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Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

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Related Experiment Video

Updated: Jul 16, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

AAVC: an automated framework for high-accuracy ACMG-based variant classification.

R Arda İnan1, Barış Kayaalp1, Fatimah Safieh1

  • 1Department of Molecular Biology and Genetics, Faculty of Science, Bilkent University, 06800 Ankara, Türkiye.

Genetics in Medicine : Official Journal of the American College of Medical Genetics
|July 15, 2026
PubMed
Summary

A new tool, the Automated ACMG-based Variant Classifier (AAVC), accurately classifies human DNA sequence variants. It improves upon existing methods, reclassifying many variants of uncertain significance for better diagnostic and research applications.

Keywords:
AAVCACMGClinGenclinical genomicsgenetic diagnosisvariantvariant classification

Related Experiment Videos

Last Updated: Jul 16, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Area of Science:

  • Genomics
  • Bioinformatics
  • Medical Genetics

Background:

  • Accurate classification of DNA sequence variants is crucial for clinical diagnostics and research.
  • Existing automated tools often lack robust and up-to-date methodologies for variant classification according to ACMG standards.
  • There is a need for advanced computational tools to interpret human germline sequence diversity.

Purpose of the Study:

  • To develop and evaluate a novel automated tool, the Automated ACMG-based Variant Classifier (AAVC).
  • To assess the performance of AAVC for both diagnostic and research purposes in classifying DNA sequence variants.
  • To provide a more accurate and up-to-date platform for sequence variant interpretation.

Main Methods:

  • The Automated ACMG-based Variant Classifier (AAVC) was developed to computationally analyze sequence variants.
  • AAVC utilizes the American College of Medical Genetics and Genomics (ACMG) guidelines, ClinGen specifications, and a novel framework.
  • The tool integrates large public databases and in silico prediction tools for variant analysis.

Main Results:

  • AAVC achieved high concordance (94.39%) with FDA-recognized variant classifications, surpassing current tools.
  • The tool successfully reclassified 55% of variants of uncertain significance (VUS) in ClinVar into clinically significant categories.
  • Novel pathogenic, likely pathogenic, or VUS-high variants were identified in the Turkish Variome, with 1 in 10 individuals carrying actionable genotypes.

Conclusions:

  • AAVC provides a robust framework for the accurate classification of human germline sequence diversity.
  • The tool offers a highly accurate, rapid, and up-to-date platform for automated sequence variant interpretation.
  • AAVC is a valuable resource for clinical laboratories and research groups engaged in genetic variant analysis.