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Published on: May 22, 2018
GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction
Sahar Al Seesi1, Anas Al-Okaily2, Tatiana V Shcheglova3
1Department of Computer Science, Southern Connecticut State University, New Haven, Connecticut, USA.
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.
Insights
GeNeo is a new bioinformatics toolbox that simplifies predicting cancer neoepitopes from sequencing data. This tool aids in developing personalized cancer vaccines by identifying tumor-specific targets.
Area of Science:
- Genomics
- Bioinformatics
- Oncology
Background:
- High-throughput DNA and RNA sequencing are advancing precision oncology.
- Personalized therapies, like cancer vaccines, target tumor-specific neoepitopes from somatic mutations.
- Identifying neoepitopes from next-generation sequencing data is complex and requires sophisticated bioinformatics pipelines.
Purpose of the Study:
- To present GeNeo, a bioinformatics toolbox designed for genomics-guided neoepitope prediction.
- To provide a comprehensive suite of tools for somatic variant calling, filtering, validation, and neoepitope prediction.
- To enhance the accessibility of neoepitope prediction tools through web-based interfaces and local deployment options.
Main Methods:
- GeNeo offers integrated tools for somatic variant calling and filtering.
- Includes variant validation capabilities.
- Provides neoepitope prediction and filtering functionalities.
Main Results:
- GeNeo streamlines the process of neoepitope identification from clinical sequencing data.
- The toolbox facilitates the use of complex bioinformatics pipelines for neoepitope prediction.
- Web-based interfaces on a Galaxy portal and a downloadable virtual machine image enhance usability.
Conclusions:
- GeNeo provides a valuable resource for researchers in precision oncology.
- The toolbox simplifies the identification of neoepitopes, crucial for developing personalized cancer vaccines.
- GeNeo aims to accelerate the application of genomics in cancer therapy development.
