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

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.9K
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%...
17.9K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

15.9K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.9K
Genetic Screens02:46

Genetic Screens

5.1K
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...
5.1K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

3.1K
3.1K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.1K
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...
6.1K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.2K
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...
14.2K

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

Remote evaluation of rice nitrogen utilization efficiency using chlorophyll-related spectral indices derived from unmanned aerial vehicle imagery.

Frontiers in plant science·2026
Same author

Accurate identification and volume computation of molecular interaction regions over 3D triangular meshes.

Journal of molecular graphics & modelling·2026
Same author

Prediction of Cancer Drug Response Based on Hypergraph Convolutional Network and Contrastive Learning.

IEEE transactions on computational biology and bioinformatics·2026
Same author

Congenital cyclic oculomotor palsy and spasms: a review of the literature and presentation of two new cases.

Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus·2026
Same author

Two New Species of the Genus <i>Caryanda</i> Stål, 1878 (Orthoptera: Acrididae) from Yunnan, China Identified Based on Morphological and Molecular Data.

Insects·2026
Same author

Evaluation of the feasibility, safety, and preliminary effectiveness of coil and foam embolization in patients with venous-origin chronic pelvic pain.

Frontiers in medicine·2026
Same journal

Improving Cancer Driver Gene Prediction using Biological knowledge-guided Prompts for LLM.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-task Learning Method.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

A Multi-Modal Framework for Phage-Host Interaction Prediction Using Multi-View Contrastive Learning.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Decoding Gene-Disease Associations with Computational Methods: A Survey.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

A Competitive Coevolution-based Cancer Driver Pathway Identification Algorithm for Maximizing Coverage, Mutual Exclusivity, and Subnet Importance.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Prediction of GO Terms Based on Partitioning PPI Networks into Highly Connected Components.

IEEE transactions on computational biology and bioinformatics·2026
查看所有相关文章

相关实验视频

Updated: Sep 11, 2025

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
09:33

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Published on: August 25, 2023

1.2K

基于GPU的进化辅助多任务处理,用于快速SNP交互检测.

Fangting Li, Ying Yin, Xin Wang

    IEEE transactions on computational biology and bioinformatics
    |August 14, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了一种GPU加速算法 (GEAMT),用于在全基因组协会研究 (GWAS) 中有效检测复杂的单核酸多态 (SNP) 相互作用. 通过利用多个GPU的进化多任务处理,GEAMT提高了搜索准确度和速度.

    更多相关视频

    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

    10.2K
    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

    13.1K

    相关实验视频

    Last Updated: Sep 11, 2025

    Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
    09:33

    Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

    Published on: August 25, 2023

    1.2K
    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

    10.2K
    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

    13.1K

    科学领域:

    • 遗传学 遗传学是一种遗传学.
    • 计算生物学 计算生物学
    • 生物信息学是一种生物信息学.

    背景情况:

    • 在数百万个单核酸多态 (SNP) 之间识别复杂的相互作用对于理解复杂疾病的遗传基础至关重要.
    • 现有的基因组广泛关联研究 (GWAS) 基于进化算法 (EA) 的方法在高维数据集中面临局部最佳和高计算成本的挑战.

    研究的目的:

    • 开发一种快速而准确的方法来检测GWAS中的SNP相互作用.
    • 解决基于EA的现有方法在高维基遗传数据中的局限性.

    主要方法:

    • 引入了一种基于GPU的进化辅助多任务算法 (GEAMT) 用于SNP交互检测.
    • GEAMT使用一个主要任务进行全球搜索和辅助任务进行本地优化,并在任务之间传输信息.
    • 该算法在多个图形处理单元 (GPU) 上实现,以利用并行性和内存带宽.

    主要成果:

    • 通过其多GPU实现,GEAMT表现出了显著的可扩展性和效率.
    • 与现有方法相比,对合成和现实数据集的实验显示了搜索准确性和速度的显著改进.
    • 辅助任务通过跨任务的知识共享来增强人口多样性和融合速度.

    结论:

    • 在GWAS中,GEAMT提供了一种强大而高效的解决方案,用于识别复杂的SNP相互作用.
    • 以GPU为动力的进化型多任务处理方法有效地克服了高维基遗传数据分析中的计算挑战.
    • 这种方法有可能加速发现复杂疾病背后的遗传结构.