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FAQ

1. What does "Read-Through" mean in Next-Generation Sequencing (NGS)?

In sequencing, "read-through" describes the condition where the generated sequencing reads completely span the entire length of the DNA insert. This concept is particularly relevant in paired-end (PE) sequencing. Read-through occurs when the combined length of the forward and reverse reads equals or exceeds the size of the insert fragment. For instance, with a PE150 strategy (2 × 150 bp), the total potential read length is 300 bp. Consequently, any DNA fragment with an insert size of 300 bp or smaller will be fully sequenced from both ends, achieving read-through.



2. How are adapter contamination and small fragments handled during NGS library QC?

The approach to resolving these issues depends on the nature of the contaminant. Adapter contamination or the presence of primer dimers can typically be effectively removed using magnetic bead-based purification, which selectively binds fragments of a desired size range. However, for libraries that genuinely consist of small inserts, a more targeted approach like gel extraction is often required. It is important to note that gel extraction generally yields lower recovery rates (approximately 30%), posing a higher risk of sample loss compared to standard bead purification.



3. How does the DNBSEQ-T7 platform ensure accuracy and reliability?

The DNBSEQ technology ensures high data fidelity through a multi-faceted approach:

  • DNA Nanoball (DNB) Technology: By employing rolling circle amplification (RCA) to generate DNA nanoballs, the system circumvents the error propagation associated with traditional PCR amplification.

  • Patterned Array & cPAS: The use of patterned array chips combined with combinatorial Probe-Anchor Synthesis (cPAS) minimizes signal crosstalk and significantly reduces base-calling errors.

  • Comprehensive Quality Control: The workflow incorporates rigorous monitoring at every stage—from initial sample inspection and library QC to the sequencing run itself. Real-time filtering of low-quality data ensures that key metrics, such as Q30 scores and GC distribution, consistently meet stringent standards.



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