Antibody Epitope Prediction - Standalone version 3.1
===========================================================

Release Note
------------
2020-05-12
* This release will run only under Python 3.6 or higher.



Introduction
------------
This package contains a collection of methods to predict linear B cell epitopes based on
sequence characteristics of the antigen using amino acid scales and HMMs. The collection
is a mixture of pythons scripts and linux environment specific binaries for Bepipred method.


Prerequisites:
-------------
+ Linux 64-bit environment
  * http://www.ubuntu.com/
    - This distribution has been tested on Linux/Ubuntu 64 bit system.

+ Python 3.6 or higher
  * http://www.python.org/

+ numpy
  * https://pypi.python.org/pypi/numpy
    - Under ubuntu: pip install numpy==1.16.6

+ matplotlib (tested on version 2.0.0)
  * https://matplotlib.org/downloads.html
    - Under ubuntu: pip install matplotlib==2.0.0

Dependencies for Bepipred 2.0 (optional):
-------------
+ Python 3.6
  * http://www.python.org/

+ scipy
  * https://www.scipy.org/
    - Under ubuntu: pip install scipy==1.2.3

+ scikit-learn (tested on version 0.17)
  * http://scikit-learn.org/
    - Under ubuntu: pip install scikit-learn==0.17

+ NetsurfP (version 1.0d)
  * https://services.healthtech.dtu.dk/software.php
    - To download and install NetsurfP (version 1.0d), visit the download page.  
    In order to predict with method BepiPred-2.0, you will need the NetsurfP 
    installed either globally, so the "netsurfp" global command is available 
    or an environment variable named NETSURFP_BIN with the path to the binary 
    of netsurfp.


Installation:
------------
Unpack the tar.gz files (IEDB_BCell-3.1.tar.gz)
Run the 'configure' script to set up path variables for trained models.

Specifically for bcell_standalone:
  $ tar -zxvf IEDB_BCell-3.1.tar.gz
  $ cd bcell_standalone
  $ ./configure


* All available method names:
-----------------------------
1) Chou-Fasman
2) Emini
3) Karplus-Schulz
4) Kolaskar-Tongaonkar
5) Parker
6) Bepipred
7) Bepipred-2.0


Available commands:
------------------
* for detail usage
$ python predict_antibody_epitope.py --help

* show program's version number
$ python predict_antibody_epitope.py --version

* show all available method options
$ python predict_antibody_epitope.py --list

* run using required options
$ python predict_antibody_epitope.py -m [method name] -f [sequence file]

* run using required options and generate plot
$ python predict_antibody_epitope.py -m [method name] -f [sequence file] --plot [path]

Examples:
--------
# run using 'Chou-Fasman' method and a single sequence
$ python predict_antibody_epitope.py -m Chou-Fasman -f test/single_sequence.txt

# run using 'Bepipred' method and a multiple fasta sequences
$ python predict_antibody_epitope.py -m Bepipred -f test/multiple_sequences.txt

# run using 'Bepipred' method and a swissprot id instead of a sequence file
$ python predict_antibody_epitope.py -m Bepipred -s P02185

# generate a plot into a specified directory
$ python predict_antibody_epitope.py -m Chou-Fasman -f test/single_sequence.txt --plot ~/Downloads