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. 2015 Dec 15;10(12):e0144811.
doi: 10.1371/journal.pone.0144811. eCollection 2015.

Utilization of Benchtop Next Generation Sequencing Platforms Ion Torrent PGM and MiSeq in Noninvasive Prenatal Testing for Chromosome 21 Trisomy and Testing of Impact of In Silico and Physical Size Selection on Its Analytical Performance

Affiliations

Utilization of Benchtop Next Generation Sequencing Platforms Ion Torrent PGM and MiSeq in Noninvasive Prenatal Testing for Chromosome 21 Trisomy and Testing of Impact of In Silico and Physical Size Selection on Its Analytical Performance

Gabriel Minarik et al. PLoS One. .

Abstract

Objectives: The aims of this study were to test the utility of benchtop NGS platforms for NIPT for trisomy 21 using previously published z score calculation methods and to optimize the sample preparation and data analysis with use of in silico and physical size selection methods.

Methods: Samples from 130 pregnant women were analyzed by whole genome sequencing on benchtop NGS systems Ion Torrent PGM and MiSeq. The targeted yield of 3 million raw reads on each platform was used for z score calculation. The impact of in silico and physical size selection on analytical performance of the test was studied.

Results: Using a z score value of 3 as the cut-off, 98.11%-100% (104-106/106) specificity and 100% (24/24) sensitivity and 99.06%-100% (105-106/106) specificity and 100% (24/24) sensitivity were observed for Ion Torrent PGM and MiSeq, respectively. After in silico based size selection both platforms reached 100% specificity and sensitivity. Following the physical size selection z scores of tested trisomic samples increased significantly--p = 0.0141 and p = 0.025 for Ion Torrent PGM and MiSeq, respectively.

Conclusions: Noninvasive prenatal testing for chromosome 21 trisomy with the utilization of benchtop NGS systems led to results equivalent to previously published studies performed on high-to-ultrahigh throughput NGS systems. The in silico size selection led to higher specificity of the test. Physical size selection performed on isolated DNA led to significant increase in z scores. The observed results could represent a basis for increasing of cost effectiveness of the test and thus help with its penetration worldwide.

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Conflict of interest statement

Competing Interests: Co-authors Gabriel Minarik and Tomas Szemes are employed by Geneton. Co-author Maria Gerykova Bujalkova is employed by Medirex and Nemocnicna. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Z score values of samples calculated by three different previously published methods.
–trisomic samples, –euploid samples, horizontal line—mean z score value of trisomic samples. Dotted lines represent the standard limit for identification of a trisomic sample (z score = 3). A—Ion Torrent PGM analyzed samples, B—MiSeq analyzed samples.
Fig 2
Fig 2. Determination of optimal in silico size selection limit for sequencing reads to be used in z score calculation.
Dotted lines represent the standard limit for identification of a trisomic sample (z score = 3). A—Ion Torrent PGM analyzed samples, B—MiSeq analyzed samples.
Fig 3
Fig 3. Z score values of trisomic and euploid samples before and after in silico size selection of sequencing reads.
–trisomic samples, –euploid samples, horizontal line—mean z score value). Dotted lines represent the standard limit for identification of a trisomic sample (z score = 3). A—Ion Torrent PGM analyzed samples, B—MiSeq analyzed samples.
Fig 4
Fig 4. Z score values calculated from reads without size selection (all), after in silico size selection (IS) and after physical size selection (P).
Horizontal lines represent mean z score value calculated from 3, 2 and 1 million raw reads (3m, 2m, 1m). Dotted lines represent the standard limit for identification of a trisomic sample (z score = 3). A—Ion Torrent PGM analyzed samples, B—MiSeq analyzed samples.

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