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DeepC: predicting 3D genome folding using megabase-scale transfer learning
Nature Methods ( IF 48.0 ) Pub Date : 2020-10-12 , DOI: 10.1038/s41592-020-0960-3
Ron Schwessinger 1, 2, 3 , Matthew Gosden 1 , Damien Downes 1 , Richard C Brown 3 , A Marieke Oudelaar 1, 2 , Jelena Telenius 2 , Yee Whye Teh 4 , Gerton Lunter 2, 3 , Jim R Hughes 1, 2
Affiliation  

Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from megabase-scale DNA sequence. DeepC predicts domain boundaries at high resolution, learns the sequence determinants of genome folding and predicts the impact of both large-scale structural and single base-pair variations.



中文翻译:

DeepC:使用兆碱基规模的迁移学习预测 3D 基因组折叠

预测非编码遗传变异的影响需要在三维基因组结构的背景下对其进行解释。我们开发了 deepC,这是一种基于迁移学习的深度神经网络,可准确预测兆碱基级 DNA 序列的基因组折叠。DeepC 以高分辨率预测域边界,学习基因组折叠的序列决定因素,并预测大规模结构和单碱基对变异的影响。

更新日期:2020-10-12
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