染色质
生物信息学
计算生物学
染色体构象捕获
基因组
计算机科学
基因
转录因子
生物
人工智能
遗传学
增强子
作者
Kevin B Dsouza,Alexandra Maslova,Ediem Al-Jibury,Matthias Merkenschlager,Vijay K Bhargava,Maxwell W. Libbrecht
标识
DOI:10.1038/s41467-022-31337-w
摘要
Abstract Despite the availability of chromatin conformation capture experiments, discerning the relationship between the 1D genome and 3D conformation remains a challenge, which limits our understanding of their affect on gene expression and disease. We propose Hi-C-LSTM, a method that produces low-dimensional latent representations that summarize intra-chromosomal Hi-C contacts via a recurrent long short-term memory neural network model. We find that these representations contain all the information needed to recreate the observed Hi-C matrix with high accuracy, outperforming existing methods. These representations enable the identification of a variety of conformation-defining genomic elements, including nuclear compartments and conformation-related transcription factors. They furthermore enable in-silico perturbation experiments that measure the influence of cis-regulatory elements on conformation.
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