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FRED 2: an immunoinformatics framework for Python.

Schubert B, Walzer M, Brachvogel HP, Szolek A, Mohr C, Kohlbacher O - Bioinformatics (2016)

Bottom Line: 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.FRED 2 is implemented in Python and designed to be extendable and flexible to allow rapid prototyping of complex applications.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics, University of Tübingen, Tübingen 72076, Germany Department of Computer Science, Applied Bioinformatics, Tübingen 72076, Germany.

No MeSH data available.


Schematic overview of FRED 2. FRED 2 is organized into modules dealing with epitope, cleavage and TAP prediction, HLA typing, epitope selection and assembly. The framework also offers accession to biological databases
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btw113-F1: Schematic overview of FRED 2. FRED 2 is organized into modules dealing with epitope, cleavage and TAP prediction, HLA typing, epitope selection and assembly. The framework also offers accession to biological databases

Mentions: FRED 2 covers four major areas of immunoinformatics: T-cell epitope prediction, epitope selection, epitope assembly and HLA typing (Fig. 1). Prediction methods are split into three packages EpitopePrediction, TAPPrediction and CleavagePrediction, each providing factory classes as entry points for the supported prediction methods. A detailed overview of the supported prediction methods can be found in Supplementary Table S1. OptiTope (Toussaint and Kohlbacher, 2009), a highly flexible mathematical framework capable of expression various aspects of epitope-based vaccines, was implemented for epitope selection. To enable epitope assembly, FRED 2 implements the traveling-salesperson (TSP) approach proposed by Toussaint et al. (Toussaint et al., 2011) and OptiVac (Schubert and Kohlbacher, 2016) for string-of-beads design with optimal spacer sequences, which is similar to the approach taken in (Antonets and Bazhan, 2013). For HLA typing, FRED 2 provides wrapper methods for many HLA typing approaches, such as OptiType (Szolek et al., 2014), Polysolver (Shukla et al., 2015), seq2HLA (Boegel et al., 2013) and ATHLATES (Liu et al., 2013). FRED 2 also offers methods to interact with many biological databases like BioMart, UniProt, RefSeq and Ensembl. It provides support for handling sequence variations at all major biological levels, from transcript, protein, to peptide level. FRED 2 is open-source software and released under a three-clause BSD license. It was designed to be open and easily extendable by providing self explanatory interfaces so that implementation of new functionalities by a wider community can be easily accomplished.Fig. 1


FRED 2: an immunoinformatics framework for Python.

Schubert B, Walzer M, Brachvogel HP, Szolek A, Mohr C, Kohlbacher O - Bioinformatics (2016)

Schematic overview of FRED 2. FRED 2 is organized into modules dealing with epitope, cleavage and TAP prediction, HLA typing, epitope selection and assembly. The framework also offers accession to biological databases
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920123&req=5

btw113-F1: Schematic overview of FRED 2. FRED 2 is organized into modules dealing with epitope, cleavage and TAP prediction, HLA typing, epitope selection and assembly. The framework also offers accession to biological databases
Mentions: FRED 2 covers four major areas of immunoinformatics: T-cell epitope prediction, epitope selection, epitope assembly and HLA typing (Fig. 1). Prediction methods are split into three packages EpitopePrediction, TAPPrediction and CleavagePrediction, each providing factory classes as entry points for the supported prediction methods. A detailed overview of the supported prediction methods can be found in Supplementary Table S1. OptiTope (Toussaint and Kohlbacher, 2009), a highly flexible mathematical framework capable of expression various aspects of epitope-based vaccines, was implemented for epitope selection. To enable epitope assembly, FRED 2 implements the traveling-salesperson (TSP) approach proposed by Toussaint et al. (Toussaint et al., 2011) and OptiVac (Schubert and Kohlbacher, 2016) for string-of-beads design with optimal spacer sequences, which is similar to the approach taken in (Antonets and Bazhan, 2013). For HLA typing, FRED 2 provides wrapper methods for many HLA typing approaches, such as OptiType (Szolek et al., 2014), Polysolver (Shukla et al., 2015), seq2HLA (Boegel et al., 2013) and ATHLATES (Liu et al., 2013). FRED 2 also offers methods to interact with many biological databases like BioMart, UniProt, RefSeq and Ensembl. It provides support for handling sequence variations at all major biological levels, from transcript, protein, to peptide level. FRED 2 is open-source software and released under a three-clause BSD license. It was designed to be open and easily extendable by providing self explanatory interfaces so that implementation of new functionalities by a wider community can be easily accomplished.Fig. 1

Bottom Line: 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.FRED 2 is implemented in Python and designed to be extendable and flexible to allow rapid prototyping of complex applications.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics, University of Tübingen, Tübingen 72076, Germany Department of Computer Science, Applied Bioinformatics, Tübingen 72076, Germany.

No MeSH data available.