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Related Concept Videos

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

6.2K
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...
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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Gene Families01:57

Gene Families

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Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
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Gene Duplication and Divergence02:37

Gene Duplication and Divergence

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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...
6.8K
Genetic Variation01:25

Genetic Variation

1.7K
Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

Wei-Po Lee1, Yu-Ting Hsiao, Wei-Che Hwang

  • 1Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan. wplee@mail.nsysu.edu.tw.

BMC Systems Biology
|January 17, 2014
PubMed
Summary
This summary is machine-generated.

Automated gene network reconstruction is improved using parallel evolutionary algorithms and cloud computing. This approach reduces computation time and enhances the accuracy of inferring large gene networks.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Gene network reconstruction is crucial for understanding biological systems.
  • Experimental methods for gene interaction testing are time-consuming.
  • Automated reverse engineering offers a more efficient alternative.

Purpose of the Study:

  • To develop a practical framework for inferring large gene networks.
  • To address challenges of premature convergence and high computational cost in evolutionary algorithms.
  • To leverage parallel computing and cloud environments for faster and more accurate network inference.

Main Methods:

  • Developed and parallelized a hybrid Genetic Algorithm-Particle Swarm Optimization (GA-PSO) method.
  • Integrated the parallel method with the Hadoop MapReduce programming model.
  • Executed the framework in cloud computing environments for large-scale gene network inference.

Main Results:

  • The parallel approach successfully infers gene networks with desired behaviors.
  • Significant reduction in computation time compared to sequential methods.
  • Validated using yeast S. cerevisiae sub-networks via GeneNetWeaver.

Conclusions:

  • Parallel population-based algorithms outperform sequential methods for gene network inference.
  • Cloud computing environments accelerate the computation of these parallel algorithms.
  • The integrated approach yields high-quality solutions efficiently, promising for large network reconstruction.