PDF(Pubmed)
Abstract：
UNASSIGNED: Microsatellite instability (MSI) secondary to mismatch repair (MMR) deficiency is characterized by insertions and deletions (indels) in short DNA sequences across the genome. These indels can generate neoantigens, which are ideal targets for precision immune interception. However, current neoantigen databases lack information on neoantigens arising from coding microsatellites. To address this gap, we introduce The MicrOsatellite Neoantigen Discovery Tool (MONET).
UNASSIGNED: MONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions.
UNASSIGNED: MONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. <25%) using a set of experimentally validated neoAgs.
UNASSIGNED: MONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.
UNASSIGNED: MONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions.
UNASSIGNED: MONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. <25%) using a set of experimentally validated neoAgs.
UNASSIGNED: MONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.
摘要：
■错配修复（MMR）缺陷继发的微卫星不稳定性（MSI）的特征在于整个基因组的短DNA序列中的插入和缺失（indel）。这些indel可以产生新抗原，是精确免疫拦截的理想目标。然而,当前的新抗原数据库缺乏编码微卫星产生的新抗原的信息。为了解决这个差距，我们介绍了微卫星新抗原发现工具（MONET）。
■MONET通过预测人类基因组编码微卫星序列中的移码突变来鉴定潜在的突变的肿瘤特异性新抗原（neoAgs）。然后，MONET用结合亲和力等关键特征注释这些neoAgs，稳定性,表达式,频率,和使用既定算法的潜在致病性，工具,和公共数据库。用户友好的Web界面（https://monet。mdanderson.org/)便于访问这些预测。
■MONET预测了超过400万和1500万I类和II类潜在移码新G，分别。与现有数据库相比，MONET显示出较高的覆盖率（>85%与<25%)使用一组实验验证的neoAg。
■MONET是免费提供的，用户友好的网络工具，利用公开可用的资源来识别源自微卫星基因座的neoAgs。这种系统生物学方法赋予了精确免疫拦截领域的研究人员权力。
■MONET通过预测人类基因组编码微卫星序列中的移码突变来鉴定潜在的突变的肿瘤特异性新抗原（neoAgs）。然后，MONET用结合亲和力等关键特征注释这些neoAgs，稳定性,表达式,频率,和使用既定算法的潜在致病性，工具,和公共数据库。用户友好的Web界面（https://monet。mdanderson.org/)便于访问这些预测。
■MONET预测了超过400万和1500万I类和II类潜在移码新G，分别。与现有数据库相比，MONET显示出较高的覆盖率（>85%与<25%)使用一组实验验证的neoAg。
■MONET是免费提供的，用户友好的网络工具，利用公开可用的资源来识别源自微卫星基因座的neoAgs。这种系统生物学方法赋予了精确免疫拦截领域的研究人员权力。
