:一个R包用于 de novo 变种分析
1Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA. tychele@wustl.edu.
BMC bioinformatics
|September 2, 2023
概括
开发了一个新的R包,,以弥合评估新变种的差距. 它有助于评估这些遗传变异的质量和特征,帮助人类表型研究.
科学领域:
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
背景情况:
- 对于理解人类表型而言,de novo变异至关重要.
- 现有的工具专注于变量调用或负载测试,留下评估缺口.
- 评估 de novo 变体质量和调用集特征至关重要.
研究的目的:
- 为了引入,一个R包,旨在进行中期de novo变种评估.
- 为分析 de novo 变种质量和特征提供一个工具.
主要方法:
- 开发了"树R"包.
- 它可以根据个体,变异类型或基因组区域对新变异数据进行子设置.
- 它计算了诸如变体计数,长度和CpG位点存在等特征.
主要成果:
- 艾科恩检查了各种新的变种特征.
- 它分析了与父母年龄相关的变异特征.
- 该套件有助于深入分析de novo变种的呼叫集.
结论:
- 艾科恩解决了新变种评估中的一个关键缺口.
- 这个R包将有利于研究新变异的研究人员.
- 它有助于理解新的遗传变异的生物特征.
相关概念视频
Comparing Copy Number Variations and SNPs
17.7K
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%...
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%...
17.7K
Genome-wide Association Studies-GWAS
13.6K
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...
GWAS does not require the identification of the target gene involved in...
13.6K
Introduction to R
397
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
397
Statistical Software for Data Analysis and Clinical Trials
619
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
619
Statistical Analysis: Overview
6.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.6K
Genomics
36.4K
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...
36.4K


