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.
during paired-end (PE) chemistry, cluster sizes increase slightly due to extra cycles of amplification, which can lead to an increase in the number of overlapping clusters. With overclustered flow cells, this can affect run image registration and lead to poor Q30 scores and possible run failures (Figure 2). 1A 1B Figure 1: Data by Cycle: Intensity.
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