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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...
Gene Evolution - Fast or Slow?02:05

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.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

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.
In contrast, regions which code...
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
Evolution of Microbial Genome01:08

Evolution of Microbial Genome

Microbial genome evolution is a highly dynamic process shaped by continual gene gain and loss across species and strains. This genomic flexibility allows microorganisms to adapt rapidly to environmental pressures and interactions with other organisms. Central to understanding this diversity is the distinction between the core and pan genomes.The core genome comprises the genes shared by all sampled strains of a species, representing essential functions needed for fundamental cellular processes.
Gene Duplication and Divergence02:37

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 characterized.

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Updated: May 30, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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

Published on: December 7, 2021

Multi-granularity Parallel Computing in a Genome-Scale Molecular Evolution Application.

Jesse D Walters1, Thomas B Bair, Terry A Braun

  • 1Coordinated Laboratory for Computational Genomics, University of Iowa, Iowa City, IA 52242 USA.

The Journal of Supercomputing
|August 16, 2011
PubMed
Summary
This summary is machine-generated.

This study enhances computational methods for identifying horizontal gene transfers (HGTs) by employing multi-granularity parallelism. The improved XenoCluster 1.0 significantly speeds up phylogenetic tree clustering for evolutionary analysis.

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Last Updated: May 30, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Published on: December 7, 2021

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Horizontal gene transfers (HGTs) are rare but significant molecular evolutionary events.
  • Previous coarse-grained parallel approaches achieved speedups but required long execution times (e.g., 12 days).
  • Large-scale genomic data and compute clusters necessitate efficient computational methods.

Purpose of the Study:

  • To develop and evaluate a multiple granularity parallelism approach for HGT identification.
  • To optimize the tree-clustering phase of the XenoCluster 1.0 pipeline.
  • To assess the biological accuracy and relevance of the computational results.

Main Methods:

  • Implemented a multiple granularity parallelism strategy, integrating multi-core shared memory nodes.
  • Applied phylogenetic tree similarity as a distance metric for gene clustering.
  • Benchmarked performance using speedup efficiency on multi-core processors.
  • Validated results against known xenologs in yeast.

Main Results:

  • Achieved up to 80% speedup efficiency on 8 CPU cores.
  • Demonstrated computational feasibility for analyzing large gene clusters using phylogenetic similarity.
  • The enhanced method significantly reduces the computational time compared to serial solutions.

Conclusions:

  • The multiple granularity parallelism approach effectively accelerates HGT identification.
  • XenoCluster 1.0 with enhanced parallelism is suitable for large-scale genomic analysis.
  • The method provides biologically relevant insights into evolutionary events.