Abstract
Summary: Immunoinformatics approaches are widely used in a variety of applications from basic immunological to applied biomedical research. Complex data integration is inevitable in immunological research and usually requires comprehensive pipelines including multiple tools and data sources. Non-standard input and output formats of immunoinformatics tools make the development of such applications difficult. Here we present FRED 2, an open-source immunoinformatics framework offering easy and unified access to methods for epitope prediction and other immunoinformatics applications. FRED 2 is implemented in Python and designed to be extendable and flexible to allow rapid prototyping of complex applications.Availability and implementation: FRED 2 is available at http://fred-2.github.ioContact: schubert@informatik.uni-tuebingen.deSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
The field of immunoinformatics has matured over the last decades
FRED 2 implements the traveling-salesperson (TSP) approach proposed by Toussaint et al (Toussaint et al, 2011) and OptiVac (Schubert and Kohlbacher, 2016) for stringof-beads design with optimal spacer sequences, which is similar to the approach taken in (Antonets and Bazhan, 2013)
1. #read in virus proteins of interest 2. prots 1⁄4 IO.read_fasta(“./proteins.fasta”,in_type 1⁄4 Protein) 3. #read in HLA alleles of target population 4. hlas 1⁄4 IO.read_line(“./europe_hlas.txt”,in_type 1⁄4 Allele) 5. #generate 9mer peptides from proteins 6. peps 1⁄4 Generator.generate_peptides_from_proteins(prots,9) 7. 8. #predict binding affinity 9. netMHC 1⁄4 EpitopePredictionFactory(“NetMHC”) 10. aff 1⁄4 netMHC.predict(peps, alleles 1⁄4 hlas) 11. #initialize OptiTope and select up to 10 epitopes. 12. #assume a binding threshold of 500nM 1⁄4 0.425 NetMHC
Summary
Epitope prediction methods are widely used and have been successfully applied in many areas from basic immunological to translational research (Boisguerin et al, 2014; Shukla et al, 2015). These applications often require complex pipelines combining multiple tools, multiple data sources and extensive preand post- processing. Many of the HLA epitope prediction tools do not offer a unified interface and output format, which makes it difficult to use prediction methods interchangeably. By building on top of popular modules such as BioPython (http://biopython.org) and Pandas (http://pandas.pydata.org), FRED 2 allows rapid prototyping of complex and innovative immunoinformatics applications
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