Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning
Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning
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DOI：
10.1038/s41551-022-00961-8
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发表时间：
2022-11-21
影响因子：
28.1
通讯作者：
Terao, Chikashi
中科院分区：
文献类型：
--
作者：
Koido, Masaru;Hon, Chung-Chau;Terao, Chikashi
机器学习模型可以在细胞类型水平上可靠地将基因组序列和非编码RNA表达联系起来。基因转录通过涉及非编码RNA（ncRNA）的复杂机制进行调节。由于ncRNA的转录，特别是增强子RNA的转录，通常是低的和细胞类型特异性的，RNA转录水平如何取决于基因型仍然在很大程度上未被探索。在这里，我们报告了一个机器学习模型（MENTR）的开发和实用性，该模型在细胞类型水平上可靠地将基因组序列和ncRNA表达联系起来。该模型预测的对ncRNA转录的影响与以细胞类型依赖性方式发表的研究的估计一致，无论等位基因频率和遗传连锁如何。在来自全基因组关联研究的41，223个变体中，该模型确定了7，775个增强子RNA和3，548个长ncRNA与348个主要人类原代细胞和组织中的复杂性状有因果关系，例如罕见变体可能改变增强子RNA的转录以影响克罗恩病和哮喘的风险。该模型可能有助于发现因果变异和产生可检验的假设的生物机制驱动复杂的性状。
A machine-learning model can reliably link genome sequence and non-coding RNA expression at the cell type level.Gene transcription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). As the transcription of ncRNAs, especially of enhancer RNAs, is often low and cell type specific, how the levels of RNA transcription depend on genotype remains largely unexplored. Here we report the development and utility of a machine-learning model (MENTR) that reliably links genome sequence and ncRNA expression at the cell type level. Effects on ncRNA transcription predicted by the model were concordant with estimates from published studies in a cell-type-dependent manner, regardless of allele frequency and genetic linkage. Among 41,223 variants from genome-wide association studies, the model identified 7,775 enhancer RNAs and 3,548 long ncRNAs causally associated with complex traits across 348 major human primary cells and tissues, such as rare variants plausibly altering the transcription of enhancer RNAs to influence the risks of Crohn's disease and asthma. The model may aid the discovery of causal variants and the generation of testable hypotheses for biological mechanisms driving complex traits.