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

DNA Microarrays02:34

DNA Microarrays

17.2K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
17.2K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.2K
Next-generation Sequencing03:00

Next-generation Sequencing

87.4K
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....
87.4K

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

Rethinking bioinformatics expertise in the era of artificial intelligence.

NPJ digital medicine·2026
Same author

Enhancing protein structure prediction: evaluating the role of amino acid physicochemical features in homology search.

Briefings in bioinformatics·2026
Same author

Protrec2: tissue-specific network-based missing protein recovery method.

Briefings in bioinformatics·2025
Same author

Establishing the Asia & Pacific Bioinformatics Joint Congress: a historic milestone in regional bioinformatics collaboration.

Briefings in bioinformatics·2025
Same author

Comprehensive benchmarking of methods for mutation calling in circulating tumor DNA.

Nature communications·2025
Same author

Efficient trace reconstruction in DNA storage systems using bidirectional beam search.

iScience·2025
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 5, 2025

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling
08:04

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling

Published on: October 8, 2019

8.6K

基准测试用于DNA结合蛋白质识别的最新计算工具.

Xizi Luo1, Amadeus Song Yi Chi1, Andre Huikai Lin1

  • 1School of Computing, National University of Singapore, Singapore 119077, Singapore.

Briefings in bioinformatics
|December 10, 2024
PubMed
概括
此摘要是机器生成的。

识别DNA结合蛋白 (DBPs) 是理解基因调节的关键. 这项研究对计算工具进行了基准测试,揭示了数据泄露问题,并提出了一种改善DBP识别的共识方法.

关键词:
这是一次爆炸式爆炸.这是一张CD-HIT.这是一种DNA结合蛋白质.深度学习是一种深度学习.机器学习是机器学习.一个动机,一个动机,一个动机.

更多相关视频

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

13.5K
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

4.9K

相关实验视频

Last Updated: Jun 5, 2025

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling
08:04

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling

Published on: October 8, 2019

8.6K
Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

13.5K
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

4.9K

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.
  • 分子生物学分子生物学

背景情况:

  • 结合DNA的蛋白质 (DBPs) 对于基本的细胞过程至关重要,包括基因调节,DNA复制和转录控制.
  • 准确识别DBPs对于全面的基因组注释和理解生物机制至关重要.

研究的目的:

  • 进行对11种最先进和传统的计算工具进行公正的基准测试,以识别DNA结合蛋白质.
  • 为了解决和突出传统评估数据集中普遍存在的数据泄露问题.
  • 引入新的评估数据集,以便更强大,更可靠地对DBP识别工具进行基准测试.

主要方法:

  • 进行了11种先进和传统的计算工具 (例如,ScanProsite,BLAST,HMMER) 的公正基准测试,用于DBP识别.
  • 引入了新的,精心策划的评估数据集,以减轻数据泄露,并确保现实的绩效评估.
  • 开发了一个全面的评估管道来分析模型限制,特征提取和培训方法.

主要成果:

  • 在传统数据集中发现了显著的数据泄露,导致了高估的性能指标.
  • 突出了现有计算模型的局限性,特征提取技术和DBP识别的培训策略.
  • 证明了将顶级计算工具和BLAST的预测结合在一起的共识方法显著提高了DBP识别准确性.

结论:

  • 该研究对当前的DBP识别工具和方法进行了批判性评估.
  • 新的数据集和共识方法为DBP识别和未来工具开发提供了更可靠的框架.
  • 提供了实现共识方法的用户友好软件,以促进基因组研究中的DBP识别.