Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

15.0K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.0K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.3K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
13.3K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
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...
5.7K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

123
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
123
Viral Mutations00:36

Viral Mutations

32.3K
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...
32.3K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.1K
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...
7.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Predicting allosteric sites using fast conformational sampling as guided by coarse-grained normal modes.

The Journal of chemical physics·2023
Same author

Predicting lipid and ligand binding sites in TRPV1 channel by molecular dynamics simulation and machine learning.

Proteins·2021
Same author

Cross-subunit interactions that stabilize open states mediate gating in NMDA receptors.

Proceedings of the National Academy of Sciences of the United States of America·2021
Same author

Predicting cryptic ligand binding sites based on normal modes guided conformational sampling.

Proteins·2020
Same author

Investigating dual Ca<sup>2+</sup> modulation of the ryanodine receptor 1 by molecular dynamics simulation.

Proteins·2020
Same author

Heat activation mechanism of TRPV1: New insights from molecular dynamics simulation.

Temperature (Austin, Tex.)·2019

Related Experiment Video

Updated: Jun 26, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

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

Published on: January 16, 2019

11.0K

Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network

Wenjun Zheng1

  • 1Department of Physics, State University of New York at Buffalo, Buffalo, NY, United States of America.

Plos One
|May 14, 2024
PubMed
Summary
This summary is machine-generated.

Predicting disease-causing protein mutations is crucial for personalized medicine. This study introduces a novel sequence-based method using protein residue contact networks and machine learning to accurately identify mutation hotspots.

More Related Videos

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
00:06

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

13.6K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

13.0K

Related Experiment Videos

Last Updated: Jun 26, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

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

Published on: January 16, 2019

11.0K
In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
00:06

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

13.6K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

13.0K

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Accurate prediction of disease-causing mutations is essential for personalized medicine but remains challenging.
  • Existing computational methods often rely on protein structures or neglect residue interactions, limiting their predictive power.
  • There is a need for high-throughput, sequence-based approaches to identify deleterious protein variants.

Purpose of the Study:

  • To develop a sequence-based workflow for predicting disease-causing mutation hotspots in proteins.
  • To leverage protein residue contact networks and machine learning for enhanced variant site prediction.
  • To improve the accuracy and throughput of identifying critical residues for disease mutations.

Main Methods:

  • Integrated multiple deep learning-based coevolution analysis tools (RaptorX, DeepMetaPSICOV, SPOT-Contact) to construct protein residue networks.
  • Employed machine learning algorithms (Random Forest, Gradient Boosting, Extreme Gradient Boosting) to combine network centrality scores.
  • Utilized a dataset of 107 proteins with known disease mutations for rigorous evaluation.

Main Results:

  • Demonstrated the effectiveness of combining network scores using machine learning to predict mutation hotspots.
  • Showcased the utility of sequence-based protein residue contact networks in identifying disease-relevant residues.
  • Individual and collective evaluations confirmed the predictive power of network centrality scores.

Conclusions:

  • A promising strategy involves combining ensemble network scores via machine learning for accurate hotspot prediction.
  • This sequence-based approach overcomes limitations of structure-dependent methods and independent residue analysis.
  • The findings will facilitate disease diagnosis and the design of targeted therapies.