A typical sequencing workflow comprises sample/library preparation, cluster amplification, DNA sequencing, image analysis/base calling, read alignment, and variant discovery. If any of these steps generate poor results, the quality of the final data set is compromised.
With Illumina sequencing, each step in this process is optimized to deliver accurate data across a broad range of applications, to ensure a high standard of quality for any research project.
Platform accuracy describes the overall accuracy of the sequencing workflow, accounting for each step of the process, from sample/library preparation through variant discovery. The sequencing workflow can be segmented into three main stages that each provide a unique accuracy contribution: Sample/Library Accuracy, Detection Accuracy, and Algorithm Accuracy.
Sample accuracy is associated with the library preparation step of the sequencing workflow. In this stage, DNA is fragmented for library construction. Each fragment in the library will eventually correspond to a sequencing read, so high fragment size uniformity and library diversity is important for achieving even coverage across the genome.
Errors that occur during library preparation, such as missing fragments due to a non-diverse library, cannot be identified by the sequencer. The portions of the genome not represented in the library will not be sequenced, leading to gaps in the data set.
In addition, quality scores do not reflect errors introduced during this step, as the sequencing signal will appear clean and error-free. The maximal achievable accuracy of most sequencing platforms is limited by the sample accuracy.
Thus it's critical to utilize high-quality library construction solutions such as TruSeq and other Illumina technologies.
Detection accuracy accounts for the second stage of the sequencing workflow, comprising cluster generation, DNA sequencing, and primary data analysis. Any errors that occur during this stage are typically reflected in the quality scores.
Detection errors, unlike sample errors, can be tracked using the well-established per-base quality scores.
Detection errors can be improved by re-sequencing, single-read error correction, or encoding schemes.Learn More About Quality Scores
Algorithm accuracy pertains to the secondary data analysis phase of the workflow, typically involving alignment and variant calling. The accuracy of the alignment method is critical.
Regardless of how high the quality of data is from the sequencing instrument, sub-optimal alignment will lead to a poor final data set, potentially with incorrectly placed mismatches, non-uniform coverage, and a high number of gaps. In turn, this can lead to high false positive and false negative rates. The variant calling method, by itself, also needs to be highly accurate for the same reasons.
Illumina offers user-friendly bioinformatics tools that enable researchers to perform accurate alignment and variant calling.Explore Bioinformatics Tools
Simple, all-inclusive whole-genome sequencing (WGS) library preparation that provides accurate and comprehensive coverage of complex genomes.
Generate whole-genome sequencing libraries and efficiently interrogate samples with limited available DNA.
Prepare sequencing libraries from mRNA to get a clear view of the coding transcriptome with strand-specific information.