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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...

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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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Gene Evolution - Fast or Slow?

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.
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Gene Duplication and Divergence

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Gene Conversion

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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...

Gene Conversion

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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...

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Related Experiment Video

Updated: May 8, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Highly efficient genotype compression leveraging genealogical relatedness.

Amber Shen¹, Xinran Wang², Luke J O'Connor^2,3,4

¹Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Biorxiv : the Preprint Server for Biology

|May 7, 2026

Summary

This summary is machine-generated.

A new lossless compression algorithm, kodama, creates a linear ancestral recombination graph (ARG) from genetic data. This significantly reduces file size and accelerates large-scale genetic analyses like genome-wide association studies.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Large genetic datasets (terabytes) pose significant computational challenges for analysis.
Scaling sequencing efforts exacerbates storage and processing demands.
Existing methods struggle with the efficiency required for massive genomic datasets.

Purpose of the Study:

Introduce a novel lossless compression algorithm, kodama, for large genetic datasets.
Develop a new data structure, the linear ancestral recombination graph (ARG), for efficient statistical analysis.
Enable scalable genetic analyses on datasets with millions of individuals.

Main Methods:

Developed kodama, a lossless compression algorithm utilizing genealogical relatedness.
Inferred a linear ARG data structure from whole genome sequencing data.
Applied kodama to UK Biobank and All of Us datasets.

Main Results:

Linear ARGs achieved 17-89x reduction in data size compared to input.
The UK Biobank (200k samples) dataset loaded into memory (58GB).
Linear ARG is 2.5x smaller than genotype representation graph (GRG).
Genome-wide association study (GWAS) completed in 100 seconds (4,700x speedup over PLINK 2.0).

Conclusions:

Kodama and the linear ARG offer a highly efficient solution for compressing and analyzing large genetic datasets.
The method significantly accelerates computationally intensive tasks like GWAS.
This approach is expected to facilitate genetic analyses at unprecedented scales, potentially including millions of samples.