Division of Health Medical Data Science, Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
Laboratory of DNA Information Analysis, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
Bioinformatics. 2020 Sep 15;36(18):4813-4816. doi: 10.1093/bioinformatics/btaa616.
It is known that some mutant peptides, such as those resulting from missense mutations and frameshift insertions, can bind to the major histocompatibility complex and be presented to antitumor T cells on the surface of a tumor cell. These peptides are termed neoantigen, and it is important to understand this process for cancer immunotherapy. Here, we introduce an R package termed Neoantimon that can predict a list of potential neoantigens from a variety of mutations, which include not only somatic point mutations but insertions, deletions and structural variants. Beyond the existing applications, Neoantimon is capable of attaching and reflecting several additional information, e.g. wild-type binding capability, allele specific RNA expression levels, single nucleotide polymorphism information and combinations of mutations to filter out infeasible peptides as neoantigen.
The R package is available at http://github/hase62/Neoantimon.
已知某些突变肽，如错义突变和移码插入产生的肽，可与主要组织相容性复合物结合，并在肿瘤细胞表面呈递至抗肿瘤 T 细胞。这些肽被称为新抗原，了解这一过程对于癌症免疫治疗很重要。在这里，我们介绍了一个名为 Neoantimon 的 R 包，它可以从各种突变中预测潜在的新抗原列表，这些突变不仅包括体细胞点突变，还包括插入、缺失和结构变异。除了现有的应用程序，Neoantimon 还能够附加和反映其他一些信息，例如野生型结合能力、等位基因特异性 RNA 表达水平、单核苷酸多态性信息以及突变组合，以过滤掉不可行的肽作为新抗原。
R 包可在 http://github/hase62/Neoantimon 获得。
