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相关概念视频

Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Epistasis Analysis01:09

Epistasis Analysis

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Genetic Drift03:33

Genetic Drift

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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
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Genetic Variation01:25

Genetic Variation

268
Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
268
In-vitro Mutagenesis01:16

In-vitro Mutagenesis

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To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
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相关实验视频

Updated: Jun 13, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

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学习遗传乱的影响与变异性因果推理.

Emily Liu, Jiaqi Zhang, Caroline Uhler

    bioRxiv : the preprint server for biology
    |June 12, 2025
    PubMed
    概括

    我们开发了一个混合计算模型,单细胞因果变异自编码器 (SCCVAE),以预测细胞对遗传干扰的反应. 在预测未见的遗传变化方面,SCCVAE的性能优于现有的方法,有助于功能基因组学和治疗标识.

    科学领域:

    • 基因组学就是基因组学.
    • 计算生物学 计算生物学
    • 系统生物学 系统生物学

    背景情况:

    • 测序方面的进步,比如Perturb-seq,使单细胞转录组对遗传干扰的分析成为可能.
    • 现有的计算模型在一般化方面扎:深度学习过度,而机械模型对于大规模数据来说太简单了.

    研究的目的:

    • 开发一种混合计算模型,整合机械因果推断和深度学习,以对细胞对遗传干扰的反应进行可靠的预测.
    • 为了提高对未见扰动的推断能力,并提高单细胞转录组数据的解释性.

    主要方法:

    • 提出了单细胞因果变异自编码器 (SCCVAE),这是一个混合模型,将学习的基因调节网络与变异自编码器相结合.
    • 机械组件将扰动模型作为通过调节网络传播的移动干预.
    • 将此集成到一个深度学习框架中,以生成全面的转录基因反应.

    主要成果:

    • 与最先进的方法相比,SCCVAE在预测对未见的遗传干扰的反应方面表现出卓越的表现.
    • 该模型的潜空间促进了功能扰动模块的识别.
    • 启用了基因淘汰实验的模拟,具有不同透度.

    结论:

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    In Vivo Modeling of the Morbid Human Genome using Danio rerio
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    Last Updated: Jun 13, 2025

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    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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    • SCCVAE提供了一个强大的工具来解释和插曲单细胞扰动反应.
    • 混合方法有效地平衡了机械理解与深度学习对复杂数据的能力.
    • 这种方法推进了功能基因组学,并有助于识别潜在的治疗点.