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Pedigree Analysis01:35

Pedigree Analysis

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Overview
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Epistasis Analysis01:09

Epistasis Analysis

5.0K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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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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Karyotyping01:17

Karyotyping

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Overview
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Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
4.9K

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相关实验视频

Updated: Jun 27, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K

基因型表示图:使生物库规模数据的有效分析成为可能.

Drew DeHaas1, Ziqing Pan1, Xinzhu Wei1

  • 1Department of Computational Biology, Cornell University, Ithaca, NY.

bioRxiv : the preprint server for biology
|May 7, 2024
PubMed
概括

基因组数据存储效率低下. 新的基因型表示图 (GRG) 紧地存储了整个基因组的分阶段多态,使得大基因数据集的分析速度更快.

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.
  • 数据结构 数据结构

背景情况:

  • 大规模的基因多态数据存储在计算上是繁重的.
  • 当前的表格格式 (例如,VCF) 对像英国生物库 (350TB) 这样的大规模数据集是低效的.

研究的目的:

  • 为基因组数据引入一个紧而高效的数据结构.
  • 能够更快地对大规模遗传变异进行计算分析.

主要方法:

  • 开发了基因型表示图 (GRG),一种层次图形结构.
  • GRG利用样本间的变体共享来实现无损压缩.
  • 创建命令行工具和库 (C++,Python) 用于GRG构建和处理.

主要成果:

  • GRG压缩了生物银行规模的人类数据,以适应服务器RAM (5-26GB/染色体).
  • 200,000个英国生物库基因组以GRG格式压缩到160GB (比VCF小13倍).
  • 通过图形穿越,GRG可以显著更快地计算变体总结 (例如,等位基因频率,关联效应).

结论:

  • GRG提供了一个可扩展的解决方案,用于分析大规模的基因组数据集.

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Infinium Assay for Large-scale SNP Genotyping Applications
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  • GRG促进了高效的重复计算和交互式数据分析.
  • 预计基于GRG的算法将降低基因组计算的成本并提高基因组计算的可扩展性.