In the next three years, the number of next-generation sequencing (NGS) samples processed will more than double. The NGS protocol will need to be industrialized, which will require identifying and eliminating bias and errors in assays. Major productivity gains, including reducing systematic bias, can occur by improving sample handling during sample preparation. All of the major technology platforms rely on ligation of DNA fragments during sample prep. This step is a major contributor to system-wide bias.
Three primary types of errors lead to bias: systematic, genome coverage, and batch processing effects. Depending on your sequencing platform, the genome content in your sample and data may be susceptible. Systemic bias is the most difficult to identify and therefore the most difficult to correct. It is pernicious because it can remain regardless of the genome coverage and platform.
Low-quality chemistry and poor liquid handling may exacerbate errors but can be minimized.
Chemistry is the main culprit behind erroneous base calls. Sequencing reactions are quite precise; minute variations in reagent volumes, flow, and temperature can lead to base substitution errors or an incomplete sequence extension. Because all technology platforms use adapters, base substitution or incomplete sequence extension can also mean fragment loss in the library. Eliminating these errors is vital when using an amplification-based protocol, as they are magnified during each cycle.
One way to minimize bias is to improve sample preparation and handling. Target enrichment, library preparation, and library amplification are key steps that are especially susceptible to introducing bias. At each step of enzymatically modifying DNA into genomic DNA (gDNA), the fragment diversity can be compromised. At the sample prep and quality control (QC) stages, input DNA can be twice the mass needed to run in the sequencing machine; in the final library, only a small fraction of the original sample is still present.