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相关概念视频

Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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What is Population Genetics?01:25

What is Population Genetics?

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A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
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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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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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在全球人类人口中计算准确度.

Jordan L Cahoon1,2,3, Xinyue Rui1, Echo Tang2

  • 1Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.

bioRxiv : the preprint server for biology
|June 9, 2023
PubMed
概括
此摘要是机器生成的。

在全球人口中,基因型赋值的准确性差异很大,非欧洲祖先的表现较低. 目前的归算方法和参考面板并不能完全解决这种差异,突出显示需要在遗传研究中增加多样性.

科学领域:

  • 基因组学就是基因组学.
  • 人口遗传学 人口遗传学

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  • 生物信息学是一种生物信息学.
  • 背景情况:

    • 基因型归因对于全基因组关联研究 (GWAS) 至关重要.
    • 现有的归算参考小组,如TOPMed,低于非欧洲祖先,导致公平性问题.
    • 尽管取得了进展,但对北美以外的人口的归算准确性仍然是一个问题.

    结论:

    • 目前的参考小组的多样性和规模不足以进行公平的基因型归算.
    • 在经过测试的设计中,元归算并没有提高代表性不足的非欧洲人口的归算准确性.
    • 在参考小组中增加多样性和样本规模对于促进基因研究中的公平性至关重要.