Spike-in controls designed for assessing sample cross contamination and misidentification in sequencing workflows

Verification and elimination of sample ambiguity is critical for the widespread diagnostic use of NGS. Understanding the extent and source of miscalled reads, even at low levels, is important for accurate variant calling, and... [ view full abstract ]

Verification and elimination of sample ambiguity is critical for the widespread diagnostic use of NGS. Understanding the extent and source of miscalled reads, even at low levels, is important for accurate variant calling, and can affect statistical power in the detection of genetic associations.

Traditional library preparation methods lack the ability to effectively screen for procedural, instrumental, and human error in sequencing workflows. Here we describe how the use of a fully validated set of spike-in controls can identify these errors and ensure samples are correctly labeled and identified, with minimal impact on sequencing data. These spike-ins offer sequencing service providers a powerful tool to assess sample purity, cross-contamination, and index carryover, giving them confidence in the integrity of their data, and a confirmatory checkpoint for when errors do occur.