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相关概念视频

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

8.5K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
8.5K
The Nucleolus02:55

The Nucleolus

8.7K
The nucleolus is the most prominent substructure of the nucleus. When it was first discovered, it was considered to be an isolated organelle that forms fibrils and granules. In 1931, the relationship between the nucleolus and chromosomes was first described by Heitz. He observed that the appearance and size of nucleolus varies depending on the stage of the cell cycle. He also noticed constricted regions on different chromosomes clustered together at definite cell cycle stages. These regions,...
8.7K
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
Ribosome Profiling02:24

Ribosome Profiling

3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.5K
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.
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相关实验视频

Updated: Jun 4, 2025

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

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针对 lncRNA 函数和目标组的计算资源

Anamika Thakur1,2, Manoj Kumar3,4

  • 1Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh, India.

Methods in molecular biology (Clifton, N.J.)
|December 20, 2024
PubMed
概括

本综述总结了用于研究长非编码RNA (lncRNAs) 的计算工具和数据库. 这些资源有助于研究人员了解ncRNA功能及其在各种疾病中的作用.

关键词:
算法算法是一种算法.分析工具是分析工具.数据库数据库数据库是一个数据库.机器学习是机器学习.在cnRNA中.

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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
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Published on: December 1, 2023

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Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells
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Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells

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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
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Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells
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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:

  • 长非编码RNAs (lncRNAs) 是生物过程中的关键调节者.
  • 失调的lncRNA表达与各种疾病有关,表明治疗潜力.
  • 为 lncRNA 研究而出现了众多的计算资源.

研究的目的:

  • 提供现有的 lncRNA 数据库和预测工具的全面审查.
  • 突出 lncRNA 资源在不同生物体 (包括人类和模型生物体) 中的重要性.
  • 引导生物学家选择适合他们的研究计算工具.

主要方法:

  • 审查 lncRNA 数据库和注册表.
  • 分析机器学习算法 (深度学习,SVM,RF) 用于lncRNA识别.
  • 汇编了关于lncRNA表达,疾病关联和目标调节的资源.

主要成果:

  • 详细概述关键的 lncRNA 数据库和最新的资源.
  • 讨论使用各种技术识别 lncRNA 的计算工具.
  • 基于数据类型的资源分类,如微分表达式和目标交互.

结论:

  • 在 silico 资源对于推进 lncRNA 研究至关重要.
  • 这一综述赋予了生物学家掌握可用的计算工具的知识.
  • 通过计算方法了解lncRNA对于生物学见解和疾病研究至关重要.