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相关概念视频

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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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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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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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Viral Mutations00:36

Viral Mutations

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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
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Gene Evolution - Fast or Slow?02:05

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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相关实验视频

Updated: Jun 26, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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使用基于序列的共同进化,网络分析和机器学习来预测致病单核酸变体的热点.

Wenjun Zheng1

  • 1Department of Physics, State University of New York at Buffalo, Buffalo, NY, United States of America.

PloS one
|May 14, 2024
PubMed
概括
此摘要是机器生成的。

预测引起疾病的蛋白质突变对于个性化医学至关重要. 这项研究引入了一种新的基于序列的方法,使用蛋白质残留接触网络和机器学习来准确识别突变热点.

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科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 准确预测致病突变对于个性化医学至关重要,但仍然具有挑战性.
  • 现有的计算方法通常依赖于蛋白质结构或忽视残留相互作用,限制了它们的预测能力.
  • 需要高通量,基于序列的方法来识别有害的蛋白质变体.

研究的目的:

  • 开发一种基于序列的工作流程,用于预测蛋白质中引起疾病的突变热点.
  • 利用蛋白质残留接触网络和机器学习进行增强的变异部位预测.
  • 提高鉴定疾病突变关键残留物的准确性和吞吐量.

主要方法:

  • 集成多个基于深度学习的共同进化分析工具 (RaptorX,DeepMetaPSICOV,SPOT-Contact) 来构建蛋白质残留网络.
  • 采用机器学习算法 (随机森林,梯度增强,极端梯度增强) 来结合网络中心性得分.
  • 利用了107种具有已知疾病突变的蛋白质的数据集进行严格评估.

主要成果:

  • 通过机器学习结合网络分数来预测突变热点的有效性.
  • 展示了基于序列的蛋白质残留接触网络在识别疾病相关残留物的实用性.
  • 个人和集体评估证实了网络中心性得分的预测能力.

结论:

  • 一个有前途的策略是通过机器学习结合集团网络得分,以准确预测热点.
  • 这种基于序列的方法克服了结构依赖方法和独立残留分析的局限性.
  • 这些发现将促进疾病诊断和向治疗的设计.