Highlights
- •A machine learning pipeline for analyzing UMI barcoded reads was created.
- •On low-template data, single source allele calling was improved by 13% (PR AUC).
- •Allele calling for a 100 pg balanced mixture was improved by 10%.
Abstract
PCR artifacts are an ever-present challenge in sequencing applications. These artifacts
can seriously limit the analysis and interpretation of low-template samples and mixtures,
especially with respect to a minor contributor. In medicine, molecular barcoding techniques
have been employed to decrease the impact of PCR error and to allow the examination
of low-abundance somatic variation. In principle, it should be possible to apply the
same techniques to the forensic analysis of mixtures. To that end, several short tandem
repeat loci were selected for targeted sequencing, and a bioinformatic pipeline for
analyzing the sequence data was developed. The pipeline notes the relevant unique
molecular identifiers (UMIs) attached to each read and, using machine learning, filters
the noise products out of the set of potential alleles. To evaluate this pipeline,
DNA from pairs of individuals were mixed at different ratios (1−1, 1−9) and sequenced
with different starting amounts of DNA (10, 1 and 0.1 ng). Naïvely using the information
in the molecular barcodes led to increased performance, with the machine learning
resulting in an additional benefit. In concrete terms, using the UMI data results
in less noise for a given amount of drop out. For instance, if thresholds are selected
that filter out a quarter of the true alleles, using read counts accepts 2381 noise
alleles and using raw UMI counts accepts 1726 noise alleles, while the machine learning
approach only accepts 307.
Keywords
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Article info
Publication history
Published online: November 22, 2022
Accepted:
November 18,
2022
Received in revised form:
October 20,
2022
Received:
June 14,
2022
Identification
Copyright
© 2022 Elsevier B.V. All rights reserved.
