⬅️ NGS Handbook
The amount of DNA one loads onto a flow cell is an important part of Illumina sequencing as it
influences the density of the clusters that form. Cluster density is the quantity of clusters that
are generated per flow cell surface area during the cluster generation stage of sequencing, and it
has a significant impact on data quality and total data output. If you load too little DNA, you’re likely to
‘under-cluster’ the flow cell. Under-clustering usually maintains data quality, but results in lower
data output. If you load too much DNA, clusters will be too close together (over-clustering),
resulting in poor image resolution and analysis problems. Over-clustered flow cells have lower Q30
scores and reduced data output. In each case (over/under clustering) the caveat is lower data
output. The goal of any sequencing run is to hit the Goldilocks balance between under and over
clustering. In this guide, we summarize Illumina’s recommendations for each instrument and discuss
procedures to prevent over/under clustering.
Table 1. Optimal flow cell loading concentrations and cluster density
| Illumina Instrument |
Version of Reagent Chemistry |
Recommended flow cell loading concentration |
Recommended Insert size (bp) |
Raw Density (K/mm2) |
Reference |
| HiSeq X |
v2.5 |
250+ pM * |
350 and 450 |
1255 - 1412 |
1
|
| HiSeq 3000 / 4000 |
N/A |
250+ pM * |
350 and 450 |
1310 - 1524 |
2 |
| HiSeq 2500 High Output |
v3 |
8 – 10 pM |
N/A |
750 - 850 |
3
|
| HiSeq 2500 High Output |
v4 |
8 – 10 pM |
N/A |
950 - 1050 |
3
|
| HiSeq Rapid Run |
v1, 2 |
8 – 10 pM |
N/A |
850 - 1000 |
3
|
| NextSeq |
v2 High and Mid Output |
1.8 pM |
N/A |
170 - 220 |
4
|
| MiniSeq |
High and Mid Output |
1.8 pM |
N/A |
170 - 220 |
5 |
| MiSeq |
v2 |
6 – 10 pM |
N/A |
1000 - 1200 |
6 |
| MiSeq |
v3 |
6 – 20 pM |
N/A |
1200 – 1400 |
6 |
* HiSeq 3000/4000 and HiSeq X use patterned flow cells (billions of nano-wells in a structured
pattern). While uniform cluster spacing and density reduce the emphasis of loading concentration,
under-loading a patterned flow cell results in a lower number of reads passing filter and fewer
unique reads. Overloading also results in a lower number of pass filter reads.
Best Practices for Avoiding Over/Under Clustering
Properly quantify your library
Inaccurate library quantification is the most common cause of over or under-clustering. The most
effective method for quantifying a library for NGS is by qPCR. Primers used in qPCR are similar to
those used in cluster generation, so only dually ligated, doubly stranded libraries with the proper
adapters are most efficiently amplified and quantified. With qPCR you don’t have to worry about
partially ligated library fragments or primer dimers inaccurately skewing the concentration of a
library. Fluor-metric methods that only detect double stranded DNA, such as Qubit, are fairly
accurate. However, dyes in these assays also bind partially ligated double stranded libraries and
adapter dimers, potentially overinflating the actual concentration of a library. We don’t recommend
using a Bioanalyzer or spectrophotometer for accurate library quantification. While the Bioanalyzer
is a good method to determine library size, measurement of concentration is not as accurate.
Accuracy of one’s measurement is critical for subsequent serial dilution and loading of a flow cell.
Ensure your libraries are of high quality
Contaminating spurious library products (adapter and primer dimer, singly ligated templates) and
improper size measurement of a library can overinflate concentrations causing you to underload a
flow cell and reduce optimal cluster density. In some circumstances calculating your library’s
concentration using the wrong template size can lead you to overload a flow cell. Using a
microfluidic instrument such as the Bioanalyzer or LabChip to size DNA products and detect spurious
library products is recommended. Dimers and partially ligated products can be easily eliminated
using magnetic bead based size selection.
Check sequence diversity of your library
Sequence diversity refers to the proportion of each nucleotide in each position on a template
library. Libraries with an equal proportion of each nucleotide are considered balanced. Cluster
density and flow cell loading recommendations in Table 1 assume you have a library that’s
sufficiently diverse. If your library is not diverse or sufficiently balanced reduce the library
loading amounts recommended in Table 1. by at least 10 - 20%. The exact reduction needed depends on
several factors such as your library’s insert size and whether the first bases of read 1 are
sufficiently diverse. Whenever you have a low diversity library, consider spiking in a high
diversity library such as PhiX to increase your overall nucleotide diversity.
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