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

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
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.8K
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.
18.8K
RNA-seq03:21

RNA-seq

9.8K
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...
9.8K

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

DOGT: Double-Order Graph Transformers With Adaptive Node-Group Learning.

IEEE transactions on neural networks and learning systems·2026
Same author

Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

CGHNet: Cross-Guided 2D-3D Hybrid Network with attention mechanism for focal liver lesion classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same author

WEmarker: breast cancer-specific prognostic analysis with weighted multiplex network embedding.

IEEE transactions on computational biology and bioinformatics·2026
Same author

FluNexus: A versatile web platform for antigenic prediction and visualization of influenza A viruses.

iMeta·2026
Same author

GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model.

Briefings in bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 11, 2025

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

923

FindCSV:一种基于长期阅读的方法,用于检测复杂的结构变异.

Yan Zheng1, Xuequn Shang2

  • 1School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xi'an, 710072, China. yan.zheng@mail.nwpu.edu.cn.

BMC bioinformatics
|September 28, 2024
PubMed
概括
此摘要是机器生成的。

一种新的深度学习方法,FindCSV,通过使用长读序列改进了复杂结构变异 (SV) 的检测. 这种进步为识别影响健康和进化的遗传变异提供了更高的准确性.

关键词:
复杂的结构变异.共识序列的共识序列.深度学习是一种深度学习.长读序列化数据的长读序列化数据.

更多相关视频

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
10:34

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

22.7K
Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

13.7K

相关实验视频

Last Updated: Jun 11, 2025

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

923
Ultra-long Read Sequencing for Whole Genomic DNA Analysis
10:34

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

22.7K
Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

13.7K

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.

背景情况:

  • 结构变异 (SVs) 在遗传疾病和进化过程中至关重要.
  • 简单的SV检测方法已经建立,但复杂的SV的影响越来越被认可.
  • 复杂的SVs目前缺乏精确的检测方法,需要新的方法.

研究的目的:

  • 开发一种新的,高效的,准确的方法来检测复杂的结构变异.
  • 解决现有方法在识别复杂基因组变化的局限性.

主要方法:

  • 提出了FindCSV,这是一种利用深度学习技术的新方法.
  • 采用共识序列来增强结构变异检测.
  • 使用长读数测序数据进行分析.

主要成果:

  • 与现有方法相比,FindCSV在检测复杂和简单的结构变异方面表现出卓越的性能.
  • 该方法在真实和模拟的基因组数据上都取得了合理的准确性.

结论:

  • FindCSV是一种新的,准确的工具,用于检测复杂和简单的结构变异.
  • 开发的方法显示了对推进基因组研究和临床应用的前景.
  • 源代码是公开可用的,供社区使用和进一步开发.