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Updated: Apr 6, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
Published on: April 11, 2016
Bioinformatic Challenges in Clinical Diagnostic Application of Targeted Next Generation Sequencing: Experience from
Joakim Crona1, Viktor Ljungström2, Staffan Welin3
1Department of Surgical Sciences, Uppsala University, SE-75185, Uppsala, Sweden.
Background:
Recent studies have demonstrated equal quality of targeted next generation sequencing (NGS) compared to Sanger Sequencing. Whereas these novel sequencing processes have a validated robust performance, choice of enrichment method and different available bioinformatic software as reliable analysis tool needs to be further investigated in a diagnostic setting.
Methods:
DNA from 21 patients with genetic variants in SDHB, VHL, EPAS1, RET, (n=17) or clinical criteria of NF1 syndrome (n=4) were included. Targeted NGS was performed using Truseq custom amplicon enrichment sequenced on an Illumina MiSEQ instrument. Results were analysed in parallel using three different bioinformatics pipelines; (1) Commercially available MiSEQ Reporter, fully automatized and integrated software, (2) CLC Genomics Workbench, graphical interface based software, also commercially available, and ICP (3) an in-house scripted custom bioinformatic tool.
Results:
A tenfold read coverage was achieved in between 95-98% of targeted bases. All workflows had alignment of reads to SDHA and NF1 pseudogenes. Compared to Sanger sequencing, variant calling revealed a sensitivity ranging from 83 to 100% and a specificity of 99.9-100%. Only MiSEQ reporter identified all pathogenic variants in both sequencing runs.
Conclusions:
We conclude that targeted next generation sequencing have equal quality compared to Sanger sequencing. Enrichment specificity and the bioinformatic performance need to be carefully assessed in a diagnostic setting. As acceptable accuracy was noted for a fully automated bioinformatic workflow, we suggest that processing of NGS data could be performed without expert bioinformatics skills utilizing already existing commercially available bioinformatics tools.
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