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DeepC：使用兆碱基规模的迁移学习预测 3D 基因组折叠。

DeepC: predicting 3D genome folding using megabase-scale transfer learning.

机构信息

MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.

MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.

出版信息

Nat Methods. 2020 Nov;17(11):1118-1124. doi: 10.1038/s41592-020-0960-3. Epub 2020 Oct 12.

DOI:10.1038/s41592-020-0960-3
PMID:33046896
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7610627/
Abstract

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.

摘要

预测非编码遗传变异的影响需要将其置于三维基因组结构的背景下进行解释。我们开发了 deepC，这是一种基于迁移学习的深度神经网络，可以从兆碱基规模的 DNA 序列中准确预测基因组折叠。DeepC 可以高分辨率地预测结构域边界，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变异的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad47/7610627/b0f2036b04f7/EMS118366-f003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad47/7610627/2ccccb17aee4/EMS118366-f001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad47/7610627/2ccccb17aee4/EMS118366-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad47/7610627/b88151d6e005/EMS118366-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad47/7610627/b0f2036b04f7/EMS118366-f003.jpg

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