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Related Concept Videos

Spontaneous and Induced Mutations01:30

Spontaneous and Induced Mutations

Spontaneous mutations arise infrequently during DNA replication due to errors in the process. A key factor behind these errors is tautomeric shifts in nitrogenous bases, where bases transition from keto to enol forms or amino to imino forms. This shift can alter base-pairing rules, leading to mutations. Additionally, reactive oxygen species (ROS) arising from aerobic metabolism can damage DNA, resulting in depurination (loss of a purine base) or depyrimidination (loss of a pyrimidine base).
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.
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Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
Mutations01:35

Mutations

Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
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Related Experiment Video

Updated: May 27, 2026

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

Correlated mutations via regularized multinomial regression.

Janardanan Sreekumar1, Cajo J F ter Braak, Roeland C H J van Ham

  • 1Central Tuber Crops Research Institute, Thiruvananthapuram-695017, Kerala, India.

BMC Bioinformatics
|November 16, 2011
PubMed
Summary
This summary is machine-generated.

We introduce Regularized Multinomial Regression for Correlated Mutations (RMRCM), a novel method to predict protein residue contacts from evolutionary signals in multiple sequence alignments. RMRCM effectively captures complex residue relationships beyond pairwise correlations, improving contact prediction accuracy.

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Last Updated: May 27, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Published on: August 3, 2018

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04:57

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Published on: October 23, 2020

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Protein multiple sequence alignments contain evolutionary signals beyond sequence conservation, including correlated amino acid variations.
  • These correlated variations indicate interdependent amino acid positions, useful for predicting residue contacts.
  • Existing methods often rely on pairwise correlations, potentially conflating direct and indirect dependencies.

Purpose of the Study:

  • To develop a novel method for detecting correlated mutations in protein multiple sequence alignments.
  • To predict residue-residue contacts by considering the network of relationships between protein residues.
  • To improve upon existing methods by moving beyond pairwise correlation analysis.

Main Methods:

  • Development of Regularized Multinomial Regression for Correlated Mutations (RMRCM).
  • RMRCM analyzes the network of residue relationships, not just pairwise correlations.
  • Regularization is employed to prevent overprediction and limit the number of predicted links.

Main Results:

  • RMRCM demonstrates good performance in predicting residue-residue contacts on simulated and biological datasets.
  • Validation with protein structure data shows improved accuracy compared to previous methods.
  • The method successfully predicted PDZ-peptide interactions, demonstrating its capability for predicting interactions.

Conclusions:

  • A novel method, RMRCM, utilizing regularized multinomial regression, has been presented for identifying correlated mutations.
  • RMRCM effectively predicts residue-residue contacts by considering the network structure of protein evolution.
  • The approach shows promise for predicting protein interactions beyond contact sites.