GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction
Resource type
Authors/contributors
- Al Seesi, S. (Author)
- Al-Okaily, A. (Author)
- Shcheglova, T.V. (Author)
- Sherafat, E. (Author)
- Alqahtani, F.H. (Author)
- Hagymasi, A.T. (Author)
- Kaur, A. (Author)
- Srivastava, P.K. (Author)
- Mǎndoiu, I.I. (Author)
Title
GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction
Abstract
High-Throughput DNA and RNA sequencing are revolutionizing precision oncology, enabling personalized therapies such as cancer vaccines designed to target tumor-specific neoepitopes generated by somatic mutations expressed in cancer cells. Identification of these neoepitopes from next-generation sequencing data of clinical samples remains challenging and requires the use of complex bioinformatics pipelines. In this paper, we present GeNeo, a bioinformatics toolbox for genomics-guided neoepitope prediction. GeNeo includes a comprehensive set of tools for somatic variant calling and filtering, variant validation, and neoepitope prediction and filtering. For ease of use, GeNeo tools can be accessed via web-based interfaces deployed on a Galaxy portal publicly accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo locally is also available to academic users upon request. © Copyright 2023, Mary Ann Liebert, Inc., publishers 2023.
Publication
Journal of Computational Biology
Date
2023
Volume
30
Issue
4
Pages
538-551
Citation Key
alseesiGeNeoBioinformaticsToolbox2023
ISSN
1066-5277
Archive
Scopus
Short Title
GeNeo
Language
English
Library Catalog
Scopus
Extra
0 citations (Crossref) [2023-10-31]
Citation
Al Seesi, S., Al-Okaily, A., Shcheglova, T. V., Sherafat, E., Alqahtani, F. H., Hagymasi, A. T., Kaur, A., Srivastava, P. K., & Mǎndoiu, I. I. (2023). GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction. Journal of Computational Biology, 30(4), 538–551. Scopus. https://doi.org/10.1089/cmb.2022.0491
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