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Related Concept Videos

Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
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...
Point and Frameshift Mutations01:30

Point and Frameshift Mutations

Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...

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Assessment of In-Frame Indel Variants in an Unsolved Cohort of Inherited Retinal Diseases Using Machine Learning.

David E Rauch1,2, Meng Wang1, Muhammad Jafar Hussain Hafiz1

  • 1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA, bcm.edu.

Human Mutation
|March 4, 2026
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Summary
This summary is machine-generated.

Machine learning tools can predict the pathogenicity of in-frame insertions and deletions (indels) in inherited retinal diseases (IRDs). MetaRNN-indel demonstrated superior performance, aiding in the identification of causal variants in IRD patients.

Keywords:
IRDin-frame indelin-silicomachine learningunsolved

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Area of Science:

  • Genetics
  • Bioinformatics
  • Ophthalmology

Background:

  • Predicting the pathogenicity of in-frame insertions and deletions (indels) is challenging and less standardized than for other genetic variations.
  • Inherited retinal diseases (IRDs) are a diverse group of genetic disorders affecting vision, often caused by various genetic mutations.

Purpose of the Study:

  • To systematically evaluate the performance of in silico machine learning (ML) tools for predicting the pathogenicity of in-frame indels in IRD patients.
  • To identify the most effective ML tool for this specific type of genetic variation.

Main Methods:

  • Four ML tools (CADD, FATHMM-indel, VEST4, and MetaRNN-indel) were compared using a patient cohort with IRDs.
  • The best-performing tool, MetaRNN-indel, was applied to a larger cohort of 1013 unsolved IRD patients.

Main Results:

  • MetaRNN-indel exhibited the best overall performance among the evaluated ML tools.
  • Application of MetaRNN-indel to 1013 IRD patients identified two likely pathogenic causal variants.
  • The identified variants were confirmed to correlate with the clinical phenotypes of the respective patients.

Conclusions:

  • Existing ML tools, particularly MetaRNN-indel, can reliably predict the pathogenicity of in-frame indels.
  • Proper evaluation and tuning of ML tools are crucial for accurate variant interpretation in genetic disease diagnosis.
  • This study advances the diagnostic capabilities for IRDs by improving the analysis of in-frame indel variants.