.. _tools: Tools Used By pVACtools ----------------------- IEDB (Immune Epitope Database) ______________________________ - Website: https://www.iedb.org - Citation: Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, Wheeler DK, Sette A, Peters B. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 2018 Oct 24. doi: `10.1093/nar/gky1006 <10.1093/nar/gky1006>`_. [Epub ahead of print] PubMed PMID: `30357391 `_. - License: Non-Profit OSL 3.0 By using the IEDB software, you are consenting to be bound by and become a "Licensee" for the use of IEDB tools and are consenting to the terms and conditions of the Non-Profit Open Software License ("Non-Profit OSL") version 3.0. Please read these two license agreements `here `_ before proceeding. If you do not agree to all of the terms of these two agreements, you must not install or use the product. Companies (for-profit entities) interested in downloading the command-line versions of the IEDB tools or running the entire analysis resource locally, should contact IEDB (license@iedb.org) for details on licensing options. MHCflurry _________ - Website: http://openvax.github.io/mhcflurry/ - GitHub: https://github.com/openvax/mhcflurry - Citation: T. J. O’Donnell, A. Rubinsteyn, M. Bonsack, A. B. Riemer, U. Laserson, and J. Hammerbacher, "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," Cell Systems, 2018. doi: https://doi.org/10.1016/j.cels.2018.05.014 PubMed PMID: `29960884 `_. - License: `Apache License 2.0 `_ MHCnuggets __________ - Website: https://karchinlab.org/software/ - GitHub: https://github.com/KarchinLab/mhcnuggets - Citation: Shao XM, Bhattacharya R, Huang J, Sivakumar IKA, Tokheim C, Zheng L, Hirsch D, Kaminow B, Omdahl A, Bonsack M, Riemer AB, Velculescu VE, Anagnostou V, Pagel KA, Karchin R. High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets. Cancer Immunol Res. 2020 Mar;8(3):396-408. doi: https://doi.org/10.1158/2326-6066.CIR-19-0464 PubMed PMID: `31871119 `_ - License: `JHU Academic Software License Agreement `_ BigMHC ______ - Website: https://karchinlab.org/software/ - GitHub: https://github.com/KarchinLab/BigMHC - Citation: Albert BA, Yang Y, Shao XM, Singh D, Smit KN, Anagnostou V, Karchin R. Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity. Nat Mach Intell. 2023 Aug;5(8):861-872. doi: https://doi.org/10.1038/s42256-023-00694-6 PMID: `37829001 `_ - License: `BigMHC Academic License `_ DeepImmuno __________ - Website: https://deepimmuno.research.cchmc.org/ - GitHub: https://github.com/frankligy/DeepImmuno - Citation: Li G, Iyer B, Prasath VBS, Ni Y, Salomonis N. DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity. Brief Bioinform. 2021 Nov 5;22(6):bbab160. doi: https://doi.org/10.1093/bib/bbab160 PubMed PMID: `34009266 `_ - License: `MIT License `_ MixMHCpred __________ - Website: https://gfellerlab.org/index.php/computational-tools/ - GitHub: https://github.com/GfellerLab/MixMHCpred - Citation: Gfeller D, Schmidt J, Croce G, Guillaume P, Bobisse S, Genolet R, Queiroz L, Cesbron J, Racle J, Harari A. Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes. (2023), Cell Systems, 14, 72. doi: https://doi.org/10.1016/j.cels.2022.12.002 PubMed PMID: `36603583 `_ - License: `Academic License `_ MixMHC2pred ___________ - Website: https://gfellerlab.org/index.php/computational-tools/ - GitHub: https://github.com/GfellerLab/MixMHC2pred - Citation: Racle J, Guillaume P, Schmidt J, Michaux J, Larabi A, Lau K, Perez MAS, Croce G, Genolet R, Coukos G, Zoete V, Pojer F, Bassani-Sternberg M, Harari A, Gfeller D. Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes. (2023) Immunity, 2023, 56, 1-17. doi: https://doi.org/10.1016/j.immuni.2023.03.009 PubMed PMID: `37023751 `_ - License: `Academic License `_ PRIME _____ - Website: https://gfellerlab.org/index.php/computational-tools/ - GitHub: https://github.com/GfellerLab/PRIME - Citation: Gfeller D, Schmidt J, Croce G, Guillaume P, Bobisse S, Genolet R, Queiroz L, Cesbron J, Racle J, Harari A. Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes. (2023), Cell Systems, 14, 72. doi: https://doi.org/10.1016/j.cels.2022.12.002 PubMed PMID: `36603583 `_ - License: `Academic License `_ TL-MHC ______ - Website: https://www.kavrakilab.org/publications/fasoulis2024-transfer.html - GitHub: https://github.com/KavrakiLab/TL-MHC - Citation: Fasoulis R, Rigo MM, Antunes DA, Paliouras G, Kavraki LE. Transfer learning improves pMHC kinetic stability and immunogenicity predictions. Immunoinformatics (Amst). 2024 Mar;13:100030. doi: http://dx.doi.org/10.1016/j.immuno.2023.100030 PubMed PMID: `38577265 `_ - License: None provided ImmuScope _________ - GitHub: https://github.com/shenlongchen/ImmuScope - Citation: Shen, LC., Zhang, Y., Wang, Z. et al. Self-iterative multiple-instance learning enables the prediction of CD4+ T cell immunogenic epitopes. Nat Mach Intell 7, 1250–1265 (2025). doi: https://doi.org/10.1038/s42256-025-01073-z - License: `GPL-3.0 license `_ NetChop _______ - Website: http://www.cbs.dtu.dk/services/NetChop/ - Citation: The role of the proteasome in generating cytotoxic T cell epitopes: Insights obtained from improved predictions of proteasomal cleavage. M. Nielsen, C. Lundegaard, O. Lund, and C. Kesmir. Immunogenetics., 57(1-2):33-41, 2005. doi: https://doi.org/10.1007/s00251-005-0781-7 PubMed PMID: `15744535 `_. - License: `Academic License `_ NetMHCstabpan _____________ - Website: http://www.cbs.dtu.dk/services/NetMHCstabpan/ - Citation: Pan-specific prediction of peptide-MHC-I complex stability; a correlate of T cell immunogenicity. Michael Rasmussen, Emilio Fenoy, Mikkel Harndahl, Anne Bregnballe Kristensen, Ida Kallehauge Nielsen, Morten Nielsen, Soren Buus. J Immunol. 2016 Aug 15;197(4):1517-24. doi: https://doi.org/10.4049/jimmunol.1600582 PubMed PMID: `27402703 `_. - License: `Academic License `_ Vaxrank _______ - Website: https://github.com/openvax/vaxrank - Citation: Rubinsteyn, A., Hodes, I., Kodysh, J., & Hammerbacher, J. (2017). Vaxrank: a computational tool for designing personalized cancer vaccines. bioRxiv, `142919 `_. - License: `Apache License 2.0 `_ BLAST _____ - Website: https://blast.ncbi.nlm.nih.gov/Blast.cgi - Citations: `Link `_ - License: This software/database is a "United States Government Work" under the terms of the United States Copyright Act. It was written as part of the author's official duties as a United States Government employee and thus cannot be copyrighted. This software/database is freely available to the public for use. The National Library of Medicine and the U.S. Government have not placed any restriction on its use or reproduction.