Design Markers | Process BeadChip | Analyze Data

Microarray Data Analysis Workflows
Optimizing analysis efficiency for low- and high-throughput workflows.
Introduction®

The content and capacity of Infinium whole-genome genotyping
BeadChips continue to grow dramatically, leading to substantial
increases in the amount of data being processed. Such large data sets
can increase the import and processing time required by the analysis
pipeline significantly. To help users optimize data processing
efficiency, this technical note describes specific recommendations foranalysis workflows based on the throughput volume of a given project.

Import File TypeAny array-based genotyping assay begins with data acquisition. After®
sample DNA is hybridized to a BeadChip, it is loaded onto an iScan
System and scanned. DMAP files, which can be downloaded from theIllumina support site, enable identification of bead locations on theBeadChip and quanitification of the signal associated with each bead.
The resulting raw Intensity Data files (*.idat) can be converted toGenotype Call files (*.gtc). Both file types are compatible with®
GenomeStudio Software. However, given that *.gtc files can beprocessed ~ 20% faster than *.idat files (Figure 2), it is best practice toconvert *.idat files to *.gtc files before importing into GenomeStudio
Software for analysis.

Figure 2: Comparison of Analysis Times with Different File Types—Time required
to generate sample and SNP statistics for 1000 samples using GenomeStudio
Software with 2 different import file types. Data were processed on a computerwith 8 GB RAM.

During *.gtc file generation, raw signal intensity data from the *.idat files
is combined with Bead Pool Manifest files (*.bpm) that contain
information about single-nucleotide polymorphism (SNP)/probe
content on the BeadChip and cluster files (*.egt or *.egtp), which havereference information for each interrogated locus. All these files areneeded for Illumina software to call genotypes successfully.
iScan Control Software includes an AutoConvert feature, which
automatically converts *.idat files to *.gtc files during scanning on a perchip basis. Alternatively, the Illumina Laboratory Information
Management System (LIMS) has its own built-in automated *.idat fileconversion feature, called AutoCall. As discussed in subsequent
sections, the volume of a given project will determine which program touse for this file conversion.

Cluster File Generation

Figure 1: Cluster File Generation—Custom iSelect Arrays require generation ofclusters files for each project. After a BeadChip is scanned, the resulting data files(*.idat) and manifest files (*.bpm) are used to analyze at least 100 representative
samples in GenomeStudio Software to create cluster files.

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While pregenerated cluster files (*.egt for diploid organisms or *.egtp for
polyploid organisms) are provided for Illumina commercial genotyping®
array products, cluster files are not provided for custom iSelect Arrays.
To generate cluster files for custom arrays, GenomeStudio Softwareuses a training data set of at least 100 representative samples (Figure1). After a cluster file is created for a given experiment, the same cluster
file can typically be reused throughout the study. If experimental
conditions change enough to cause the quality of genotype calling todrift from the initial results, a new cluster file should be created using anew training data set.

Design Markers | Process BeadChip | Analyze Data

Low-Throughput Workflow

Table 1: Low-Throughput System Requirements

For low-throughput environments where instruments are not running athigh capacity, use the AutoConvert functionality within iScan Control
Software to convert *.idat files to *.gtc files (Figure 3). Running thisprocedure on the instrument reduces the expense of an offline LIMS
AutoCall server. However, the iScan System cannot be used to scananother BeadChip until after the AutoConvert process is complete.

Parameter

Minimum
Specifications

Recommended
Specifications

CPU Speed

2.0 GHz or greater

2.2 GHz or greater

Number of CPUCores

2 or more cores

2 or more cores

Memory8 GB RAM

16 GB RAM

With a smaller data volume, low-throughput users are unlikely to need
to use Beeline™ Software to prefilter the loci before performing further
analysis. The *.gtc files from AutoConvert can be imported directly into
GenomeStudio Software from iScan Control Software for analysis.
Report files (*.txt) generated by GenomeStudio Software can beanalyzed further downstream using third-party software (Figure 3).

Hardware Requirements
For low-throughput environments, computer workstations must
contain a minimum of 8 GB RAM (Table 1). While GenomeStudio
Software can be run using the minimum specifications, at least 16 GB
RAM is recommended to optimize processing time.

Software Requirements
Because data from iScan Control Software can be directly imported
into GenomeStudio Software, users working in a low-throughput
environment might not need to prefilter data with Beeline Software(Table 2).
Table 2: Low-Throughput Software UsageFeatureBeeline Software

GenomeStudio SoftwareData Filtering

Required

Required

No Data Filtering

Not Required

RequiredHigh-Throughput Workflow
High-throughput labs often process data sets at high capacities (eg, 24hours/day, 7 days/week). Because the iScan System must be offline toperform file conversion, turn off the AutoConvert feature during
BeadChip scanning. Direct the *.idat files to an offline server withAutoCall (LIMS users) or AutoConvert (non-LIMS users) for the *.gtc fileconversion (Figure 4).
The *.gtc files generated by AutoCall/AutoConvert are imported
directly into Beeline Software, where data is filtered to exclude poorly
performing loci across all samples (Figure 4). Including only useful data
reduces the final number of loci to be analyzed, resulting in betteroverall performance time. Users can import data rapidly into Beeline
Software to run basic analysis and reporting without using
GenomeStudio Software, other than to generate cluster files. Users
can create GenomeStudio projects from Beeline Software toinvestigate their data more deeply using the robust tools found in
GenomeStudio Software. Alternatively, users can employ third-party
software to perform further analysis (Figure 4).

Hardware Requirements
For high-throughput environments, computer workstations should
contain at least 16 GB of RAM (Table 3). To achieve optimal
performance, systems should be equipped with 32 GB of RAM. This will
provide Beeline and GenomeStudio Software with sufficient memory toload and process data.
Figure 3: Low-Throughput Workflow—For low-throughput labs, it isrecommended that users convert *.idat files into *.gtc files with the iScan Control
Software AutoConvert feature before importing into GenomeStudio Software foranalysis.

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Design Markers | Process BeadChip | Analyze Data

Table 4: High-Throughput Software UsageFeatureBeeline
Software

GenomeStudio Software

Data Filtering

Required

Required (for cluster filegeneration)

No Data Filtering (notrecommended)

Not Required

Required

Open-Source Tooling
While Beeline and GenomeStudio Softwares offer robust and flexible
solutions for filtering, analyzing, and reporting genotyping data, someusers prefer a fully automated approach that does not requireinteracting with any graphical user interfaces. To this end, an opensource library for parsing genotype call data encoded in *.gtc files
generated by either AutoCall or AutoConvert is available. Experienced
users can access this library to develop their own methods for filtering,analyzing, and reporting genotyping data that can be executed in a
fully automated pipeline (Figure 5). GenomeStudio Software is stillrequired to generate new genotyping cluster files (Table 5). For more
information and to access the open-source library for parsing *.gtc files,
visit the GitHub repository: github.com/Illumina/BeadArrayFiles.
Table 5: Open-Source Workflow Software Usage
FeatureBeeline Software

GenomeStudio Software

Data Filtering

Not Required

Required (for cluster file generation)

No Data Filtering

Not Required

Required (for cluster file generation)

Figure 4: High-Throughput Workflow—For high-throughput labs, convert *.idat
files to *.gtc files on an offline server using LIMS AutoCall to minimize instrument
downtime. After conversion, Beeline Software filters and analyzes *.gtc files. Use
GenomeStudio Software or third-party software for further analysis.

Table 3: High-Throughput System Requirements
Parameter

Minimum
Specifications

RecommendedSpecifications

CPU Speed

2.0 GHz or greater

2.2 GHz or greater

Number of CPUCores

2 or more cores

2 or more cores

Memory16 GB RAM

32 GB RAM

Software Requirements
Because prefiltering data is important in high-throughput environments
to improve the performance time of analysis, use of Beeline Software isrecommended. GenomeStudio Software, while not strictly required formost analysis and reporting, is still needed to generate genotyping
cluster files (Table 4).

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Figure 5: Open-Source Tooling Workflow—Experienced users can automate
genotyping analysis and reporting from *.gtc files by writing customized scripts that
use the Illumina open-source library for parsing *.gtc files.

Design Markers | Process BeadChip | Analyze Data

Summary
The genotyping microarray analysis pipeline can be configured at
multiple points to optimize efficiency based on the volume of data
being processed. By considering multiple factors, such as lociprefiltering and system hardware and software requirements,
researchers can minimize the sample processing time for low- andhigh-throughput environments.

Learn More
To learn more about microarray data analysis, visit
www.illumina.com/techniques/microarrays/array-data-analysisexperimental-design.html

Illumina, Inc. • 1.800.809.4566 toll-free (US) • +1.858.202.4566 tel • techsupport@illumina.com • www.illumina.com
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© 2016 Illumina, Inc. All rights reserved. Illumina, Beeline, GenomeStudio, Infinium, iSelect, iScan, and the pumpkin orange color are trademarks of Illumina, Inc.
and/or its affiliate(s) in the U.S. and/or other countries. Pub. No. 970-2011-006. Current as of 18 October 2016