DeepHINT: understanding HIV-1 integration via deep learning with attention
DeepHINT: understanding HIV-1 integration via deep learning with attention
复制标题
DeepHINT：通过深度学习和注意力理解 HIV-1 整合
DOI：
10.1093/bioinformatics/bty842
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发表时间：
2019-05-15
期刊：
影响因子：
5.8
通讯作者：
Zeng, Jianyang
中科院分区：
文献类型：
--
作者：
Hu, Hailin;Xiao, An;Zeng, Jianyang
人类免疫缺陷病毒1型（HIV-1）基因组整合与临床潜伏期和病毒反弹密切相关。除了直接与整合机制相互作用的人类DNA序列之外，HIV整合位点的选择也被证明取决于大区域周围的异质基因组背景，这极大地阻碍了HIV整合的预测和机制研究。同时提供HIV整合位点的准确预测和检测位点的机制解释。对高密度HIV整合位点数据集的广泛测试表明，DeepHINT可以通过单独或与表观遗传信息一起从主要DNA序列自动学习HIV整合的基因组背景，从而超越传统的建模策略。对HIV整合的多种已知因素的系统分析进一步验证了预测结果的生物学相关性。更重要的是，对DeepHINT输出的注意力值的深入分析揭示了HIV整合位点选择的有趣机制，包括几种DNA结合蛋白的潜在作用。这些结果确立了DeepHINT作为一个有效和可解释的深度学习框架，用于HIV整合的预测和机制研究。可用性和实施DeepHINT是一个开源软件，可以从https://github.com/nonnerdling/DeepHINT.Supplementary信息下载补充数据可以在生物信息学在线获得。
Motivation Human immunodeficiency virus type 1 (HIV-1) genome integration is closely related to clinical latency and viral rebound. In addition to human DNA sequences that directly interact with the integration machinery, the selection of HIV integration sites has also been shown to depend on the heterogeneous genomic context around a large region, which greatly hinders the prediction and mechanistic studies of HIV integration.Results We have developed an attention-based deep learning framework, named DeepHINT, to simultaneously provide accurate prediction of HIV integration sites and mechanistic explanations of the detected sites. Extensive tests on a high-density HIV integration site dataset showed that DeepHINT can outperform conventional modeling strategies by automatically learning the genomic context of HIV integration from primary DNA sequence alone or together with epigenetic information. Systematic analyses on diverse known factors of HIV integration further validated the biological relevance of the prediction results. More importantly, in-depth analyses of the attention values output by DeepHINT revealed intriguing mechanistic implications in the selection of HIV integration sites, including potential roles of several DNA-binding proteins. These results established DeepHINT as an effective and explainable deep learning framework for the prediction and mechanistic study of HIV integration.Availability and implementation DeepHINT is available as an open-source software and can be downloaded from https://github.com/nonnerdling/DeepHINT.Supplementary informationSupplementary data are available at Bioinformatics online.