NovaSeq 6000 and iSeq 100 sequencing instruments use patterned flow cells that have a set number of nanowells where libraries cluster. Because of this set metric, the cluster density is reported identically from run to run when comparing runs from the same platform type. To optimize loading concentrations on NovaSeq 6000 and iSeq 100 platforms, the % Occupied and % Pass Filter metrics can be plotted in Sequencing Analysis View™ (SAV) to determine if a run was underloaded, optimally loaded, or overloaded. Follow the instructions below for plotting these metrics and reviewing the resulting plot to optimize library loading.

To view the % Occupied data, SAV v2.4.5 or greater is required for NovaSeq 6000 data and SAV v2.4.7 is required for iSeq 100 data. SAV installers can be downloaded from the Sequencing Analysis Viewer Support page.

A video tutorial on how to create the % Occupied by % Pass Filter plot can be found here.

Note: The % Occupied metric is not available for data from other Illumina sequencing instruments.

Plotting % Occupied by % Pass Filter:

1. Load run data into SAV and select the Imaging tab.


 

2. Select the Scatter Plot tool from the toolbar to bring up a plotting window.


 

3. On the Data tab to the right of the window, scroll to select "% Occupied" for the "X Values" and "% Pass Filter" for the "Y Values”. A plot will automatically be generated.

Reviewing Plots:

Underloaded:

• Points fall on a line with a positive slope from the bottom-left to the top-right of the plot.

Optimally Loaded:

• Points fall within a cloud of points with a positive slope in the body of the plot.

Overloaded:

• Points have a slightly negative, near-vertical slope and approach a % Occupied in the high 90’s.

If the resulting plot indicates the run is underloaded or overloaded, increase or decrease the loading concentration, respectively, to optimally load the library. If the plot indicates the run was optimally clustered, continue to use this loading concentration for future runs of this library type that are of similar size.

For more information, refer to the Cluster Optimization Overview Guide.