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

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.

Optimizing Analytical Thresholds for Low-Template DNA Analysis: Insights from Multi-Laboratory Negative Controls

“…Without reference genotypes, distinguishing whether peaks above the adjusted AT represent alleles or non-allelic artifacts remains challenging. Forensic researchers have recognized and actively worked toward discerning non-allelic peaks or directly mitigating their impact, particularly with the incorporation of artificial intelligence [10,[20][21][22][23][24][25]. Nevertheless, the "black box" issue introduced by sophisticated artificial intelligence algorithms poses a challenge in legal contexts [26].…”

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

“…By comparing an individual's DNA profile to a massive DNA database, AI algorithms can quickly identify potential matches. This process, known as DNA fingerprinting, has been instrumental in solving cold cases and exonerating wrongfully convicted individuals [47,48,50,56].…”

Recent advances in forensic biology and forensic DNA typing: INTERPOL review 2019–2022

“…Electropherograms generated by CE instruments exhibit both STR alleles and artifacts that complicate data interpretation. Efforts are underway to understand and model instrumental artifacts [ [248] , [249] , [250] , [251] ] as well as biological artifacts of the PCR amplification process such as STR stutter products [ 252 , 253 ]. Machine learning approaches are being applied to classify artifacts versus alleles with the goal to eventually replace manual data interpretation with computer algorithms [ [254] , [255] , [256] , [257] ].…”