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相关概念视频

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.7K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
17.7K
RNA-seq03:21

RNA-seq

9.9K
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...
9.9K
Real Time RT-PCR02:57

Real Time RT-PCR

57.2K
Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
57.2K

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相关实验视频

Updated: Jun 23, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
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深度学习用于预测16S rRNA基因拷贝数.

Jiazheng Miao1,2, Tianlai Chen1,3, Mustafa Misir4

  • 1Division of Applied and Natural Sciences, Duke Kunshan University, Suzhou, China.

Scientific reports
|June 20, 2024
PubMed
概括

我们开发了ANNA16,这是一种深度学习工具,可以直接从基因序列中准确估计16S rRNA基因拷贝数. 这种方法通过超越现有算法来提高微生物组分析的准确性.

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Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization
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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
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相关实验视频

Last Updated: Jun 23, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
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Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization
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科学领域:

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

背景情况:

  • 16S rRNA基因元编码对于微生物组的分析至关重要.
  • 准确的16S rRNA基因拷贝数 (16S GCN) 估计对于定量微生物组分析至关重要.
  • 对于16S GCN预测的现有生物信息工具依赖于分类学或族系学.

研究的目的:

  • 介绍ANNA16,一种新的深度学习方法,用于直接从16S基因序列中估计16S GCN.
  • 评估ANNA16的性能与当前的16S GCN预测方法相比.
  • 探索ANNA16识别信息序列位置的能力.

主要方法:

  • 开发了ANNA16,一个人工神经网络对16SrRNA基因拷贝数进行近似,利用深度学习.
  • 在 27,579 16S rRNA 基因序列和来自 rrnDB 数据库的相关 GCN 数据上训练了 ANNA16.
  • 采用沙普利添加式解释 (SHAP) 来解释ANNA16的预测并识别序列特征.

主要成果:

  • 与现有的算法相比,ANNA16在估计16S GCN方面表现出优异的性能.
  • SHAP分析显示,ANNA16能够在没有先前的遗传学知识的情况下识别信息序列位置.
  • 这些发现表明ANNA16具有超出16S GCN预测范围的应用潜力.

结论:

  • ANNA16为16S GCN估计提供了一个高度准确的,基于序列的方法.
  • 深度学习方法为基于序列的GCN预测提供了新的见解.
  • ANNA16对推进定量微生物组研究和序列分析有影响.