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Related Concept Videos

RNA-seq03:21

RNA-seq

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 microarray-based...
Nucleic Acid Structure01:25

Nucleic Acid Structure

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
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Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Nucleic acids02:43

Nucleic acids

Nucleic acids are the most important macromolecules for the continuity of life. They carry the cell's genetic blueprint and carry instructions for its functioning.
DNA and RNA
The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA is the genetic material in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is in the nucleus of eukaryotes and in the organelles, chloroplasts, and mitochondria. In prokaryotes, the...

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Updated: Jul 2, 2026

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

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Jiayu Yang1,2,3, Yulin Huang1,2,3, Zhuolun Li1,2

  • 1School of Public Health, Xiamen University, No. 4221 Xiang'an South Road, Xiang'an District, Xiamen, Fujian 361102, China.

Briefings in Bioinformatics
|June 30, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel model to accurately assess nucleic acid testing results. The fluorescence value-target sequence replication rate model offers personalized and universal evaluations for infectious disease detection.

Keywords:
BiGRU-BWIdeep learningenergy migrationnucleic acid testingquantitative framework

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Area of Science:

  • Biotechnology
  • Molecular Diagnostics
  • Infectious Disease Surveillance

Background:

  • Nucleic acid testing is crucial for disease screening, genetic analysis, and prenatal diagnosis.
  • Current methods rely on optical detection and fluorescence quantitative polymerase chain reaction (PCR) for target amplification.
  • Assessing fluorescence values presents challenges due to variations in sample types, reagents, and detection systems.

Purpose of the Study:

  • To develop a universal and personalized model for nucleic acid testing result assessment.
  • To establish a relationship between fluorescence values and target sequence replication rates.
  • To improve the accuracy and generality of nucleic acid detection across different scenarios.

Main Methods:

  • Development of a fluorescence value-target sequence replication rate relationship model.
  • Utilizing fluorescence quantitative polymerase chain reaction (PCR) for gene amplification.
  • Characterizing the relationship between optical signals and biochemical reactions in nucleic acid detection.

Main Results:

  • The proposed model accurately assesses nucleic acid testing results based on fluorescence values.
  • The model demonstrates both personalized and universal applicability for different testing requirements.
  • It addresses challenges in large-scale screening and diverse detection scenarios.

Conclusions:

  • A novel fluorescence value-target sequence replication rate model enhances nucleic acid testing accuracy.
  • The model provides a versatile solution for personalized and universal assessment in diagnostics.
  • This approach is vital for reliable infectious disease detection and control.