GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction

Sahar Al Seesi(Southern Connecticut State University), Anas Al-okaily(King Hussein Cancer Center), Tatiana Shcheglova(University of Connecticut), Elham Sherafat(University of Connecticut), Fahad Alqahtani(King Abdulaziz City for Science and Technology), Adam T. Hagymasi(University of Connecticut), Anupinder Kaur(University of Connecticut), Pramod K. Srivastava(University of Connecticut), Ion Măndoiu(University of Connecticut)
Journal of Computational Biology
March 31, 2023
Cited by 3

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.


Related Papers

No related papers found

Powered by citation graph analysis