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Related Concept Videos

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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Genetic Variation01:25

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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.
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Related Experiment Video

Updated: Jul 5, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

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Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures.

Dylan Lebatteux1, Hugo Soudeyns2,3,4, Isabelle Boucoiran5

  • 1Department of Computer Science, Université du Québec à Montréal, Montréal, Québec, Canada.

Plos One
|January 19, 2024
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Summary

KEVOLVE, a machine learning tool, effectively identifies genomic signatures in SARS-CoV-2 variants. These signatures, often biologically relevant, aid in variant classification and characterization without needing sequence alignments.

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Last Updated: Jul 5, 2025

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Genomic signatures are key for distinguishing viral species and variants.
  • Identifying these signatures aids SARS-CoV-2 research, including variant recognition and functional characterization.

Purpose of the Study:

  • To assess KEVOLVE, a machine learning approach, for identifying genomic signatures in SARS-CoV-2.
  • To compare KEVOLVE's effectiveness against existing statistical tools.

Main Methods:

  • KEVOLVE utilizes a genetic algorithm with a machine learning kernel to find minimal k-mer sets defining genomic signatures.
  • KANALYZER, an extension of KEVOLVE, characterizes signature variations across different variant classes.

Main Results:

  • KEVOLVE outperformed gold-standard statistical tools in identifying variant-discriminative signatures in a large SARS-CoV-2 dataset.
  • Identified signatures frequently correlated with known mutations and their functional/pathological impacts.

Conclusions:

  • KEVOLVE is a robust machine learning method for identifying biologically relevant genomic signatures in SARS-CoV-2 variants.
  • The approach bypasses the need for multiple sequence alignments, offering a significant advantage.