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

¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.0K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
1.0K

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

Nucleotide resolution 4-thiouridine sequencing by SNU-Seq and sf4sU-Seq reveals the transcriptional responsiveness of an epigenetically primed human genome.

Nucleic acids research·2026
Same author

De novo design and experimental characterization of bitter peptides.

NPJ science of food·2026
Same author

Generative modeling for RNA splicing prediction and design.

eLife·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

Mechanistic understanding of a bifunctional carbonate additive for enhanced performance in lithium-sulfur battery.

Energy storage materials·2026
Same author

Detection of a sequence feature for recursive splicing.

bioRxiv : the preprint server for biology·2026

相关实验视频

Updated: Jun 12, 2025

Quantifying Microorganisms at Low Concentrations Using Digital Holographic Microscopy DHM
07:27

Quantifying Microorganisms at Low Concentrations Using Digital Holographic Microscopy DHM

Published on: November 1, 2017

10.3K

使用HiFi-NNN对微生物暗物质进行注释.

Gavin Ayres1, Geraldene Munsamy1, Michael Heinzinger2

  • 1Basecamp Research Ltd., London, UK.

iScience
|June 10, 2025
PubMed
概括

HiFi-NN提高了蛋白质功能注释的准确性. 这种新方法在识别酶功能方面超过了当前的深度学习方法和BLASTp,特别是在新型蛋白质方面.

关键词:
计算机科学 计算机科学微生物学 微生物学

更多相关视频

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures
09:11

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Published on: April 4, 2021

3.1K
Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy
11:47

Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy

Published on: April 7, 2012

12.8K

相关实验视频

Last Updated: Jun 12, 2025

Quantifying Microorganisms at Low Concentrations Using Digital Holographic Microscopy DHM
07:27

Quantifying Microorganisms at Low Concentrations Using Digital Holographic Microscopy DHM

Published on: November 1, 2017

10.3K
Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures
09:11

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Published on: April 4, 2021

3.1K
Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy
11:47

Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy

Published on: April 7, 2012

12.8K

科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 酶学 是一种酶学.

背景情况:

  • 对具有酶功能的蛋白质序列进行准确的计算注释是一个重大挑战.
  • 现有的方法在精度和回忆方面存在困难,特别是对于具有远程序列同质性的蛋白质.

研究的目的:

  • 为精确的蛋白质功能注释引入HiFi-NN (层次精细调整的最近邻居搜索).
  • 评估HiFi-NN的性能与最先进的方法相比,包括深度学习和BLASTp.
  • 为了证明HiFi-NN在纠正数据库错误和注释非特征蛋白质方面的实用性.

主要方法:

  • 开发了HiFi-NN,一种新的层次精细调整的近邻搜索算法.
  • 与现有的深度学习方法和BLASTp对酶委员会 (EC) 编号注释进行基准HiFi-NN.
  • 研究了查找集多样性对HiFi-NN性能的影响.
  • 应用了HiFi-NN来纠正BRENDA数据库中的错误注释,并注释NMPFamDB序列.

主要成果:

  • HiFi-NN实现了更高的精度和回忆比最先进的深度学习方法EC号码注释.
  • 与BLASTp相比,HiFi-NN正确地识别了与BLASTp相比较低的序列身份的EC数字.
  • 查找集中的多样性增加显著改善了HiFi-NN的性能.
  • 在BRENDA数据库中成功纠正了特定的错误注释.
  • 来自NMPFamDB的注释功能暗物质蛋白序列.

结论:

  • HiFi-NN为*in silico*蛋白质功能注释提供了更准确和更强大的方法.
  • 该方法对来自遥远序列空间的蛋白质特别有效,并有助于改进现有的酶数据库.
  • HiFi-NN通过让人们更好地了解蛋白质的功能来推动生物信息学领域的发展.