link to paper, output clarification

README.md

@@ -1,5 +1,7 @@
 # hmMHC 
-A hidden Markov model-based MHC II binding predictor. Currently, only H2-IAb predictions are supported.
+A hidden Markov model-based MHC II binding predictor. Currently, only H2-IAb predictions are supported. The predictor is described in the paper
+
+Elise Alspach et al. MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature (2019), available at https://www.nature.com/articles/s41586-019-1671-8
 
 ## Installation
 hmMHC can be installed on Linux and macOS via a combination of conda and pip install. Windows is not supported. Python 3 is not supported.
@@ -25,6 +27,14 @@ peptides = ['VNGYNEAIVHVVETP', 'IKSEHPGLSIGDVAK', 'KESVVSGKAVPREEL']
 predictor.predict(peptides)
 ```
 
+## Output
+
+The predictor outputs a list of peptides with the predicted -10 log odds scores and corresponding percentile ranks. Percentile ranks are computed from -10 log odds scores based on model calibration on a large set of random natural peptides. For both metrics, smaller values correspond to higher binding likelihood. See Methods section in the paper for further details.
+
 ## Dependencies
 
 hmMHC relies on General Hidden Markov Model library (GHMM) by A. Schliep et al., see http://ghmm.sourceforge.net/.
+
+## Latest version
+
+The latest version of hmMHC is available at https://github.com/artyomovlab/hmmhc.