Transcription of Optimizing Cluster Density on Illumina Sequencing Systems
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Optimizing Cluster Density on Illumina Sequencing Systems
Optimizing Cluster Density on Illumina Sequencing SystemsUnderstanding Cluster Density limitations and strategies for preventing under- and of ContentsI. Introduction 3II. Understanding Optimal Cluster Density 3a. How Does Overclustering Affect Sequencing Data? 3 III. How to Diagnose Overclustering with Sequencing Analysis Viewer 3a. The Analysis Tab 3b. The Imaging Tab 5c. The Summary Tab 7IV. Common Causes of Under- and Overclustering and Strategies for Prevention 7a. Library Quality 7b. Library Quantification 8c. Flow Cell Loading 8d. Library Nucleotide Diversity 8V. Summary 10VI. Glossary 10 VII. References 10 3 I. IntroductionThe Illumina Sequencing workflow is based on 3 simple steps: libraries are prepared from virtually any nucleic acid sample, amplified to produce clonal clusters, and sequenced using massively parallel synthesis. The Density of clonal clusters has a large impact on Sequencing performance in terms of data quality and total data output. This primer discusses the principles of optimal Cluster Density , how to diagnose overclustering, and common causes and strategies for the prevention of overclustering on Illumina Sequencing Systems .
2500 in high output mode (Figure 4C), and the MiSeq® (Figure 4D) are shown. The blue or black tiles represent tiles that have intensities = 0 due to high cluster density. This is an indication that the flow cell is severely impacted. Figure 4: Flow Cell Charts. A) Density PF evaluation shows severe overclustering on the HiSeq 2500 in rapid run ...
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