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相关概念视频

The Retina01:32

The Retina

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The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.
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At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
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The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle...
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In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
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Single-cell Profiling of Developing and Mature Retinal Neurons
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解码人类视网膜中非编码变异的细胞类特定作用.

Leah S VandenBosch1, Amy S Leonardson1,2, Timothy J Cherry3,4,5

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

Scientific reports
|December 11, 2025
PubMed
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了对监管元素的功能影响,并优先考虑了进一步研究的变体.

关键词:
关联调节元素的关联调节元素.机器学习是机器学习.没有编码的变体.视网膜疾病 视网膜疾病

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科学领域:

  • 基因组学就是基因组学.
  • 分子生物学分子生物学
  • 计算生物学 计算生物学

背景情况:

  • 在cis-regulatory元素中的非编码变体与遗传性视网膜疾病 (IRDs) 有关.
  • 这些调控变异的功能性表征是理解IRD病原学的重大挑战.
  • 识别具有影响力的变体对于诊断和潜在治疗IRD至关重要.

研究的目的:

  • 开发和验证机器学习 (ML) 模型,用于预测非编码变体对视网膜 cis-regulatory 元素的功能影响.
  • 加强在遗传性视网膜疾病中引起疾病的变异的识别和优先考虑.
  • 为了利用单核ATAC-seq数据进行细胞类特定变异效应预测.

主要方法:

  • 实施了一个空隙k-mer支持向量机器 (SVM) 方法,该方法是在人类视网膜的单核ATAC-seq数据上训练的.
  • 开发了18种不同的ML模型来预测变异对39437个细胞类特定监管元素的影响.
  • 利用变异影响预测 (VIP) 评分,并将其与大规模并行报告员测试 (MPRA) 相关联,以验证.

主要成果:

  • 在开发的ML模型中实现了超过90%的预测准确性 (AUROC),具有高细胞类特异性.
  • VIP评分有效突出了监管元素中的序列,包括对突变敏感的转录因子结合动机.
  • MPRA相关性证实了单核酸变异和indels的VIP分数的细胞类特定预测能力.

结论:

  • 单核表观遗传学数据可以有效地用于预测非编码序列变异的功能影响.
  • 开发的ML模型和VIP分数提供了一个强大的工具,用于在IRD中快速对患者变异进行功能分析.
  • 这种方法促进了对遗传视网膜疾病的遗传贡献的理解,并促进了变异解释.