Highlights
- •Automation of library preparation using ForenSeq™ DNA Signature Preparation kit.
- •Assessment of concordance, repeatability, reproducibility and sensitivity.
- •Pooling volume adjustment to allow combined analysis of rich and poor DNA samples.
- •Final library pool quantification to ensure optimal sequencing quality metrics.
Abstract
Massively parallel sequencing (MPS) applications in forensic science highlight the
advantages of this technique compared to capillary electrophoresis (CE). The multiplexing
of a wide range of genetic markers and access to the full amplicon sequence, allowing
the detection of isoalleles, make it a very promising tool which could be applied
to the most challenging casework DNA samples. However, the complexity of the manual
library preparation protocol, potential DNA contamination and sample tracking issues
are the main reasons why forensic scientists still hesitate to implement MPS analytical
workflows in their laboratory. Here, we present the automation of all library preparation
steps for up to 96 samples using the Verogen’s ForenSeq™ DNA Signature Preparation kit. This automated protocol, developed on a Hamilton ID
STARlet robotic platform, is designed to allow the combined sequencing of rich and
poor DNA samples thanks to a final step which adjusts normalized library pooling volume
to guarantee a uniform depth of coverage across all samples. Our study includes tests
of concordance, repeatability, reproducibility and sensitivity (1000 pg, 700 pg, 500 pg,
250 pg, 100 pg and 50 pg). Sequencing results obtained with the automated protocol
were found to be concordant with previous validation studies using the manual protocol
in terms of depth of coverage and allele coverage ratio. The results of this study
will assist forensic laboratories seeking to acquire a plug and play solution to optimize
the processing and analysis of casework samples with MPS.
Keywords
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Article info
Publication history
Published online: January 31, 2019
Accepted:
January 30,
2019
Received in revised form:
November 30,
2018
Received:
August 1,
2018
Identification
Copyright
© 2019 Elsevier B.V. All rights reserved.
