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GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction.
Al Seesi, Sahar; Al-Okaily, Anas; Shcheglova, Tatiana V; Sherafat, Elham; Alqahtani, Fahad H; Hagymasi, Adam T; Kaur, Anupinder; Srivastava, Pramod K; Mandoiu, Ion I.
Afiliação
  • Al Seesi S; Department of Computer Science, Southern Connecticut State University, New Haven, Connecticut, USA.
  • Al-Okaily A; Department of Cell Therapy and Applied Genomics, King Hussein Cancer Center, Amman, Jordan.
  • Shcheglova TV; Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA.
  • Sherafat E; Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, USA.
  • Alqahtani FH; National Centre for Bioinformatics, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia.
  • Hagymasi AT; Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA.
  • Kaur A; Department of Neuroscience, University of Connecticut School of Medicine, Farmington, Connecticut, USA.
  • Srivastava PK; Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA.
  • Mandoiu II; Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, USA.
J Comput Biol ; 30(4): 538-551, 2023 04.
Article em En | MEDLINE | ID: mdl-36999902
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article