Department of Computer Science, Southern Connecticut State University, New Haven, Connecticut, USA.
Department of Cell Therapy and Applied Genomics, King Hussein Cancer Center, Amman, Jordan.
J Comput Biol. 2023 Apr;30(4):538-551. doi: 10.1089/cmb.2022.0491. Epub 2023 Mar 30.
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.
高通量 DNA 和 RNA 测序正在彻底改变精准肿瘤学，使癌症疫苗等个性化疗法成为可能，这些疗法旨在针对由癌细胞中表达的体细胞突变产生的肿瘤特异性新抗原。从临床样本的下一代测序数据中识别这些新抗原仍然具有挑战性，需要使用复杂的生物信息学管道。在本文中，我们介绍了 GeNeo，这是一个用于基因组指导的新抗原预测的生物信息学工具包。GeNeo 包括一组用于体细胞变异调用和过滤、变异验证以及新抗原预测和过滤的全面工具。为了便于使用，GeNeo 工具可以通过部署在公共访问的 Galaxy 门户上的基于网络的界面访问，网址为 https://neo.engr.uconn.edu/。学术用户也可以根据请求获得用于本地运行 GeNeo 的虚拟机映像。
