Application Note: Sequencing

Sequencing Library QC on the MiSeq® SystemPre-configured, automated QC workflows for cost-effective measurement of library quality
prior to a large-scale sequencing study.
Introduction
Increasing yields from the HiSeq® 2000 sequencing system haveenabled the largest and most complex genomic studies to date. To
maximize the efficiency of high-throughput sequencing, it is important
to know the quality of the starting library. A poor quality library canundermine the success of large-scale sequencing, and lead to costly
and time-consuming repeat experiments. Performing library quality
control (QC) using the MiSeq system before committing it to a fullscale HiSeq run can save time and money, while leading to better
sequencing results.
Using a single library prep method and taking only a single day, detailed
QC parameters, including cluster density, library complexity, percent
duplication, GC bias, and index representation can be generated on theMiSeq system. The MiSeq system has the unique ability to do paired-end(PE) sequencing for accurately assessing insert size. Library clusterdensity can also be determined and used to predict HiSeq cluster
density, maximizing yield and reducing rework. This application note
describes a library QC run performed on the MiSeq system.

Table 1: Sequencing Library QC Metrics
MetricSample name from the sample sheet

Clusters

Number of clusters sequenced for this sample

Clusters %Percentage of successfully indexed clusters
from this sample% PF

Percentage of clusters for this sample thatpassed filters% Aligned R1

Percentage of clusters for which read 1
successfully aligned% Aligned R2

Percentage of clusters for which read 2
successfully aligned

Length MedianMedian fragment length for this sample

Length Min

Low percentile (corresponding to 3 standard
deviations from the median) of fragment lengths
for this sample

Length Max

High percentile (corresponding to 3 standard
deviations from the median) of fragment lengths
for this sample

Mismatch R1

Mismatch rate for this sample in read 1

Mismatch R2

Mismatch rate for this sample in read 2

Observed
Diversity

Number of distinct aligned positions(x, y) for read 1 and read 2. Reads with the samealigned positions may represent PCR duplicatesof the same source oligonucleotide.

EstimatedDiversity

Estimate of the total library diversity derived
from the observed diversity and the number ofapparent PCR duplicates

QC Metrics
Using the Illumina Experiment Manager, samples within the library were
defined in the sample sheet, and the samples loaded into the MiSeq
reagent cartridge for automated cluster generation and sequencing.
After a 2 × 26 bp run, an automated Library QC report was generated
on the instrument using the MiSeq Reporter software. The Library
QC workflow does not perform variant calling, but provides a rangeof important QC metrics that pertain to library quality and sequencing
performance (Table 1 and Figure 1A). A Summary tab (Figure 1B)
provides information about the run parameters, depicting graphsof cluster data, high and low values, and mismatches. In addition,a sample tab provides coverage statistics showing the number ofaligned reads that cover each position in the reference sequence,and quality score data for each sample (data not shown). Users canregularly monitor these metrics and develop a standard set of criteriathat can guide future high-throughput sequencing runs.

Summary
Assessing the quality of a sequencing library before committing it toa full-scale sequencing run ensures maximum sequencing efficiency,
leading to accurate sequencing data with more even coverage.
Because the MiSeq system uses the same library prep methods andproven sequencing by synthesis chemistry as the HiSeq system,
it is ideal for analyzing prepared libraries in a single day prior toperforming high-throughput sequencing. With automated reports
available through the MiSeq Reporter, all quality metrics are easily

Definition

Sample Name

accessible and exportable. Data generated by the MiSeq system iscomparable to other Illumina next-generation sequencing platforms,ensuring a smooth transition from one instrument to another. Basedon your individual experimental requirements, metrics obtained fromperforming simple QC can be used to streamline and improve yoursequencing projects.

Learn More
Go to www.illumina.com/miseq to learn more about the next revolutionin personal sequencing.

Application Note: Sequencing
Figure 1: Automated Library QC Reports Generated on MiSeq Reporter

A. Quality Control (QC) Tab showing quality metrics for the sequencing library, including sample name (column one), abundance (columns two through six),
fragment length (columns seven through nine), mismatch rate (columns ten through twelve), and library diversity (last two columns).
B. Summary Tab showing high-level run information including low run statistics (upper left graph), cluster performance (upper right),
high run statistics (lower left), and mismatches between reads and the reference sequence (lower right).

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Pub. No. 770-2011-014 Current as of 10 November 2014