araCNA: somatic copy number profiling using long-range sequence models

Ellen Visscher

doi:10.1093/nargab/lqaf124

2025-09-01

Ellen Visscher

Christopher Yau *

DOI：10.1093/nargab/lqaf124

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摘要

En 中文

体细胞拷贝数变异（CNAs）是癌症的标志性特征。目前从全基因组测序（WGS）数据中识别CNAs的算法尚未利用深度学习方法，这主要受到计算规模限制的影响。在此，我们提出一种新颖的深度学习算法araCNA，该算法仅使用模拟数据进行训练，能够准确预测真实WGS癌症基因组中的CNAs。araCNA采用新颖的Transformer替代模型（如Mamba）来处理基因组规模的序列长度（约1M）并学习长距离相互作用。结果表明，该算法在模拟数据上表现极其准确，且在应用于癌症基因组图谱的50个WGS样本时，这一零样本方法的效果与现有方法相当。值得注意的是，我们的方法仅需肿瘤样本而无需匹配的正常样本，具有更少的过拟合指标，并且推理过程仅需几分钟。araCNA展示了领域知识如何用于模拟训练集，从而在生物应用中发挥现代机器学习的强大能力。

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CANCER

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期刊

NAR Genomics and Bioinformatics

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2.8

论文数:

261

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机构

University of Oxford

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