annotation
+predictions with confidence levels.
+
 ## How to Use Snekmer
 
 For installation instructions, documentation, and more, refer to