Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Labeling DNA Probes03:31

Labeling DNA Probes

9.6K
DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
Radioisotopes, fluorophores, or small molecule binding partners like biotin or digoxigenin, are the most widely used reporter tags for labeling DNA probes. These labels can be attached to the probe DNA molecule via...
9.6K
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

804
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...
804
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.1K
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...
7.1K
Next-generation Sequencing03:00

Next-generation Sequencing

100.0K
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....
100.0K
Sanger Sequencing01:57

Sanger Sequencing

776.6K
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...
776.6K
RNA-seq03:21

RNA-seq

12.3K
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...
12.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

How can biological databases support the new UN mechanism for benefit-sharing from digital sequence information?

Scientific data·2026
Same author

Transposon Ecology and the Octopus Genome.

BioEssays : news and reviews in molecular, cellular and developmental biology·2026
Same author

Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods.

Nature methods·2025
Same author

Metabarcoding arthropods in agroecosystems in Southern Ontario, Canada.

Biodiversity data journal·2025
Same author

Global arthropod beta-diversity is spatially and temporally structured by latitude.

Communications biology·2024
Same author

PROTAX-GPU: a scalable probabilistic taxonomic classification system for DNA barcodes.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2024

Related Experiment Video

Updated: Mar 8, 2026

Author Spotlight: Harnessing DNA Barcode Technology to Enhance the Efficiency of Medicinal Plant Identification
08:55

Author Spotlight: Harnessing DNA Barcode Technology to Enhance the Efficiency of Medicinal Plant Identification

Published on: November 1, 2024

2.6K

Machine Learned Replacement of N-Labels for Basecalled Sequences in DNA Barcoding.

Eddie Y T Ma, Sujeevan Ratnasingham, Stefan C Kremer

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 17, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a machine learning method to improve Sanger Sequencing accuracy by correcting N-labels in DNA sequences. The system enhances base identification, boosting sequence reliability and analysis correctness.

    More Related Videos

    2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications
    05:41

    2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

    Published on: July 10, 2020

    2.4K
    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

    4.6K

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    Author Spotlight: Harnessing DNA Barcode Technology to Enhance the Efficiency of Medicinal Plant Identification
    08:55

    Author Spotlight: Harnessing DNA Barcode Technology to Enhance the Efficiency of Medicinal Plant Identification

    Published on: November 1, 2024

    2.6K
    2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications
    05:41

    2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

    Published on: July 10, 2020

    2.4K
    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

    4.6K

    Area of Science:

    • Genomics
    • Bioinformatics
    • Machine Learning

    Background:

    • Sanger Sequencing is a foundational method for DNA sequencing.
    • Identifying all bases accurately is crucial for reliable genetic analysis.
    • N-labels in chromatograms represent uncalled bases, limiting sequence data utility.

    Purpose of the Study:

    • To develop a machine learning method to increase identified bases in Sanger Sequencing.
    • To improve the accuracy and reliability of DNA sequence data.
    • To enhance the utility of genetic markers for biodiversity studies.

    Main Methods:

    • A machine learning system post-processes KB basecalled chromatograms.
    • It identifies and corrects 'N-labels' using additional sequence reads and human-verified data.
    • Corrections are incorporated during system training, utilizing a large dataset from Barcode of Life Datasystems.

    Main Results:

    • The system recovers a significant percentage of N-labels across different genetic markers: 79% for COI (animal), 80% for matK and rbcL (plant), and 58% for non-protein-coding sequences.
    • The method maintains a low error rate, adhering to barcoding standards.
    • Corrections are selectively applied only when the system estimates a low error rate.

    Conclusions:

    • The developed machine learning approach effectively increases the number of identified bases in Sanger Sequencing data.
    • This improvement enhances reference sequence reliability, sequence identification accuracy, and overall analysis correctness.
    • The system demonstrates robust performance across diverse DNA barcode regions, supporting biodiversity research.