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Novel Sequence Discovery by Subtractive Genomics
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MACARON: a python framework to identify and re-annotate multi-base affected codons in whole genome/exome sequence

Waqasuddin Khan1,2, Ganapathi Varma Saripella1,2, Thomas Ludwig3

  • 1Sorbonne Universités, UPMC Université Paris 06, INSERM UMR_S 1166, Paris, France.

Bioinformatics (Oxford, England)
|May 5, 2018
PubMed
Summary
This summary is machine-generated.

The MACARON program accurately predicts amino acid changes from multiple variants within a single codon, improving genetic variant analysis. This tool is crucial for re-annotating and understanding complex genetic variations in human disease risk studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Predicting the deleteriousness of coding variants is vital for filtering next-generation sequencing data and identifying disease-risk candidates.
  • Current tools often use a base-to-base annotation, which can be biased when multiple variants occur within the same genetic codon.

Purpose of the Study:

  • To introduce MACARON, a novel program designed to identify, re-annotate, and predict amino acid changes resulting from multiple single nucleotide variants (SNVs) within a single codon.
  • To address the limitations of single SNV annotation in complex genetic scenarios.

Main Methods:

  • MACARON processes standard VCF files to detect multiple SNVs within codons.
  • The program re-annotates these variants and predicts the resulting amino acid substitutions.
  • Applied to a whole exome dataset of 573 individuals.

Main Results:

  • MACARON identified 114 instances where multiple SNVs in a codon led to a different amino acid change than predicted by standard single SNV annotation tools.
  • These multi-SNV events within codons are not rare and can impact variant interpretation.

Conclusions:

  • The MACARON program offers a more accurate approach to predicting amino acid changes from variants within codons.
  • Findings highlight the importance of considering multi-SNV events in genetic studies, particularly those with inconclusive results.
  • The tool can improve the analysis of next-generation sequencing data for human disease research.