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

Genomics02:02

Genomics

36.3K
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...
36.3K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.7K
Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

18.9K
Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
18.9K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.9K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
18.9K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.4K
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...
13.4K

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

Reconstruction of ancestral plant genomes for inter-crop translational research.

Molecular plant·2026
Same author

From glass to plasma: in vivo phenolic signatures and metabolic pathways after acute red and white wine intake.

Food research international (Ottawa, Ont.)·2026
Same author

DNA methylation around transcription start sites is not globally associated with transcription in the grain of natural and synthetic hexaploid wheat.

BMC plant biology·2026
Same author

The fecal microbiota of lactating Holstein cows: A meta-analysis highlighting key microbial profiles and methodological challenges.

Journal of dairy science·2026
Same author

Author Correction: Striking convergent selection history of wheat and barley and its potential for breeding.

Nature plants·2025
Same author

Striking convergent selection history of wheat and barley and its potential for breeding.

Nature plants·2025

相关实验视频

Updated: Jul 5, 2025

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

19.7K

基因组数据整合教程,一种植物病例研究.

Emile Mardoc1, Mamadou Dia Sow1, Sébastien Déjean2

  • 1UCA-INRAE UMR 1095 Genetics, Diversity and Ecophysiology of Cereals (GDEC), 5 Chemin de Beaulieu, 63000, Clermont-Ferrand, France.

BMC genomics
|January 17, 2024
PubMed
概括

本教程概述了整合下一代测序 (NGS) 技术中的基因组数据的最佳实践. 它提供了一个六步指南,以解决分析大规模基因组数据集的复杂性,帮助生物发现.

关键词:
生物学 生物学 生物学整合 整合 整合俄米克斯 (Omics) 是一个电子游戏.系统 系统 系统

更多相关视频

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
10:40

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

10.5K
Isolation and Transcriptome Analysis of Plant Cell Types
08:53

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

1.5K

相关实验视频

Last Updated: Jul 5, 2025

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

19.7K
Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
10:40

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

10.5K
Isolation and Transcriptome Analysis of Plant Cell Types
08:53

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

1.5K

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:

  • 下一代测序 (NGS) 技术产生了大量的基因组数据集.
  • 整合和分析这些大规模数据集在生物学中提出了重大的概念和分析挑战.

研究的目的:

  • 为基因组数据集成的最佳实践提供一个实用,六步的教程.
  • 为了解决与分析大规模基因组数据相关的复杂性.

主要方法:

  • 设计一个数据矩阵.
  • 为数据分析制定特定的生物问题.
  • 选择适当的分析工具.
  • 数据预处理和初步分析.
  • 执行基因组数据整合过程.

主要成果:

  • 描述了基因组数据集成最佳实践的六步教程.
  • 该教程成功地使用公开可用的 (Populus L.) 的基因组数据进行了演示.

结论:

  • 开发了一个新的图形输出,cimDiablo_v2,用于无监督的多块分析.
  • 该工具有助于识别基因组数据变异和相互作用的关键驱动因素.