deepNGS导航器:使用深度对比学习探索抗体NGS数据集
Homa MohammadiPeyhani1, Edith Lee1, Richard Bonneau1
1Prescient Design, Genentech.
Bioinformatics (Oxford, England)
|August 12, 2025
概括
deepNGS Navigator使用人工智能可视化和集群B细胞受体序列,帮助分析来自大型数据集的抗体多样性和克隆家族.
科学领域:
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 高通量测序产生了庞大的B细胞受体 (BCR) 数据集,需要高效的计算工具进行分析.
- 识别克隆家族和相关抗体序列对于理解免疫反应和抗体工程至关重要.
研究的目的:
- 介绍 deepNGS Navigator,这是一款用于分析和可视化大规模 BCR 序列数据的新型计算工具.
- 为了使克隆家族和相关抗体序列的有效检测和聚类.
主要方法:
- 利用语言模型和对比学习来将抗体序列转化为2D表示.
- 使用序列嵌入用于突变模式推断和轨迹分析.
- 开发可视化工具,用于评估数据集多样性和识别序列集群.
主要成果:
- deepNGS导航器生成直观的2D地图,可视化BCR序列多样性,并根据序列相似性实现聚类.
- 该工具有助于识别克隆家族和分析预测序列嵌入中的突变模式.
- 在各种数据集中展示了实用性,包括合成库,ML生成的数据,免疫拉玛序列,人类B细胞序列和in silico克隆血统.
结论:
- deepNGS导航提供了一种有效的方法来探索和分析大型BCR序列数据集.
- 该工具有助于通过可视化序列属性和关系来识别潜在的有益或有问题的抗体序列.
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