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相关概念视频

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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RNA-seq03:21

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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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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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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在后测序时代的结构和功能预测,评估和验证.

Chang Li1,2, Yixuan Luo3, Yibo Xie4

  • 1Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.

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概括

解释具有不确定的意义 (VUS) 的遗传变异具有挑战性. 人工智能 (AI) 提供高效的in silico预测工具,特别是使用蛋白质结构,以加快VUS分类.

关键词:
人工智能的人工智能是人工智能.临床解释 临床解释错误的意义变体这是后测序时代.蛋白质结构 蛋白质结构

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科学领域:

  • 基因组学和生物信息学
  • 计算生物学 计算生物学
  • 人工智能在医学中的应用

背景情况:

  • 基因组测序数据的快速增长导致了大量具有不确定的意义的遗传变异 (VUS).
  • 分类VUS对于遗传诊断至关重要,但由于大量的变异和有限的实验数据,仍然是一个重大挑战.
  • 目前的实验方法不足以解决发现的VUS的规模.

研究的目的:

  • 审查基于人工智能 (AI) 的误解变体预测方法的当前状态.
  • 突出使用基于蛋白质结构的AI模型用于VUS解释的潜力和挑战.
  • 强调在测序后的时代需要先进的in silico功能预测器.

主要方法:

  • 对VUS预测中的AI应用现有文献的审查.
  • 专注于利用蛋白质结构信息的AI模型.
  • 讨论用于变量效应预测的计算方法.

主要成果:

  • 人工智能在预测遗传变异的功能影响方面表现出高效率和准确性.
  • 基于蛋白质结构的AI模型显示了改善VUS分类的前景.
  • 在已识别的VUS和实验验证的变体数量之间存在很大的差距.

结论:

  • 人工智能是加速VUS解释的强大工具.
  • 基于蛋白质结构的AI预测为解决VUS挑战提供了一个有希望的途径.
  • 迫切需要进一步开发和验证in silico VUS预测器.