Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Next-generation Sequencing03:00

Next-generation Sequencing

88.5K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
88.5K
Comparing Copy Number Variations and SNPs02:26

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%...
17.7K
RNA-seq03:21

RNA-seq

9.9K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.9K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.2K
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...
13.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Sequence Diversity Lost in Early Pregnancy.

Obstetrical & gynecological survey·2026
Same author

African-ancestry-specific variant IKKβ p.Glu502Lys confers high lupus risk.

Nature genetics·2025
Same author

Mucosal calprotectin is associated with severity of aGI-GVHD and poor outcomes after allogeneic stem cell transplantation.

Blood·2025
Same author

Sequence diversity lost in early pregnancy.

Nature·2025
Same author

Missense variants in FRS3 affect body mass index in populations of diverse ancestries.

Nature communications·2025
Same author

Complete human recombination maps.

Nature·2025
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
查看所有相关文章

相关实验视频

Updated: Jun 14, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K

GGTyper:使用短读测序数据进行复杂结构变体的基因型鉴定.

Tim Mirus1, Robert Lohmayer1, Clementine Döhring1

  • 1AG Algorithmic Bioinformatics, Leibniz-Institut für Immuntherapie, Regensburg 93053, Germany.

Bioinformatics (Oxford, England)
|September 4, 2024
PubMed
概括
此摘要是机器生成的。

一种新的计算方法在短读基因组中准确地基因型复杂结构变异 (SV). 这种方法预测了基因型化难度,并且在大型数据集上表现良好,推进了基因组分析.

更多相关视频

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

931
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.1K

相关实验视频

Last Updated: Jun 14, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

931
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.1K

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:

  • 复杂的结构变异 (SV) 是人类多样性和孟德尔病的重要贡献者.
  • 目前复杂SV的分析工具有限,阻碍了全面的基因组分析.

研究的目的:

  • 开发和验证一种新的计算方法,用于在短读测序基因组中精确的复杂结构变异 (SV) 的基因型鉴定.
  • 创建一种能够预测基因型化难度的方法,使可靠的变异调用能够有效评估数据需求.

主要方法:

  • 开发了一种计算方法,用于计算对齐读取对属性的基因型特定概率分布.
  • 利用这些分布来确定观察到的读取对的最可能的基因型.
  • 基于这些概率分布,集成了一个基因型化难度预测指标.

主要成果:

  • 新方法在模拟和真实基因组数据上表现出优于现有基因型的性能.
  • 在7829个人类基因组中与人口遗传学原理和遗传模式达到了高度一致.
  • 在预测的基因型化难度和实际精度之间显示出强烈的相关性,计算资源要求较低.

结论:

  • 开发的方法在大型基因组研究中为复杂的SV基因型定型提供了强大而高效的解决方案.
  • 这种方法非常适合各种生物医学应用,从小型队列到非常大的人口规模测序项目.
  • 源代码的可用性促进了更广泛的采用和该领域的进一步发展.