Targeted next-generation sequencing (NGS) is frequently used for identifying mutations, single nucleotide polymorphisms (SNPs), and disease-associated variants, as well as for whole-exome sequencing 1,2. One of most common target enrichment (TE) methods is hybridization-based TE, which uses oligonucleotide probes to capture regions of interest for downstream sequencing and analysis.
However, while targeted sequencing can reduce sequencing costs and simplify data analysis compared to whole-genome sequencing, it is still time-consuming and expensive; understanding key sequencing metrics can help you to maximize the value of each sequencing run.
After each sequencing run, several key sequencing metrics are assessed to evaluate sequencing performance and data quality; these include base quality, cluster density, and the number of reads passing filter. Here, we review five additional metrics that provide more in-depth insights into the success of hybridization-based target enrichment experiments.
Understanding each of these metrics will allow NGS users to evaluate the results of their target enrichment experiments, and to plan for future experiments.