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相关概念视频

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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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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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,...
268
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

14.8K
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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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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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.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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潘格诺姆意识到深度变种

Mobin Asri, Pi-Chuan Chang, Juan Carlos Mier

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    此摘要是机器生成的。

    泛基因组识别DeepVariant通过使用泛基因组引用来提高变体调用准确度. 与传统的线性引用相比,这种新的方法可以减少高达25.5%的错误.

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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:

    • 人口规模基因组学为基因组分析提供了关键的先验知识.
    • 人类泛基因组参考增强了读取映射和结构变异基因型.
    • 现有的变异调用器通常依赖于线性引用,这可能会限制准确性.

    研究的目的:

    • 为了引入泛基因意识的DeepVariant,一种新型变体调用器.
    • 为了提高变体调用准确度,利用 pangenome 引用.
    • 为了评估泛基因组意识的DeepVariant在不同测序平台和读取映射器上的表现.

    主要方法:

    • 开发了泛基因组意识的DeepVariant,一种使用泛基因组参考和样本特定读取对齐的变异调用器.
    • 在潜在变体附近生成了reads和pangenome单元类型的堆积图像.
    • 采用一个卷积神经网络 (CNN) 来进行基因型推断.
    • 在各种短读测序平台和读取映射器上评估性能.

    主要成果:

    • 在所有测试的设置中,基于泛基因组的DeepVariant始终超过了基于线性引用的DeepVariant.
    • 使用泛基因组意识方法,错误率降低了高达25.5%.
    • 元素读取与泛基因组意识的DeepVariant相结合,比现有方法高出23.6%的准确性.

    结论:

    • 泛基因识别的DeepVariant有效地区分真实变体信号与序列或对齐噪声.
    • 使用 pangenome 引用显著提高了变体调用准确度.
    • 这种方法代表了人口基因组学和个性化医学的重大进步.