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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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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....
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Sanger Sequencing01:57

Sanger Sequencing

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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...
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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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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
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

146
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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Related Experiment Video

Updated: Oct 12, 2025

Pattern-based Search of Epigenomic Data Using GeNemo
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Pattern-based Search of Epigenomic Data Using GeNemo

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HpGAN: Sequence Search With Generative Adversarial Networks.

Mingxing Zhang, Zhengchun Zhou, Lanping Li

    IEEE Transactions on Neural Networks and Learning Systems
    |November 23, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces HpGAN, a novel generative adversarial network (GAN) method for discovering complex engineering sequences. HpGAN effectively finds mutually orthogonal complementary sequence sets and optimal binary Z-complementary pairs for wireless communications and radar systems.

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    Novel Sequence Discovery by Subtractive Genomics
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    Novel Sequence Discovery by Subtractive Genomics
    09:40

    Novel Sequence Discovery by Subtractive Genomics

    Published on: January 25, 2019

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    Area of Science:

    • Engineering and Computer Science
    • Signal Processing
    • Artificial Intelligence

    Background:

    • Sequence searching is crucial for engineering but remains challenging.
    • Traditional methods struggle with complex, intractable problems.
    • Generative Adversarial Networks (GANs) offer potential for sequence generation.

    Purpose of the Study:

    • To propose HpGAN, a novel algorithmic method for searching desired sequences.
    • To leverage GANs with a Hopfield network encoder for discrete data generation.
    • To demonstrate HpGAN's capability in complex sequence discovery.

    Main Methods:

    • Utilizing a zero-sum game framework to train a generative model.
    • Designing a Hopfield network as an encoder within the GAN architecture.
    • Applying HpGAN to generate mutually orthogonal complementary sequence sets (MOCSSs) and optimal odd-length binary Z-complementary pairs (OB-ZCPs).
    • Evaluating HpGAN's performance in pulse compression radar systems.

    Main Results:

    • HpGAN successfully generated novel MOCSSs and OB-ZCPs not present in the training data.
    • The generated sequences found wide applications in wireless communications.
    • New sequences discovered by HpGAN significantly improved signal-to-interference ratio in radar systems, outperforming existing methods like AlphaSeq.

    Conclusions:

    • HpGAN provides an effective algorithmic approach for searching complex sequences.
    • The method overcomes limitations of traditional algebraic tools for intractable problems.
    • HpGAN demonstrates significant potential in advancing applications within wireless communications and radar systems.