Average Rating: 4.0
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"Easy to use, useful for all kinds of library prep sequencing using HiSeq 2000. However it is difficult to reach customer services and the website is complicated."Review date:
26 Nov 2012 | TruSeq Technology
"A faster prep than before, directionality is 99.99%."Review date:
03 Oct 2012 | TruSeq Technology
TruSeq technology represents the latest advancements to Illumina sequencing reagents and software, aimed at optimizing data accuracy, research scalability, and the user experience. Illumina sequencing systems, powered by TruSeq technology, deliver the most accurate data across a broad range of applications. The TruSeq technology drives Illumina's sequencing workflow, from sample preparation to sequencing and data analysis.
A sequencing experiment workflow consists of a series of discrete steps, each making a unique contribution to the overall quality of a sequencing data set. A typical workflow comprises sample 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 TruSeq technology, each step in this process is optimized to deliver the most accurate data to ensure the highest 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 preparation through variant discovery. It ultimately determines the reliability of a sequencing experiment. The sequencing workflow can be segmented into three main stages that each provide a unique accuracy contribution: Sample Accuracy, Detection Accuracy, and Algorithm Accuracy.
Sample accuracy is associated with the sample preparation stage of the sequencing workflow. In this stage, DNA is fragmented in preparation 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 sample 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. These gaps cannot be corrected for by error correction methods employed by some sequencing technologies. Hence, quality scores do not reflect errors introduced during sample preparation, as the sequencing signal will appear clean and error-free. The maximal achievable accuracy of most sequencing platforms is limited by the sample accuracy.
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 typically leave a signature on the detected signal and are, therefore, reflected in the quality scores. Quoted error rates for sequencing systems are usually dominated by detection accuracy.
Detection errors are less harmful than sample errors because they can be tracked using the well-established per-base quality scores. Conversely, sample errors cannot be tracked directly, but manifest themselves by lowering the overall system accuracy. Detection errors can be improved by single-read error correction, multiple interrogation (re-sequencing), or encoding schemes.
Algorithm accuracy pertains to 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's TruSeq technology ensures that overall system accuracy is consistently maintained at a very high level throughout the sequencing workflow.