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Related Concept Videos

Mutations01:39

Mutations

Overview
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.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...
Mutations01:39

Mutations

Overview
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...
Point and Frameshift Mutations01:30

Point and Frameshift Mutations

Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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).

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Performance of mutation pathogenicity prediction methods on missense variants.

Janita Thusberg1, Ayodeji Olatubosun, Mauno Vihinen

  • 1Institute of Biomedical Technology, F1-33014 University of Tampere, Finland.

Human Mutation
|March 18, 2011
PubMed
Summary

This study evaluates nine computational tools for predicting single nucleotide polymorphism (SNP) pathogenicity. SNPs&GO and MutPred demonstrated the best performance, offering valuable insights for genetic variation analysis.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Single nucleotide polymorphisms (SNPs) are common genetic variations.
  • Experimental determination of SNP pathogenicity is challenging and time-consuming.
  • Computational tools are needed to predict SNP effects on disease.

Purpose of the Study:

  • To evaluate the performance of nine popular SNP pathogenicity prediction tools.
  • To identify the most accurate computational methods for classifying SNPs.
  • To assess factors influencing prediction accuracy, such as amino acid changes and protein structure.

Main Methods:

  • Tested nine prediction methods: MutPred, nsSNPAnalyzer, Panther, PhD-SNP, PolyPhen, PolyPhen2, SIFT, SNAP, and SNPs&GO.
  • Used a dataset of over 40,000 pathogenic and neutral variants.
  • Analyzed the impact of amino acid properties and protein structural context on prediction accuracy.

Main Results:

  • Prediction tool performance varied significantly, from poor (MCC 0.19) to good (MCC 0.65).
  • SNPs&GO and MutPred achieved the highest accuracies (0.82 and 0.81, respectively).
  • Methodical correlations were generally poor, indicating diverse prediction strategies.

Conclusions:

  • SNPs&GO and MutPred are the most reliable tools for predicting SNP pathogenicity among those evaluated.
  • The choice of prediction method impacts accuracy.
  • Further development is needed to improve the consistency and accuracy of SNP pathogenicity prediction tools.