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Introducing COCOS: codon consequence scanner for annotating reading frame changes induced by stop-lost and frame

Mariusz Butkiewicz1, Jonathan L Haines1, William S Bush1

  • 1Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.

Bioinformatics (Oxford, England)
|January 27, 2017
PubMed
Abstract

Insights

Codon Consequence Scanner (COCOS) annotates genetic variants that alter reading frames, revealing impacts on protein sequences. This tool captures amino acid changes from stop-lost variants and insertions/deletions (InDels), aiding disease phenotype research.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Genomic variants altering reading frames can cause disease by affecting gene expression and protein structure.
  • Current annotation methods focus on DNA sequence changes, neglecting downstream transcript and protein alterations.

Purpose of the Study:

  • Introduce Codon Consequence Scanner (COCOS), a novel genetic annotation approach.
  • To capture and report the effects of reading frame-altering variants on protein sequences.

Main Methods:

  • COCOS is implemented as an Ensembl variant effect predictor (VEP) plugin.
  • It identifies and analyzes amino acid sequence alterations from stop-lost variants and small insertions/deletions (InDels).
  • The tool was applied to data from the 1000 Genomes Project.

Main Results:

  • Stop-lost variants altered transcripts by a median of 15 amino acids.
  • Small insertions/deletions (InDels) had a more significant impact, incorporating a median of 66 amino acids.
  • COCOS outputs captured sequence alterations in FASTA format for further analysis.

Conclusions:

  • COCOS addresses the limitation of current annotation approaches by reporting protein-level consequences of reading frame variants.
  • The tool provides crucial insights into how genetic variants impact protein structure and potentially induce disease phenotypes.
  • COCOS facilitates deeper analysis of variant effects on protein sequences, enhancing genetic research.

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