Fred2 can simply be installed via pip with:
pip install git+https://github.com/FRED-2/Fred2
Installation of External Tools
For a fully functioning Fred2 installation, several external tools have to be installed. These tools can not be shipped with Fred2 due to Licensing issues or due to complex installation procedures.
The following tools are not provided by Fred2 and have to be downloaded and installed:
Installation of ILP Solver
It is necessary to install a ILP solver such as CBC, CPLEX, Gurobi, or GLPK when using the epitope selection and assembly approaches of Fred2. We recommend CBC, an open-source solver supported by the COIN-OR community (https://projects.coin-or.org/Cbc).
Although modern ILP solver are quit fast, some models and especially the ones used for epitope assembly are still very time consuming even for small input sizes. Therefore, Fred2 offers also methods to approximate the solution. For doing so, Fred2 resorts to the Lin-Kernighan Helsgaun TSP heuristic, which has to be installed separately and can be found here http://www.akira.ruc.dk/~keld/research/LKH.
- Epitope Prediction - This tutorial gives a good overview of the basic functionality covering the core objects, epitope prediction, and manipulation of the prediction results.
- Polymorhpic Epitope Prediction - This tutorial exemplifies how to incorporate mutations into epitope prediction.
- Antigen Processing - The tutorial exemplifies the usage of proteasomal cleavage, TAP, and epitope prediction methods and how to combine these methods for T-cell epitope prediction.
- Vaccine Design - The tutorial covers all steps in rational vaccine design, from epitope discovery, epitope selection, as well as epitope delivery in the form of string-of-beads vaccines.
- HLA Typing - This short tutorial shows how to use FRED2 for HLA typing exemplified by the usage of OptiType.
For Advanced Users and Developers
- Databases - This tutorial familiarizes the advanced users with internal functionalities of FRED2’s data base adapters.
- Interfaces - This tutorial gives an overview of all abstract base classes relevant for implementing new prediction methods, or data base adapters.