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相关概念视频

RNA Splicing01:32

RNA Splicing

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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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Alternative RNA Splicing02:18

Alternative RNA Splicing

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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...
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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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Pre-mRNA Processing: RNA Splicing01:36

Pre-mRNA Processing: RNA Splicing

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Point and Frameshift Mutations01:30

Point and Frameshift Mutations

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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
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相关实验视频

Updated: Jul 14, 2025

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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帕迪瓦斯:深层内部变种的致病性预测器,导致异常拼接.

Ryo Kurosawa1, Kei Iida2,3, Masahiko Ajiro4,5

  • 1Department of Anatomy and Developmental Biology, Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan. kurosawa.ryo.43r@st.kyoto-u.ac.jp.

BMC genomics
|October 10, 2023
PubMed
概括
此摘要是机器生成的。

我们开发了PDIVAS,这是一种用于识别影响RNA拼接的致病深内突变的工具. 这种预测器有效地检测出致病性拼接改变变体 (SAV),改善遗传疾病诊断.

关键词:
深层内部子的内部子是什么基因组学就是基因组学.机器学习是机器学习.非编码地区地区.病原性预测的预测.通过RNA拼接进行RNA拼接.变体解释的变体解释

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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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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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In Vivo Modeling of the Morbid Human Genome using Danio rerio

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相关实验视频

Last Updated: Jul 14, 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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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 分子遗传学 分子遗传学

背景情况:

  • 深层内部变异是挑战评估遗传疾病的因果关系.
  • 在数以百万计的深层内部变异中识别致病变体对研究人员来说是一个重大的技术障碍.

研究的目的:

  • 开发一种计算工具,PDIVAS,用于高效地检测致病性深内部变异.
  • 为应对在遗传疾病诊断中评估深层内突变的挑战.

主要方法:

  • 开发PDIVAS使用集体机器学习算法,训练在精选的致病性和良性拼接改变变体 (SAV) 上.
  • 利用拼接功能和一个拼接约束度量来优化预测性能.
  • 评估PDIVAS性能与以前的变种分类和优先级预测指标相比.

主要成果:

  • PDIVAS实现了高精度,平均精度为0.92和最大的马修斯相关系数 (MCC) 为0.88,优于现有方法.
  • 在PDIVAS对基因组测序的应用中,每个人平均发现了27个病原候选人,对已知的病原性SAVs有95%的灵敏度.
  • 与之前的预测者相比,PDIVAS在模拟的患者基因组中展示了更有效的致病变异优先级.

结论:

  • 帕迪瓦斯 (PDIVAS) 有助于有效检测致病的深内体SAV,提高了遗传疾病研究中的诊断产量.
  • 建议将PDIVAS集成到变体解释管道中,以提高诊断能力.
  • 该PDIVAS工具是公开可访问的,用于研究.