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Related Concept Videos

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...
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Shape-based alignment of genomic landscapes in multi-scale resolution.

Hiroki Ashida1, Kiyoshi Asai, Michiaki Hamada

  • 1Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8561, Japan. hzxx51@gmail.com

Nucleic Acids Research
|May 8, 2012
PubMed
Summary

Scientists developed a new method to compare large genomic datasets by converting them into symbols for sequence alignment. This approach revealed a genome-wide correlation between DNA replication timing and Alu insertion density in humans and mice.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Quantitative genomic data, such as histone modifications, can now be generated across entire genomes.
  • Comparing these large, complex genomic landscapes comprehensively remains a significant challenge.
  • Existing methods primarily focus on DNA sequence comparison, not landscape shape analysis.

Purpose of the Study:

  • To introduce a novel computational method for rapidly detecting local regions with high correlations between diverse genomic landscapes.
  • To address the challenge of analyzing large-scale genomic data by developing a scalable approach.
  • To enable multi-scale alignment of genomic landscapes based on their shape.

Main Methods:

  • Genomic landscape data converted into symbolic series for efficient sequence alignment.
  • Decomposition of landscape data into different frequency bands to analyze biological processes at multiple scales.
  • Application of the method to human and mouse genomic data, including histone modifications.

Main Results:

  • Successfully aligned genomic landscapes at multiple scales based on shape, a novel capability.
  • Identified a significant genome-wide correlation between DNA replication timing and Alu insertion density in both human and mouse genomes.
  • Demonstrated the method's utility and generality across various well-studied human genomic landscapes.

Conclusions:

  • The developed method provides a powerful new tool for comparative analysis of genomic landscapes.
  • The correlation between DNA replication timing and Alu density suggests underlying biological connections conserved across species.
  • This work opens new avenues for understanding genome organization and function through landscape shape analysis.