Immune epitope database analysis resource
By Yohan Kim, J. Ponomarenko, Zhanyang Zhu, Dorjee G. Tamang, Peng Wang, J. Greenbaum, Claus Lundegaard, Alessandro Sette, Ole Lund, Philip E. Bourne and 2 more. Nucleic Acids Res., 2012
514 citations recorded by the source corpus.
The immune epitope database analysis resource (IEDB-AR: http://tools.iedb.org) is a collection of tools for prediction and analysis of molecular targets of T- and B-cell immune responses (i.e. epitopes). Since its last publication in the NAR webserver issue in 2008, a new generation of peptide:MHC binding and T-cell epitope predictive tools have been added. As validated by different labs and in the first international competition for predicting peptide:MHC-I binding, their predictive performances have improved considerably. In addition, a new B-cell epitope prediction tool was added, and the homology mapping tool was updated to enable mapping of discontinuous epitopes onto 3D structures. Furthermore, to serve a wider range of users, the number of ways in which IEDB-AR can be accessed has been expanded. Specifically, the predictive tools can be programmatically accessed using a web interface and can also be downloaded as software packages.
Subfields: Immunology, Molecular Biology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/02e72b05d309cbc7652666c72a2dbb0bc68cd9ea
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In Immunology
- Bjoern Peters
- Sinu Paul
- Massimo Andreatta
- Morten Nielsen
- Alessandro Sette
- Swapnil Mahajan
- Sandeep Kumar Dhanda
- Paolo Marcatili
In Molecular Biology
- Bjoern Peters
- Morten Nielsen
- Massimo Andreatta
- Alessandro Sette
- Sinu Paul
- Paolo Marcatili
- Swapnil Mahajan
- Sandeep Kumar Dhanda
In Radiology, Nuclear Medicine and Imaging
- Bjoern Peters
- Alessandro Sette
- Sinu Paul
- Massimo Andreatta
- Morten Nielsen
- Paolo Marcatili
- Swapnil Mahajan
- Sandeep Kumar Dhanda
Citations in the corpus
- A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction (2020, methodology): “Here we classify these publically available tools into three major categories based on the methodologies they used, namely (i) methods based on sequence-scoring functions, including SYF-PEITHI [22], RANKPEP [23], PickPocket 1.1 [24], stabilized matrix method (SMM)—peptide:major histocompatibility co”
- Immunotherapy of non-Hodgkin lymphoma with a defined ratio of CD8+ and CD4+ CD19-specific chimeric antigen receptor-modified T cells (2016, methodology): “Predictions of major histocompatibility complex class I binding by peptides shared between the candidate pools were made using the Immune Epitope Database and Analysis Resource Consensus tool (32), which combines predictions from NetMHC (3.”
- The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design (2017, methodology): “The tools in the Analysis Resource1 (1, 2) fall into two general categories: prediction and analysis tools.”
- IEDB-AR: immune epitope database—analysis resource in 2019 (2019, methodology): “Here, we describe the newly implemented tools (Table 1), updates to the previously existing tools, and novel functionalities that have been added since the last report in the 2012 NAR webserver edition (4).”