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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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What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.While some alleles of a given gene might be observed commonly, other variants...
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Genetic Variation

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, which...
Genetic Screens02:46

Genetic Screens

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Forward genetic screens
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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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

Published on: June 21, 2018

Core Hunter: an algorithm for sampling genetic resources based on multiple genetic measures.

Chris Thachuk1, José Crossa, Jorge Franco

  • 1Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T1Z4, Canada. cthachuk@cs.ubc.ca

BMC Bioinformatics
|August 8, 2009
PubMed
Summary
This summary is machine-generated.

Core Hunter is a new algorithm that selects diverse plant genetic resources for core subsets. It achieves better genetic diversity and distance than existing methods, creating smaller, representative collections.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Current methods for core subset selection focus on either allele representativeness or allele richness.
  • A need exists for a flexible algorithm balancing breeder's and taxonomist's preferences.

Purpose of the Study:

  • To develop a powerful and flexible algorithm for selecting core subsets with high genetic diversity and/or genetic distance.
  • To optimize multiple genetic measures simultaneously based on user-defined preferences.

Main Methods:

  • Development of Core Hunter, an advanced stochastic local search algorithm.
  • Evaluation of Core Hunter against state-of-the-art algorithms using various genetic distance and diversity measures.

Main Results:

  • Core Hunter identifies core subsets with superior genetic diversity and average genetic distance compared to existing methods.
  • The algorithm can optimize multiple genetic measures concurrently.
  • Core Hunter selects significantly smaller core subsets that retain all unique alleles from a reference collection.

Conclusions:

  • Core Hunter is an effective and flexible tool for genetic resource sampling and core subset establishment.
  • The software, documentation, and source code are publicly available.