achieving optimal cluster density for your sequencing runs it's the first
step in maximizing the output and efficiency of your Illumina sequencing instrument hello I'm
Tim Gilmartin a staff field application scientist with aluminum and I'm Michele ei a staff technical
application scientists with Illumina in this video we will discuss optimal cluster density
and how over-and-under clustering can affect your sequencing data we will then examine common
clustering issues and ways to prevent them please use the cluster optimization overview guide found
on the Illumina support website as the official cluster optimization resource let's go back to Tim
to discuss optimal cluster density thanks Michelle one of the most critical metrics and assessing
sequencing run performance is cluster density this metric impacts both data quality and total
yield due to its effect on the number of reads passing filter and the percent of those reads
that remain high quality over clustering increases signal brightness which makes it difficult for the
instruments optics to find the correct focal plane this can cause poor cluster identification known
as template generation poor registration and other image analysis issues let's take a closer look on
how this can negatively affect sequencing data first overloaded signal intensities decrease the
ratio of base intensity to background this creates ambiguity during base calling and decreases data
quality leading to lower percent greater than Q 30 next over clustered flow cells typically have
more overlapping clusters which leads to poor signal purity from each cluster and thus poor
template generation this decreases the percent of clusters passing filter or percent PF a lower
percent PF ultimately reduces a run output also index reads typically have lower diversity which
can cause poor base calling since over clustering worsens the potential for poor base calling this
can lead to a de multiplexing failure finally when over clustering is extreme image focusing can fail
and terminate the run at any cycle resulting in a run failure as you can see over clustering
can have a significant impact on sequencing data as for under clustering these runs still
maintain high data quality but lower data output in general under clustering is preferable to over
clustering because these effects are less severe if optimal loading concentration is unknown for a
library type it's best to load at a conservative concentration for the first sequencing run let's
go to Michelle for more on recommended cluster densities thanks Tim performing a run at optimal
cluster density involves finding a balance between under clustering and over clustering ideally we
want to sequence at a high enough density to maximize total data output while maintaining
a low enough density to avoid over clustering Illumina has these recommended target densities
for our non pattern flow cells raw cluster density is displayed here in thousands of clusters per
square millimeter and measures how many clusters are identified on the flow cell regardless of
whether they pass this quality filter now let's discuss some common clustering issues and how
to prevent them from decreasing data quality and yield from your sequencing runs okay let's first
talk about library quality library preparations that undergo insufficient library cleanup leave
contaminants such as adapter dimers primer dimers or partial library constructs in the final library
pool in this case quantification of cluster generating fragments can be inaccurate due to
these contaminants which can lead to inconsistent cluster densities be sure to verify the quality
and purity of all libraries using the method described in the appropriate library prep
documentation commonly bioanalyzer tape station or fragment analyzer can be used to check the library
integrity average insert size and unexpected contaminant Peaks as seen at the hundred and
twenty base pair peak in this example next we'll review common issues resulting from library
quantification even with a high quality sequencing library inaccurate library quantification will
still lead to sub optimal or inconsistent clustering there are multiple quantification
methods available for next-generation sequencing the main three methods are key PCR fluorometric
based methods such as Cuba or Pico and automated electrophoresis analyzers such as bio analyzer
recommended quantification methods can vary between different library preps due to a
number of factors such as expected size range of the final library and amplification methods
the following table provides general guidance for using the different quantification methods
and can be found in the cluster optimization overview guide remember to always refer to
your library prep documentation for product specific recommendations and instructions it
is important to note that Illumina does not recommend spectrophotometry methods such as
nanodrop to quantify sequencing libraries those qualifications include single-stranded nucleic
acids and free nucleotides so these methods can overestimate library concentration and
lead to inconsistent cluster densities see the expert video tip how do I achieve consistent
quantitation part 1 for more details now let's examine common issues loading concentration can
cause the loading concentration is the final concentration of a library loaded onto an
instrument for sequencing depending on the system recommended loading concentrations can be found in
the denature and dilute guide or the system guide depending on the cluster density resulting from
using the recommended loading concentrations you may need to adjust the concentration to optimize
data output it's important to adjust loading concentrations in small increments this is because
the relationship between loading concentration and cluster density is nonlinear as can be seen
in this chart of example data for the my seek v2 chemistry here the broken blue line represents the
incorrect assumption of a linear relationship and the orange line represents the observed
relationship for this instrument in chemistry assuming a linear relationship when adjusting
the loading concentration can lead to over clustering and a corresponding drop in percent
clusters passing filter reducing output achieving optimal cluster density likely requires a small
empirical adjustment to determine optimum loading concentration another consideration in arriving
at final loading concentration is denaturation sequencing libraries we use sodium hydroxide
to denature the library to prepare hybridization to a flow cell is important to ensure that fresh
sodium hydroxide with a pH of 12.5 or greater is used sodium hydroxide that is not freshly
diluted can acidify and impair denaturation libraries with incomplete denaturation have
fewer strands available to bind and cluster on the flow cell reducing cluster density
also ensure the final concentration of sodium hydroxide in the diluted libraries is less than
1 millimolar excess sodium hydroxide in the diluted library can inhibit cluster
formation in fact the next seek 550d nature and dilution guide uses tris-hcl to neutralize the ph
and this process can be adopted for dilute and denatured protocols with other alumina
instruments if needed let's also consider how nucleotide diversity can impact clustering
alumina bases cluster density recommendations on diverse libraries diverse libraries are those
that have relatively even proportions of each nucleotide a T C and G low diversity libraries
or libraries that have an uneven proportion of nucleotides across the flow cell from one cycle
to the next can compromise software performance and data accuracy when sequencing low diversity
libraries Illumina recommends two strategies to improve data quality reduce the loading
concentration relative to the level recommended for diverse libraries and spica and Phi X or
other high diversity library to provide a more balanced signal as shown in this table
the Illumina support website hosts a wealth of resources for optimizing sequencing with
low nucleotide diversity additional support bulletins technical notes and video tips to
help you optimize clustering and sequencing with low diversity libraries are listed in
the youtube description below finally let's see how xampp preparation can impact clustering for
workflows that require manually mixing exclusion amplification or xampp reagents improper prep can
impact clusters passing filter and other metrics like occupancy be sure to follow instructions for
preparing xampp reagents carefully using specified volumes and durations we hope this video contain
helpful information about achieving optimal cluster density for your sequencing runs for
more on diagnosing suboptimal clustering issues and either patterned or non patterned flow cells
check out the videos linked in the description and thanks for being part of the Illumina community
you