Consider your research goals, application, sample number, read length, sequencing depth, and required data output. Planning these factors together can help you select an appropriate sequencing strategy and make efficient use of available sequencing capacity. Illumina provides NGS experimental design and planning resources to help evaluate considerations such as read length, coverage, and sequencing quality.

Sequencing throughput is the total amount of sequencing data a system can generate, while sequencing depth describes how many times a particular base or region is sequenced. Higher throughput can support more samples, greater sequencing depth, or a combination of both, depending on the study design. Learn more about sequencing depth.

High-throughput sequencing can support increasing sample numbers while providing the sequencing capacity needed for data-intensive applications. Multiplex sequencing can further increase sample throughput by allowing uniquely indexed libraries to be pooled and sequenced together in the same run.

Maintaining data quality as studies scale starts with careful experimental design and consistent workflows across library preparation, sequencing, and data analysis. Evaluating factors such as sequencing quality scores, read length, and coverage can help researchers design experiments that meet their data quality requirements. Illumina provides experimental planning guidance covering each of these considerations

Moving to high-throughput sequencing may be appropriate when using increasing sample volumes, greater sequencing depth, or data-intensive applications begin to exceed the practical capacity of a benchtop workflow. Consider current project requirements and anticipated growth when evaluating the throughput and scalability your lab needs. Compare Illumina sequencing platforms to find a system that aligns with your research needs.