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Related Concept Videos

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,...
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Related Experiment Video

Updated: Jun 25, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Stochastic models of sequence evolution including insertion-deletion events.

István Miklós1, Adám Novák, Rahul Satija

  • 1Bioinformatics Group, Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences, 1053 Budapest, Reáltanoda u. 13-15, Hungary. miklosi@renyi.hu

Statistical Methods in Medical Research
|February 18, 2009
PubMed
Summary

Statistical alignment methods compare DNA sequences using evolutionary models. These advanced techniques aid in understanding sequence evolution and function, despite computational challenges.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Last Updated: Jun 25, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Sequence alignment is crucial for analyzing evolutionary relationships and genomic data.
  • Statistical methods offer advantages for hypothesis testing and parameter estimation over optimality criteria.
  • Current computational limits restrict analysis to a small number of sequences.

Purpose of the Study:

  • To review the advancements and challenges in statistical sequence alignment.
  • To highlight the integration of statistical alignment with sequence annotation tasks.
  • To discuss the potential of these methods for large-scale genomic data analysis.

Main Methods:

  • Utilizing explicit stochastic models for sequence evolution, including substitutions, insertions, and deletions (indels).
  • Employing dynamic programming and Markov Chain Monte Carlo (MCMC) techniques for alignment.
  • Integrating alignment with functional annotation, such as gene finding and protein structure prediction.

Main Results:

  • Statistical alignment provides a robust framework for evolutionary inference.
  • MCMC methods extend alignment capabilities to 10-15 sequences.
  • Successful integration with gene finding, protein secondary structure prediction, and regulatory signal detection.

Conclusions:

  • Statistical alignment is a powerful, though computationally intensive, approach.
  • Significant progress has been made in combining alignment with functional annotation.
  • Further development is needed to address the analysis of large sequence datasets.