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Related Concept Videos

Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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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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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Informatics for unveiling hidden genome signatures.

Takashi Abe1, Shigehiko Kanaya, Makoto Kinouchi

  • 1Division of Evolutionary Genetics, Department of Population Genetics, National Institute of Genetics, The Graduate University for Advanced Studies, Mishima, Shizuoka-ken 411-8540, Japan.

Genome Research
|April 3, 2003
PubMed
Summary

A self-organizing map (SOM) analyzes oligonucleotide frequencies in genomes, efficiently revealing species-specific characteristics. This unsupervised neural network provides a powerful bioinformatics strategy for genomic data analysis.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • The rapid growth of available genome sequences necessitates advanced analytical tools.
  • Comprehensive analysis of species-specific sequence characteristics is crucial for understanding genomic diversity.

Purpose of the Study:

  • To evaluate the efficacy of self-organizing maps (SOMs) for analyzing oligonucleotide frequencies in diverse prokaryotic and eukaryotic genomes.
  • To demonstrate SOMs as a tool for identifying species-specific genomic signatures.

Main Methods:

  • Utilized an unsupervised neural network algorithm, the self-organizing map (SOM).
  • Analyzed di-, tri-, and tetranucleotide frequencies in 1-kb and 10-kb genomic sequences from 65 bacteria and 6 eukaryotes.
  • Applied SOMs to cluster complex genomic data and identify characteristic oligonucleotide combinations.

Main Results:

  • SOMs effectively separated sequences based on species, revealing distinct oligonucleotide frequency patterns.
  • The algorithm recognized species-specific characteristics within 10-kb sequences, acting as genomic signature features.
  • DNA sequences were successfully classified into biologically relevant subgroups within and between species.

Conclusions:

  • Self-organizing maps are an efficient and fundamental bioinformatics strategy for extracting genomic information.
  • SOMs demonstrate high classification power for analyzing global genome characteristics and identifying species-specific features.
  • This approach facilitates a wide range of genomic analyses from large sequence datasets.