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Related Concept Videos

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.
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...
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...
Exon Recombination02:32

Exon Recombination

The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
Exon shuffling follows “splice frame rules.” Each exon has three reading...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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An evolution based biosensor receptor DNA sequence generation algorithm.

Eungyeong Kim1, Malrey Lee, Thomas M Gatton

  • 1Advanced Graduate Education Center for Electronics of Jeonbuk and Information Technology-BK21, Jeonju, Jeonbuk, 561-756, Korea. rotnrwk@kongju.ac.kr

Sensors (Basel, Switzerland)
|February 9, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel DNA computing algorithm to discover DNA sequences for biosensors. It enhances the stability and efficiency of DNA biosensors for detecting real-world substances.

Keywords:
DNA computingDNA sequenceTSP (Traveling Salesman Problem)biosensorevolution programming

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Area of Science:

  • Biotechnology
  • Molecular Biology
  • Computational Biology

Background:

  • Biosensors rely on bioreceptors for target substance detection.
  • Current DNA biosensor research often overlooks real-world sample complexities.
  • Developing suitable DNA recognition molecules is crucial for advancing biosensor technology.

Purpose of the Study:

  • To explore DNA characteristics as bioreceptors.
  • To propose a hybrid evolution-based DNA sequence generation algorithm.
  • To identify DNA recognition molecules for stable hybridization with real target substances.

Main Methods:

  • Utilizing DNA computing principles for sequence generation.
  • Applying a hybrid evolution-based algorithm.
  • Employing the Traveling Salesman Problem (TSP) approach for sequence evaluation.

Main Results:

  • The algorithm generates stable and efficient DNA sequences for biosensor applications.
  • The TSP approach enhances the evaluation of DNA sequence safety and fitness.
  • The method supports the generation of variable-length DNA sequences tailored to specific receptor needs.

Conclusions:

  • The developed algorithm offers a robust method for identifying effective DNA bioreceptors.
  • This approach addresses limitations in current DNA biosensor research concerning real samples.
  • The findings pave the way for more stable and versatile DNA biosensor development.