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SigFormer:一个基于注意力的框架,用于稳健的单样突变特征分解

Yang Zhang, Muchun Niu, Chenghang Zong

bioRxiv : the preprint server for biology

|February 6, 2026

概括

新型变压器框架SigFormer增强了体质突变分析,以提高低负荷和高噪声基因组数据的准确性. 该工具通过准确地分解突变特征,有助于理解疾病病因和风险.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
癌症研究癌症研究

背景情况:

身体突变特征揭示了暴露和内源性过程的基因组历史.
精确的单样签名分解是具有挑战性的,因为低突变负担,高噪音和复杂的目录.

研究的目的:

开发一个强大的计算框架,SigFormer,用于准确的体质突变分析,特别是在具有挑战性的低负荷和高噪声场景中.
改善在单个样本水平上对突变特征的分解和检测.

主要方法:

开发SigFormer,一个使用交叉注意力机制的设置条件变压器框架.
将SigFormer与像MuSiCal.Cal.这样的概率驱动的重装方法进行比较.
适用于PCAWG基因组和低负荷正常组织数据集.

主要成果:

与现有方法相比,SigFormer表现出更好的曝光回收和检测准确性,特别是在高噪音和过度填充的环境中.
对PCAWG基因组的分析表明,SigFormer保留了组织结构,并捕获了同时发生的低丰度特征,没有瘤类型的特异性.
SigFormer准确地识别正常组织中稳定的组织依赖的突变模式,并量化不可归因的残留物.

结论:

SigFormer提供了一种强大而准确的体质突变特征分析方法,性能优于传统方法.
该框架在各种数据集中是有效的,包括低负荷的正常组织,揭示突变性异质性.
通过明确处理不完整的签名目录,SigFormer为了解基因组损伤,疾病风险和下游分析提供了有价值的工具.

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