Performance Comparison of Massively Parallel Sequencing (MPS) Instruments Using Single-Nucleotide Polymorphism (SNP) Panels for Ancestry.
SLAS Technol
; 26(1): 103-112, 2021 02.
Article
en En
| MEDLINE
| ID: mdl-32914686
ABSTRACT
Thermo Fisher Scientific released the Precision ID Ancestry Panel, a 165-single-nucleotide polymorphism (SNP) panel for ancestry prediction that was initially compatible with the manufacturer's massively parallel sequencer, the Ion Torrent Personal Genome Machine (PGM). The semiautomated workflow using the panel with the PGM involved several time-consuming manual steps across three instruments, including making templating solutions and loading sequencing chips. In 2014, the manufacturer released the Ion Chef robot, followed by the Ion S5 massively parallel sequencer in late 2015. The robot performs the templating with reagent cartridges and loads the chips, thus creating a fully automated workflow across two instruments. The objective of the work reported here is to compare the performance of two massively parallel sequencing systems and ascertain if the change in the workflow produces different ancestry predictions. For performance comparison of the two systems, forensic-type samples (n = 16) were used to make libraries. Libraries were templated either with the Ion OneTouch 2 system (for the PGM) or on the Ion Chef robot (for the S5). Sequencing results indicated that the ion sphere particle performance metrics were similar for the two systems. The total coverages per SNP and SNP quality were both higher for the S5 system. Ancestry predictions were concordant for the mock forensic-type samples sequenced on both massively parallel sequencing systems. The results indicated that automating the workflow with the Ion Chef system reduced the labor involved and increased the sequencing quality.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Polimorfismo de Nucleótido Simple
/
Secuenciación de Nucleótidos de Alto Rendimiento
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
SLAS Technol
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos