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Evolutionary Relationships through Genome Comparisons

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...

Gene Conversion

Gene Conversion

Gene Conversion

Gene Conversion

Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...

Complementary DNA

Complementary DNA

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Complementary DNA

Gene Duplication and Divergence

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...

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Related Experiment Video

Updated: Apr 7, 2026

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

ERGC: an efficient referential genome compression algorithm.

Subrata Saha¹, Sanguthevar Rajasekaran¹

¹Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.

Bioinformatics (Oxford, England)

|July 4, 2015

Summary

This summary is machine-generated.

New reference-based genome compression algorithms offer improved compression ratios for biological sequencing data. This advancement addresses the growing challenge of managing large genomic datasets, making research and medical applications more feasible.

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Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Published on: May 9, 2011

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

The rapid increase in genome sequencing data presents significant storage, processing, and analysis challenges.
Standard data compression algorithms are inefficient for biological data as they do not leverage inherent genomic properties.
Specialized compression algorithms are needed to exploit statistical and information-theoretic properties of genomic sequences.

Purpose of the Study:

To address the bottleneck in managing large-scale genomic data.
To develop efficient data compression techniques for biological sequencing data.
To propose a novel algorithm for reference-based genome compression.

Main Methods:

Development of a novel algorithm for reference-based genome compression.
Extensive experimental evaluation using five real-world sequencing datasets.
Comparison of the proposed algorithm against existing state-of-the-art methods.

Main Results:

The proposed algorithm demonstrates competitive performance against the best known algorithms for reference-based genome compression.
Achieved superior compression ratios compared to current leading algorithms.
Showcased promising compression and decompression speeds for whole genomes.

Conclusions:

The novel reference-based genome compression algorithm effectively reduces the size of genomic data.
The algorithm offers a viable solution to the data management challenges in genomics.
Improved compression efficiency supports advancements in research and future medical applications.