Automated detection and removal of capillary electrophoresis artifacts due to spectral overlap

Jonathan D. Adelman, Angie Zhao, D. Spencer Eberst, Michael Marciano

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

While DNA detection using capillary electrophoresis has enabled improvements in both resolution and throughput, the use of CE – particularly with multiple dye channels – can introduce artifacts that can complicate analyses. Undetected pull-up artifacts can pose a challenge to investigators, especially in low-level samples, while partial pull-up peaks can distort peak height balance within a locus and impact the downstream likelihood ratio. Current methods for addressing pull-up are typically manually implemented. This study presents an effective alternative: a series of mathematical models, created using symbolic regression achieved through genetic programming. The models estimate the amount of pull-up expected in a peak from a true allele for a given dye-dye relationship and instrument type. This leads to the removal of artifactual pull-up peaks and peak height corrections when pull-up is present within true alleles. When models are used in conjunction with a dynamic threshold, pull-up peaks were automatically detected and removed with an accuracy rate of 96.1%. The removal of partial pull-up from true allele peaks led to a more accurate heterozygote balance for the affected locus. These models have been optimized for use with any analytical threshold and can be implemented by any lab using a 3100 or 3500 instrument series.

Original languageEnglish (US)
Pages (from-to)1753-1761
Number of pages9
JournalElectrophoresis
Volume40
Issue number14
DOIs
StatePublished - Jul 1 2019

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Keywords

  • Artificial intelligence
  • Forensic
  • Genetic programming
  • Pull-up
  • Spectral overlap

ASJC Scopus subject areas

  • Biochemistry
  • Clinical Biochemistry

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