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相关概念视频

Long-patch Base Excision Repair01:02

Long-patch Base Excision Repair

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Since the discovery of the two BER pathways, there has been a debate about how a cell chooses one pathway over the other and the factors determining this selection. Numerous in vitro experiments have pointed out multiple determinants for the sub-pathway selection. These are:
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Mismatch Repair01:20

Mismatch Repair

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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
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Base Excision Repair01:54

Base Excision Repair

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One of the common DNA damages is the chemical alteration of single bases by alkylation, oxidation, or deamination. The altered bases cause mispairing and strand breakage during replication. This type of damage causes minimal change to the DNA double helix structure and can be repaired by the base excision repair (BER) pathways. BER corrects damaged DNA sequences by removing the damaged base and restoring the original base sequence using the complementary strand as a template.
The first step of...
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Updated: May 14, 2025

Functional Assessment of BRCA1 variants using CRISPR-Mediated Base Editors
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ReQuant:改进了基调修改,通过 k-mer 值赋值调用.

Roy Straver1,2, Carlo Vermeulen1,2, Joe R Verity-Legg2,3

  • 1Center for Molecular Medicine, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.

Nucleic acids research
|May 10, 2025
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概括
此摘要是机器生成的。

使用有限的训练,ReQuant可以准确地从纳米孔测序数据中调用DNA修改. 这种算法为未见的环境赋值模型,推进基调修改研究.

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科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 分子生物学分子生物学

背景情况:

  • 纳米孔测序检测直接从原始电信号中检测到DNA基基修饰.
  • 目前的方法,包括深度学习 (DL),需要为所有序列环境提供广泛的训练数据,这往往是不切实际的.
  • 由于数据的局限性,研究主要集中在CpG环境中的5-甲基细胞素 (5mC).

研究的目的:

  • 开发一种新的算法ReQuant,用于从有限的训练数据中归因DNA修饰模型.
  • 为了能够准确地识别纳米孔测序数据中的各种基因修改,即使在未代表的序列环境中.
  • 克服现有方法的局限性,需要全面的培训数据集.

主要方法:

  • 开发了ReQuant,这是一种算法,可以从稀疏的k-mer上下文训练数据中赋予完整的,基于k-mer的修改模型.
  • 应用ReQuant到Lambda Phage R9和人类R10纳米孔测序数据.
  • 评估CpG/GpC甲基化和CpG葡萄糖化的归算准确性.

主要成果:

  • 在调用调用修改中,ReQuant证明了高准确度,使用所有可能的6mers的≤25%进行训练.
  • 该算法成功地对不同的纳米孔测序数据 (R9和R10) 进行了概括.
  • 展示了DNA修饰对纳米孔电流信号的一致和可预测的影响.

结论:

  • ReQuant有效地归因于DNA修饰模型,规避了修饰特定DL工具的需要.
  • 这种方法使得即使所有序列上下文都不可用,也可以调用基调修改.
  • 这项工作显著扩大了使用纳米孔技术进行基改研究的范围.