DiscovEpi: automated whole proteome MHC-I-epitope prediction and visualization
Automated prediction of whole proteomes and the subsequent visualization of the location of putative epitopes on sequence-level facilitate the search for putative immunogenic proteins or protein regions and support the study of adaptive immune responses and vaccine design.
Authors
- Clemens Cammann (ORCID: https://orcid.org/0009-0008-1699-0607)
- Stefan Simm (ORCID: https://orcid.org/0000-0001-9371-2709)
- Frieder Schmiedeke (ORCID: https://orcid.org/0000-0001-6958-7936)
- Lars Kaderali (ORCID: https://orcid.org/0000-0002-2359-2294)
- Ulrike Seifert (ORCID: https://orcid.org/0000-0003-1037-3487)
- Barbara M. Bröker (ORCID: https://orcid.org/0000-0002-5020-8542)
- Cedric Mahncke
Institutions
- Friedrich-Loeffler-Institut (DE)
- Leibniz Institute of Virology (LIV) (DE)
- Coburg University of Applied Sciences (DE)
- Universitätsmedizin Greifswald (DE)
- Universität Greifswald (DE)
- Institute of Virology of the Slovak Academy of Sciences (SK)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-09-27
- DOI
- https://doi.org/10.1186/s12859-024-05931-2
- Citations
- 2
- Primary Topic
- vaccines and immunoinformatics approaches
- Type
- article
- Field-Weighted Citation Impact
- 0.47
Funders
- Deutsche Forschungsgemeinschaft