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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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%...
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Pharmacogenomics: Identification of New Drug Targets01:29

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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Single Nucleotide Polymorphisms-SNPs01:05

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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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Principles of Pharmacogenetics: Types of Genetic Variants01:27

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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Related Experiment Video

Updated: Mar 18, 2026

Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants
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Context and Applications of Targeted Genetic Testing, with Emphasis on Copy Number Variants.

J W Ahn1, C Ogilvie1

  • 1Genetics Centre, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom.

Advances in Clinical Chemistry
|June 28, 2016
PubMed
Summary
This summary is machine-generated.

Recent advances in human genome variation detection outpace our understanding of genetic diseases. Software tools can optimize clinical genetic diagnostic testing for efficiency and cost-effectiveness, maximizing patient benefit.

Keywords:
Array CGHCNVNGSSoftware filteringSoftware targetingTargeted testingVariant

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Area of Science:

  • Genomics and Bioinformatics
  • Clinical Genetics
  • Medical Diagnostics

Background:

  • Rapid technological advancements enable high-throughput detection of human genome variations.
  • Clinical diagnostic laboratories are integrating these technologies for patient benefit.
  • Understanding the clinical significance of detected genetic variations remains a significant challenge.

Purpose of the Study:

  • To describe the application of software tools for targeted clinical genetic diagnostic testing.
  • To enhance the efficiency and cost-effectiveness of genetic testing.
  • To maximize the clinical utility of genomic information in patient care.

Main Methods:

  • Utilizing advanced software solutions to analyze and interpret genomic variation data.
  • Strategically targeting genetic diagnostic tests based on clinical presentation and genetic knowledge.
  • Integrating computational approaches to bridge the gap between variant detection and clinical interpretation.

Main Results:

  • Software tools facilitate a more focused approach to genetic diagnostics.
  • Improved efficiency and cost-effectiveness in identifying disease-causing genetic variations.
  • Enhanced ability to translate genomic findings into actionable clinical insights.

Conclusions:

  • Software-driven strategies are crucial for navigating the complexities of clinical genomics.
  • Optimized genetic testing improves diagnostic yield and patient outcomes.
  • Bridging the gap between genomic technology and genetic disease understanding is essential for modern medicine.