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Cis-regulatory Sequences

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

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Updated: May 9, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

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Published on: May 22, 2017

机器学习预测非编码变体在细胞类特定的人类视网膜Cis-调控元素中的影响

Leah S VandenBosch¹, Timothy J Cherry^1,2,3

¹Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA, USA.

bioRxiv : the preprint server for biology

|March 10, 2025

概括

机器学习模型预测遗传变异如何影响遗传性视网膜疾病 (IRD). 使用单核表观遗传学数据,这些模型准确地识别引起疾病的调节变异,以更快地诊断患者.

关键词:

视网膜疾病视网膜疾病这是一个 cis-regulatory 元素.机器学习是机器学习.没有编码的变体.

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科学领域:

基因组学就是基因组学.
计算生物学计算生物学
眼科医生眼科眼科

背景情况:

在cis-regulatory元素 (CREs) 中的非编码变异与遗传性视网膜疾病 (IRD) 有关.
这些调控变异的功能性表征是遗传研究中的一个重大挑战.
识别与疾病相关的变体需要强大的预测工具.

研究的目的:

开发机器学习 (ML) 模型来预测非编码变体对视网膜CREs的功能影响.
加强与IRD相关的变体的识别和优先级.
为了利用单核ATAC-seq数据进行细胞类特定变异影响预测.

主要方法:

实施了一个有间隙的k-mer支向量机 (SVM) 方法.
从人类视网膜细胞类中使用单核ATAC-seq数据训练了18种不同的ML模型.
预测变异对39437个细胞类特定的监管元素的影响.

主要成果:

ML模型实现了超过90%的准确性,具有高细胞类特异性.
变异影响预测 (VIP) 评分在CREs中确定了特定的序列,包括对突变敏感的转录因子 (TF) 结合基因.
VIP评分在单核酸变异和indels的大规模并行报告员测定中显示出预测价值.

结论:

单核表观遗传学数据可以有效预测非编码序列变异的功能影响.
开发的ML模型和VIP评分可以快速对患者变异进行功能分析.
这种方法促进了对IRDs遗传贡献的理解.