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Related Concept Videos

Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
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...
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...

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Related Experiment Video

Updated: May 20, 2026

Collection and Extraction of Saliva DNA for Next Generation Sequencing
06:58

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Published on: August 27, 2014

DCVBin: a novel binning method for single-sample metagenomes based on DNA language model and variational autoencoder.

Jingyuan Wang1,2, Yifan Liu3, Fu Liu3

  • 1School of Artificial Intelligence, Jilin University, Qianjin Street No. 3003, 130000, Changchun, Jilin, China.

Briefings in Bioinformatics
|May 19, 2026
PubMed
Summary

DCVBin enhances metagenomic analysis by using DNA language models for single-sample binning. This method accurately reconstructs genomes from individual samples and aids in disease diagnosis.

Keywords:
DNA language modelclusteringmetagenomic binningsemantic featuressingle-sample binningvariational autoencoder

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

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Last Updated: May 20, 2026

Collection and Extraction of Saliva DNA for Next Generation Sequencing
06:58

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Published on: August 27, 2014

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

  • Metagenomics
  • Bioinformatics
  • Genomics

Background:

  • Metagenomic DNA contigs binning is crucial for reconstructing genomes.
  • Existing methods struggle with single-sample binning due to limited coverage data.
  • This limitation hinders detailed metagenomic analysis at the individual sample level.

Purpose of the Study:

  • To develop a novel single-sample metagenomic contigs binning method.
  • To improve genome reconstruction accuracy in scenarios with limited coverage.
  • To integrate advanced language modeling with traditional binning techniques.

Main Methods:

  • Proposed DCVBin, a method incorporating semantic features from a DNA language model.
  • Utilized variational autoencoder to integrate DNA language model features with 4-mer frequencies.
  • Employed k-means clustering, with cluster number determined by single-copy genes.

Main Results:

  • DCVBin demonstrated high-accuracy single-sample metagenomic binning across six datasets.
  • Outperformed existing state-of-the-art methods in single-sample binning tasks.
  • A DCVBin-integrated framework accurately predicted colorectal cancer from gut metagenomes.

Conclusions:

  • DCVBin significantly advances single-sample metagenomic binning capabilities.
  • The method holds promise for disease diagnostics using metagenomic data.
  • Identified potential microbial biomarkers for colorectal cancer detection.