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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Genetic Variation01:25

Genetic Variation

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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,...
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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相关实验视频

Updated: Jun 9, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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在个性化基因组序列上训练深度学习模型可以改善变异效应预测.

Adam Y He1, Nathan P Palamuttam1, Charles G Danko1

  • 1Cornell University, Ithaca, NY 14850.

bioRxiv : the preprint server for biology
|October 28, 2024
PubMed
概括
此摘要是机器生成的。

在功能性基因组数据和个人基因组上训练序列到功能模型显著改善了变异效应预测. 这些模型在不同的细胞环境中保持性能,有助于解释与特征相关的遗传变异.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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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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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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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.
  • 计算生物学 计算生物学

背景情况:

  • 序列到功能模型对于理解遗传变异的分子效应至关重要.
  • 目前的模型在变异效应预测的准确性方面面临挑战.
  • 解释特征相关的遗传变异仍然是一个重大挑战.

研究的目的:

  • 为了提高序列到函数模型的性能,用于变量效应预测.
  • 调查训练数据对模型准确性的影响.
  • 评估学习变量效应表示的概括性.

主要方法:

  • 使用功能性基因组数据训练序列到功能模型.
  • 将匹配的个人基因组集成到培训过程中.
  • 对各种细胞环境和实验读数进行微调模型.

主要成果:

  • 在功能性基因组数据和匹配的个人基因组上训练的模型显示了改进的变异效应预测.
  • 学习的变量效应表示是强大的,并在微调时保留.
  • 增强模型性能对理解遗传变异和特征有影响.

结论:

  • 整合个人基因组与功能基因组数据是改善序列到功能模型的关键.
  • 开发的模型为变量效应预测提供了更准确的方法.
  • 这项工作有助于更好地解释影响人类特征的遗传变异.