BMC Bioinformatics | |
NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline | |
Eszter Lakatos1  Trevor A. Graham1  Ryan O. Schenck2  Chandler Gatenbee2  Alexander R.A. Anderson2  | |
[1] Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London;Integrated Mathematical Oncology, Moffitt Cancer Center; | |
关键词: Neoantigens; Cancer; Evolution; Heterogeneity; Next-generation sequencing; | |
DOI : 10.1186/s12859-019-2876-4 | |
来源: DOAJ |
【 摘 要 】
Abstract Background Next generation sequencing has yielded an unparalleled means of quickly determining the molecular make-up of patient tumors. In conjunction with emerging, effective immunotherapeutics for a number of cancers, this rapid data generation necessitates a paired high-throughput means of predicting and assessing neoantigens from tumor variants that may stimulate immune response. Results Here we offer NeoPredPipe (Neoantigen Prediction Pipeline) as a contiguous means of predicting putative neoantigens and their corresponding recognition potentials for both single and multi-region tumor samples. NeoPredPipe is able to quickly provide summary information for researchers, and clinicians alike, on predicted neoantigen burdens while providing high-level insights into tumor heterogeneity given somatic mutation calls and, optionally, patient HLA haplotypes. Given an example dataset we show how NeoPredPipe is able to rapidly provide insights into neoantigen heterogeneity, burden, and immune stimulation potential. Conclusions Through the integration of widely adopted tools for neoantigen discovery NeoPredPipe offers a contiguous means of processing single and multi-region sequence data. NeoPredPipe is user-friendly and adaptable for high-throughput performance. NeoPredPipe is freely available at https://github.com/MathOnco/NeoPredPipe.
【 授权许可】
Unknown