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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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...

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Related Experiment Video

Updated: May 9, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Network-based interpretation of genomic variation data.

Bjarni V Halldórsson1, Roded Sharan

  • 1School of Science and Engineering, Reykjavík University, 101 Reykjavík, Iceland.

Journal of Molecular Biology
|July 27, 2013
PubMed
Summary
This summary is machine-generated.

Integrating genomic data with protein networks aids in understanding complex diseases. This approach tackles statistical challenges in genome-wide association studies for better disease insights.

Keywords:
CNVGWASSNPcopy number variationdisease associationeQTLsexpression quantitative trait locigenome-wide association studiesgraph algorithmmolecular pathwayprotein–protein interactionsingle-nucleotide polymorphism

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are expanding due to technological advances.
  • Interpreting GWAS data presents statistical and combinatorial challenges due to complex diseases and vast genomic marker spaces.
  • Human diseases are often multi-factorial, requiring sophisticated analytical approaches.

Purpose of the Study:

  • To review techniques for integrating genomic variation data with protein-protein interaction (PPI) network information.
  • To enhance the understanding of complex human diseases by leveraging network data.
  • To reveal meaningful genetic associations by combining genomic and network data.

Main Methods:

  • Surveying existing methodologies for integrating genomic variation data with PPI networks.
  • Focusing on strategies that use network proximity to filter or prioritize genetic markers.
  • Discussing computational approaches for analyzing combined genomic and network datasets.

Main Results:

  • Protein-protein interaction networks can help manage the scale and complexity of GWAS data.
  • Network information can refine the focus to relevant genomic markers or combinations of markers.
  • Integration strategies offer a promising avenue for dissecting the genetic architecture of complex diseases.

Conclusions:

  • Combining genomic variation data with PPI network information is a powerful strategy for complex disease research.
  • This integrated approach can overcome key interpretation challenges in large-scale genetic studies.
  • Future research should focus on developing and refining these integrative techniques for enhanced biological discovery.