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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

8.7K
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...
8.7K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.7K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.7K
RNA-seq03:21

RNA-seq

10.1K
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...
10.1K
CRISPR and crRNAs02:53

CRISPR and crRNAs

17.1K
Bacteria and archaea are susceptible to viral infections just like eukaryotes; therefore, they have developed a unique adaptive immune system to protect themselves. Clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins (CRISPR-Cas) are present in more than 45% of known bacteria and 90% of known archaea.
The CRISPR-Cas system stores a copy of foreign DNA in the host genome and uses it to identify the foreign DNA upon reinfection. CRISPR-Cas has three different...
17.1K
Nucleic Acid Structure01:25

Nucleic Acid Structure

6.2K
The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA...
6.2K

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相关实验视频

Updated: Jul 25, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

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通过深度学习通过使用全球和本地序列信息进行高精度ncRNA功能预测.

Alessandro Orro1, Gabriele A Trombetti1

  • 1Institute for Biomedical Technologies, National Research Council (ITB-CNR), 20054 Segrate, Italy.

Biomedicines
|June 28, 2023
PubMed
概括

预测非编码RNA (ncRNA) 功能是理解疾病的关键. 一种新的深度学习方法仅使用ncRNA序列数据进行准确,高效的功能预测,优于现有方法.

科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 分子生物学分子生物学

背景情况:

  • 非编码RNAs (ncRNAs) 在细胞调节和疾病中起着至关重要的作用.
  • 准确预测ncRNA的生物功能对于理解基因调节至关重要.
  • 目前用于ncRNA函数预测的计算方法通常受到精度或计算成本的限制.

研究的目的:

  • 开发一种新,准确和计算效率高的方法来预测ncRNA基因的生物功能.
  • 为了利用仅使用ncRNA序列信息的深度学习架构.

主要方法:

  • 一种使用深度网络架构的新计算方法.
  • 该方法完全依赖于非编码RNA序列数据,避免二次结构预测.
  • 实现功能分类的深度学习模型.

主要成果:

  • 提出的方法的准确性与使用序列和结构信息的现有方法相比或更高.
  • 与结构依赖方法相比,这种方法显著降低了计算成本.
  • 仅序列深度学习对于ncRNA功能预测的有效性已被证明.

结论:

关键词:
人工智能的人工智能是人工智能.生物信息学是一种生物信息学.功能预测 功能预测基因组学就是基因组学.机器学习是机器学习.这是一个ncRNARNA.

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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants
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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants
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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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  • 这种新的深度学习方法为预测ncRNA生物功能提供了准确而高效的解决方案.
  • 这种基于序列的方法克服了传统方法的局限性,为ncRNA研究提供了有价值的工具.
  • 这些发现为更好地了解ncRNA在健康和疾病中的作用铺平了道路.