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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...
Microbial Phylogeny01:28

Microbial Phylogeny

Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
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...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...
Mismatch Repair01:20

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

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Related Experiment Video

Updated: Jun 17, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Reducing phylogenetic bias in correlated mutation analysis.

Haim Ashkenazy1, Yossef Kliger

  • 1Compugen Ltd, 72 Pinchas Rosen, Tel Aviv 69512, Israel.

Protein Engineering, Design & Selection : PEDS
|January 14, 2010
PubMed
Summary

Correlated mutation analysis (CMA) improves protein contact map prediction by reducing noise in sequence alignments. A refined Pearson

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein science

Background:

  • Correlated mutation analysis (CMA) predicts protein contacts from sequence data.
  • CMA relies on correlations between mutations in interacting amino acid residues.
  • Estimating these correlations often involves Pearson's correlation coefficient (PCC) or mutual information (MI) from multiple sequence alignments (MSAs).

Purpose of the Study:

  • To investigate if noise reduction techniques, successful for MI-based predictors, can enhance PCC-based CMA.
  • To compare the performance of improved PCC-based CMA against MI-based methods.

Main Methods:

  • Applied a noise reduction procedure to a PCC-based correlated mutation analysis method.
  • Evaluated performance across four major SCOP protein classes.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Related Experiment Videos

Last Updated: Jun 17, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Published on: August 14, 2018

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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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10:36

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  • Compared the improved PCC method with existing MI-based predictors using MSAs of varying sizes.

    • Main Results:

    • The noise reduction approach significantly improved PCC-based CMA performance across all tested SCOP classes.
    • The enhanced PCC-based method demonstrated superior performance compared to MI-based methods for proteins with up to 100 sequences in their MSAs.

    Conclusions:

    • Noise reduction is a viable strategy to improve PCC-based correlated mutation analysis for protein contact map prediction.
    • The refined PCC-based method offers a more effective approach than MI-based methods for proteins with limited homologous sequences.