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Labeling DNA Probes03:31

Labeling DNA Probes

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...
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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.
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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.
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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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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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EvoOligo: oligonucleotide probe design with multiobjective evolutionary algorithms.

Soo-Yong Shin1, In-Hee Lee, Young-Min Cho

  • 1Medical Information Center, Seoul National University Hospital, Seoul, Korea. syshin@snuh.org

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|June 27, 2009
PubMed
Summary
This summary is machine-generated.

Designing oligonucleotide probes for deoxyribonucleic acid microarrays is crucial. Our multiobjective evolutionary optimization method, EvoOligo, yields superior probe sets compared to existing tools.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Oligonucleotide probe design is critical for successful deoxyribonucleic acid (DNA) microarray experiments.
  • Existing methods often rely on simple filtering with fixed thresholds, potentially limiting probe set quality.

Purpose of the Study:

  • To introduce a novel multiobjective evolutionary optimization method for designing oligonucleotide probes.
  • To develop a flexible and customizable approach that optimizes probe sets for specific genes and applications.

Main Methods:

  • A multiobjective evolutionary optimization algorithm was developed to design oligonucleotide probe sets.
  • The method optimizes probe combinations for a given set of genes, considering multiple criteria simultaneously.
  • The approach was implemented as a web-accessible platform named EvoOligo.

Main Results:

  • EvoOligo identified superior probe sets compared to traditional filtering methods.
  • The multiobjective approach allowed for easy incorporation of user-defined criteria.
  • Performance was validated by designing probe sets for Human Papillomavirus and Arabidopsis genes, outperforming OligoArray and OligoWiz.

Conclusions:

  • The proposed multiobjective evolutionary optimization method offers a more effective approach to oligonucleotide probe design.
  • EvoOligo provides a versatile platform for generating high-quality probe sets tailored to diverse applications.
  • This method enhances the reliability and efficiency of DNA microarray experiments.