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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genome-wide Association Studies-GWAS01:11

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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...
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Sanger Sequencing01:57

Sanger Sequencing

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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Genetic Variation01:25

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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.
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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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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.
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AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification.

Jung Kim1, Ammar S Naqvi2,3, Ryan J Corbett2,3

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD.

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|December 11, 2023
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Automated Germline Variant Pathogenicity (AutoGVP) classifies germline variants using updated ACMG-AMP guidelines. This tool aids large-scale, clinical variant classification in research settings.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Medical Genetics

Background:

  • Increasing rates of exome and whole genome sequencing necessitate efficient germline variant classification.
  • Accurate classification of germline variants is crucial for clinical research and genetic diagnostics.
  • Existing tools may not fully incorporate the latest American College of Medical Genetics - Association for Molecular Pathology (ACMG-AMP) criteria.

Approach:

  • Developed Automated Germline Variant Pathogenicity (AutoGVP), a novel computational tool.
  • Integrated germline variant pathogenicity annotations from ClinVar.
  • Incorporated sequence variant classifications from a modified InterVar algorithm with adjusted PVS1 strengths and removed PP5/BP6 criteria.

Key Points:

  • AutoGVP facilitates large-scale classification of germline sequencing variants.
  • The tool utilizes updated ACMG-AMP criteria for enhanced accuracy.
  • It combines data from ClinVar and a modified InterVar for comprehensive analysis.

Conclusions:

  • AutoGVP provides a robust solution for clinically-focused germline variant classification in research.
  • This tool supports the interpretation of genetic variations identified through large-scale sequencing projects.
  • AutoGVP aims to improve the efficiency and reliability of germline variant pathogenicity assessment.