AntiRef:人类抗体序列的参考集群
Bryan Briney1,2,3,4,5
1Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, United States.
Bioinformatics advances
|October 27, 2023
概括
遗传偏差会产生冗余的抗体序列数据,阻碍研究. 抗体参考集群 (AntiRef) 为高效的分析和机器学习模型培训提供了优化,集群的数据集.
科学领域:
- 免疫信息学是指免疫信息学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 人类抗体序列数据集遭受遗传偏差,导致大量重复和高度相似的序列.
- 现有的数据集偏向于癌症和某些感染 (HIV,流感,SARS-CoV-2) 等特定疾病,限制了更广泛的适用性.
- 数据冗余和偏差阻碍了高效的相似性搜索和统计或机器学习模型的训练.
研究的目的:
- 为了应对聚类大型抗体序列数据集的计算挑战.
- 开发针对抗体序列的优化聚类值,改进一般蛋白质聚类方法.
- 为更广泛的研究用途创建公开可用的,聚类抗体序列数据集.
主要方法:
- 开发了抗体参考集群 (AntiRef),该系统以UniRef为模型,专门用于抗体序列.
- 应用了针对抗体优化的身份值,以集成大约4.51亿个全长,有效的人类抗体序列的大数据集.
- 生成的参考数据集聚合在各种身份值,包括AntiRef90和AntiRef100.
主要成果:
- AntiRef提供了使用优化值过的人类抗体序列的集群数据集.
- AntiRef90比输入数据集 (大小的三分之一) 和非冗余的AntiRef100.大大小小小.
- 集群方法有效地减少了冗余,并改善了下游分析数据的可用性.
结论:
- AntiRef提供了一种计算效率高的解决方案,用于管理和分析大规模的人类抗体序列数据.
- 优化的聚类为研究人员提供了有价值的,非冗余的数据集,促进了抗体的发现和工程.
- 可访问的数据集和代码促进了抗体相关研究的更广泛采用和进步.
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