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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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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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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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Synthetic Biology02:55

Synthetic Biology

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
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Effects of feedback01:24

Effects of feedback

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Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
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Genome Annotation and Assembly03:36

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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相关实验视频

Updated: Sep 9, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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整合实验反可以改善生物序列的生成模型

Francesco Calvanese1,2, Giovanni Peinetti1,3, Polina Pavlinova2

  • 1Sorbonne Université, CNRS, Department of Computational, Quantitative and Synthetic Biology-CQSB, 75005 Paris, France.

Nucleic acids research
|September 3, 2025
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概括
此摘要是机器生成的。

生物分子设计的生成模型与假阳性作斗争. 整合实验反显著改善了功能性RNA和蛋白质序列的生成,从6.7%提升到63.7%.

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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相关实验视频

Last Updated: Sep 9, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

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科学领域:

  • 计算生物学
  • 分子生物学
  • 生物分子工程

背景情况:

  • 生成概率模型显示了设计人工RNA和蛋白质序列的潜力.
  • 一个主要的限制是错误阳性的高率,预测的功能序列未能在实验验证中得到验证.

研究的目的:

  • 解决生成生物分子设计中的假阳性问题.
  • 探索重新整合实验反到模型设计的影响.
  • 改善功能生物分子序列的生成.

主要方法:

  • 提出了一个基于可能性的重返社会计划.
  • 对RNA和蛋白质数据集进行了广泛的计算实验.
  • 在I组内核RNA自我拼接 ribozymes 上进行了湿实验.

主要成果:

  • 基于反的方法显著提高了模型生成功能序列的能力.
  • 在整合实验数据后,活性设计从6.7%增加到63.7% (在45个突变).
  • 该方法在设计自剪接 ribozymes 方面表现出特别的有效性.

结论:

  • 整合最近的实验数据直接解决了生物分子设计中的假阳性挑战.
  • 这种基于反的方法为设计功能性RNA和蛋白质序列提供了显著的改进.
  • 拟议的方案提高了生成生物分子设计的可靠性和成功率.