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

Next-generation Sequencing03:00

Next-generation Sequencing

88.6K
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....
88.6K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

11.1K
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...
11.1K

You might also read

Related Articles

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

Sort by
Same author

Comparative Analysis of High-Torque-Density Permanent Magnet Motors Having Similar Slot and Pole Numbers for Humanoid Robot Applications.

Biomimetics (Basel, Switzerland)·2026
Same author

An Optimization Scheme Based on the Simulated Annealing Algorithm for In situ DNA Microarray Synthesis.

Combinatorial chemistry & high throughput screening·2026
Same author

DPCM-DP-EN: a lossless dynamic compress and encrypted encode method for DNA storage of medical images with high storage density.

Medical & biological engineering & computing·2025
Same author

DNA-CTMF: Reconstruct high quality image from lossy DNA storage via Pixel-Base codebook and median filter.

Synthetic and systems biotechnology·2025
Same author

Electro-switchable addressing system for achieving repetitive random data access.

Nucleic acids research·2025
Same author

Scaling the High-Yield Potential of Large-Scale DNA Data Storage with Cap-Free DNA Synthesis.

ACS synthetic biology·2025

Related Experiment Video

Updated: Jun 21, 2025

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

12.1K

High-Risk Sequence Prediction Model in DNA Storage: The LQSF Method.

Yitong Ma, Shuai Chen, Qi Xu

    IEEE Transactions on Nanobioscience
    |July 8, 2024
    PubMed
    Summary

    This study introduces the Low Quality Sequence Filter (LQSF), a deep learning method for active DNA sequence filtering. LQSF significantly improves DNA data storage by reducing errors and enhancing efficiency.

    More Related Videos

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
    14:06

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

    Published on: June 23, 2012

    15.2K
    Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen
    10:28

    Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen

    Published on: July 15, 2018

    9.5K

    Related Experiment Videos

    Last Updated: Jun 21, 2025

    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

    12.1K
    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
    14:06

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

    Published on: June 23, 2012

    15.2K
    Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen
    10:28

    Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen

    Published on: July 15, 2018

    9.5K

    Area of Science:

    • Bioinformatics
    • Data Science
    • Molecular Biology

    Background:

    • Traditional DNA storage uses passive filtering, leading to redundancy and errors.
    • Existing methods lack efficiency in error correction during DNA synthesis and sequencing.

    Purpose of the Study:

    • To introduce an active filtering method for DNA storage.
    • To develop a deep learning model for predicting and filtering low-quality DNA sequences.

    Main Methods:

    • Developed the Low Quality Sequence Filter (LQSF) using deep learning classification models.
    • Trained models on error-prone sequences for pre-sequencing filtration.
    • Validated model performance using ROC and PR curves, and Illumina sequencing data.

    Main Results:

    • LQSF models achieved AUC > 0.91 (ROC) and > 0.95 (PR) across datasets.
    • Specific models (Alexnet, VGG16, VGG19) reached perfect AUC of 1.0 on the Original dataset.
    • Validated strong correlation between model scores and sequence error-proneness.

    Conclusions:

    • LQSF enables active sequence filtering at the encoding stage of DNA storage.
    • This method significantly enhances efficiency and reduces errors in DNA data storage.
    • LQSF represents a major advancement for future DNA storage research and applications.