AFLP
®
System Analysis
Getting Started Guide
AFLP
®
System Analysis
Getting Started Guide
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TRADEMARKS:
Applied Biosystems, AB (Design), ABI PRISM, GeneMapper, and SNaPshot are registered trademarks, and FAM, GeneScan, ROX, and SNPlex are trademarks of Applied Biosystems or its affiliates in the U.S. and/or certain other countries.
AFLP is a registered trademark of Keygene N.V.
This product includes software developed by the Apache Software Foundation (http://www.apache.org/). Copyright © 1999-2000 The Apache Software Foundation. All rights reserved.
This product includes software developed by the ExoLab Project (http://www. exolab.org/). Copyright 2000 © Intalio Inc. All rights reserved.
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All other trademarks are the sole property of their respective owners. © Copyright 2009, Applied Biosystems. All rights reserved.
Preface
vii
How to Use This Guide . . . vii
How to Obtain More Information . . . viii
How to Obtain Support . . . x
Chapter 1
Getting Started
1
About Supported AFLP Reagent Chemistries . . . 2About the Example Data . . . 6
AFLP System Analysis Workflow . . . 7
GeneMapper Software Terms . . . 8
Starting the Software and Logging In . . . 8
Using This Guide With Your Own Sample Files . . . 9
Alternatives to the Procedures in This Guide . . . 10
Chapter 2
Setting Up the Analysis
11
Overview . . . 12Creating a Project . . . 13
Adding Sample Data to the Project . . . 14
Creating an Analysis Method . . . 15
Configuring the Allele Tab Settings . . . 16
Configuring the Peak Detector Tab Settings . . . 23
Configuring the Peak Quality Tab Settings . . . 26
Configuring the Quality Flags Tab Settings . . . 27
Chapter 3
Analyzing and Examining the Data
33
Overview . . . 34
Analyzing the Project . . . 35
Examining the Off-Scale Data . . . 38
Examining the Size Quality Data . . . 40
Sizing Solution #1: Adjusting the Analysis Method . . . 42
Sizing Solution #2: Manually Correcting Miscalled Peaks . . . 44
Sizing Solution #3: Modifying the Size Standard Definition . . 47
Examining the Analyzed Data . . . 51
Reviewing the Analyzed Data in the Genotypes Table . . . 52
Displaying the Peak Data in the Samples Plot . . . 54
Reviewing the Size Standard Concordance . . . 58
Visualizing Polymorphic Peaks . . . 60
Saving the Generated Panel and Bin Set . . . 63
Editing the Results . . . 69
Modifying the Marker . . . 69
Modifying a Bin . . . 70
Modifying Genotype Calls . . . 73
Completing the Analysis . . . 76
Chapter 4
Exporting and Printing the Analyzed Data
77
Overview . . . 78Exporting Results and Objects . . . 79
Exporting Samples and Genotypes Tabs . . . 80
Exporting Plots and Graphics . . . 80
Exporting Data for Use in a Spreadsheet . . . 81
Exporting Projects and Reference Data . . . 83
Printing Project Data . . . 84
How to Use This Guide
Purpose of This
Guide
This guide explains how to analyze the example amplified fragment length polymorphisms (AFLP®) data provided with the
GeneMapper® Software. It is designed to quickly teach you how to
size, genotype, and visualize the band patterns of amplified fragment length polymorphisms using the software. It also provides exercises that illustrate basic troubleshooting techniques and methods for exporting analyzed data for further analysis or presentation.
Audience
This guide is intended for trained laboratory personnel. Applied Biosystems is not liable for damage or injury that results from use of this guide by unauthorized or untrained parties.Assumptions
This guide assumes that:• You have installed GeneMapper® Software Version 4.1 as
described in the GeneMapper® Software Version 4.1 Installation
and Administration Guide (PN 4403614).
• You have a working knowledge of the Microsoft® Windows®
operating system.
Text Conventions
This guide uses the following conventions: • Boldindicates user action. For example:Type 0, then press Enter for each of the remaining fields. • Italic text indicates new or important words and is also used for
emphasis. For example:
Before analyzing, always prepare fresh matrix.
• A right arrow bracket () separates successive commands you select from a drop-down or shortcut menu. For example: Select FileOpenSpot Set.
User Attention
Words
Two user attention words appear in Applied Biosystems user documentation. Each word implies a particular level of observation or action as described below:
Note: Provides information that may be of interest or help but is not
critical to the use of the product.
IMPORTANT! Provides information that is necessary for proper
instrument operation, accurate chemistry kit use, or safe use of a chemical.
Examples of the user attention words appear below:
Note: The size of the column affects the run time.
Note: The Calibrate function is also available in the Control
Console.
IMPORTANT! To verify your client connection to the database, you
need a valid Oracle user ID and password.
IMPORTANT! You must create a separate Sample Entry Spreadsheet
for each 96-well plate.
How to Obtain More Information
Safety
Information
See the GeneMapper® Software Version 4.1 Installation and
Administration Guide (PN 4403614) for safety information.
Software
Warranty and
License
See the GeneMapper® Software Version 4.1 Installation and
Administration Guide (PN 4403614) for warranty and licensing
Related
Documentation
The following related documents are shipped with the software: • GeneMapper® Software Version 4.1 Installation and
Administration Guide (PN 4403614) – Provides procedures for
installing, securing, and maintaining version 4.1 of the GeneMapper Software.
• GeneMapper® Software Version 4.1 Getting Started Guides for
microsatellite analysis (PN 4403672), loss of hetereozygosity (LOH) analysis (PN 4403621), AFLP® system analysis
(PN 4403620), SNaPshot® kit analysis (PN 4403618), and
SNPlex™ system analysis (PN 4403617) – Five guides that
explain how to analyze the application-specific example data provided with the GeneMapper Software. The guides provide brief, step-by-step procedures for the analysis of microsatellite, LOH, AFLP® system, SNaPshot® kit, and SNPlex™ system data
generated by compatible Applied Biosystems electrophoresis instruments and Data Collection Software. The guides are designed to help you quickly learn to use basic functions of the GeneMapper Software.
• GeneMapper® Software Version 4.1 Online Help – Describes
the GeneMapper Software and provides procedures for common tasks. Access online help by pressing F1, selecting Help Contents and Index, or clicking in the toolbar of the GeneMapper window.
• GeneMapper® Software Version 4.1 Quick Reference Guide
(PN 4403615) – Provides workflows for specific analysis types and lists instruments, software, and analysis applications compatible with the GeneMapper Software.
• GeneMapper® Software Version 4.1 Reference and
Troubleshooting Guide (PN 4403673) – Provides reference
information such as theory of operation and includes troubleshooting information.
Portable document format (PDF) versions of this guide and the other documents listed above are available on the GeneMapper® Software
Version 4.1 Documentation DVD.
Note: For additional documentation, see “How to Obtain Support”
Send Us Your
Comments
Applied Biosystems welcomes your comments and suggestions for improving its user documents. You can e-mail your comments to:
Obtaining
Information from
the Online Help
The GeneMapper® Software features an online help system that
describes how to use each feature of the user interface. To access the online help, click in any window or dialog box (HelpContents
and Index if available) for more information.
How to Obtain Support
For the latest services and support information for all locations, go to http://www.appliedbiosystems.com, then click the link for
Support.
At the Support page, you can:
• Search through frequently asked questions (FAQs) • Submit a question directly to Technical Support
• Order Applied Biosystems user documents, MSDSs, certificates of analysis, and other related documents
• Download PDF documents
• Obtain information about customer training • Download software updates and patches
Chapter 1 Getting Started
Chapter 3
Analyzing and Examining the Data
Chapter 4 Exporting and Printing the Analyzed Data Chapter 2 Setting Up the Analysis
Getting Started
This chapter covers:■ About Supported AFLP Reagent Chemistries . . . 2
■ About the Example Data . . . 6
■ AFLP System Analysis Workflow . . . 7
■ GeneMapper Software Terms. . . 8
■ Starting the Software and Logging In . . . 8
■ Using This Guide With Your Own Sample Files . . . 9
About Supported AFLP Reagent Chemistries
About the
AFLP System
Amplified fragment length polymorphism (AFLP®) is a mapping
technique used to visualize polymorphisms in genomic DNA. The AFLP system combines the well-known restriction fragment length polymorphism (RFLP) technique and the polymerase chain reaction (PCR) to generate a large number of amplified restriction fragments from prepared, genomic DNA. When separated by electrophoresis, the samples yield unique band patterns that, when visualized by southern blot or fluorescence-based fragment analysis, can be used for high-resolution genotyping, polymorphism detection, or cladistics.‡
Applications Supported by the Workflows in This Guide
The flexibility and robustness of the AFLP system provides a broad number of applications for the technology. This guide contains a general analysis workflow that can be used to support two of the most common: sample/strain identification and backcross analysis for mapping. The procedures focus on sample identification analysis, however many of them are applicable to the alternative applications.
Compatible
AFLP Assays
The GeneMapper Software can analyze samples that have been: • Prepared using an AFLP chemistry that incorporates the
Applied Biosystems fluorescent dye-labeling and detection technology
• Run on a compatible Applied Biosystems electrophoresis instrument
Applied Biosystems has adapted the AFLP technique for use with its fluorescent dye-labeling and detection technology. In the modified system, a 5′ dye-labeled primer has been substituted for one of the selective primers used in the final amplification step. The following section describes the Applied Biosystems chemistry.
Template Preparation and Adaptor Ligation
Isolated, genomic DNA is exposed to two restriction endonucleases (EcoRI and MseI in the example) to generate restriction fragments. The samples are then ligated to double-stranded, oligonucleotide adaptors that contain sequences complimentary to the ends of the digested fragments (seeFigure 1-1).
Figure 1-1 Example of template preparation and adaptor ligation Preselective Amplification (Optional)
Following ligation, the fragments are amplified using a set of forward and reverse primers that target the combined sequences of the adaptors and restriction sites. The primary products of this “preselective” PCR are amplicons generated from the restriction fragments that were ligated to adaptors at both ends (see Figure 1-2).
Selective Amplification
Following preselective amplification, the fragments are amplified again using several “selective” primers, including a fluorescent, 5′ dye-labeled primer. Figure 1-3 illustrates the selective amplification of fragments that have been digested by the EcoRI and MseI restriction endonuclease. The primary products of the PCR are amplicons generated from EcoRI/MseI-ended fragments. In this way, the combination of selective primers act to further simplify the band pattern for the sample.
Note: When run, the compatible Applied Biosystems electrophoresis
instrument detects only the products of the EcoRI-ended fragments. The MseI-MseI fragments will not be visualized because they are not fluorescently labeled.
Electrophoresis and Data Collection
After a size standard is added to the amplified samples, they are loaded onto a compatible Applied Biosystems electrophoresis instrument for electrophoretic separation and fluorescence detection. During the electrophoresis, the instrument monitors the passage of the fluorescent, 5′ dye-labeled fragments through the polymer by detecting fluctuations in emitted light when the fragments migrate past a fixed laser beam. When finished, the instrument assembles the spectral data for each sample (see Figure 1-4) and stores it as a sample file, or saves it to the application database.
Figure 1-4 Signal data (electropherograms) of the example files provided for this guide (see “About the Example Data” on page 6)
Compatible
Instruments
See the GeneMapper® Software Version 4.1 Quick Reference Guide
(PN 4403615) for a list of compatible Applied Biosystems electrophoresis instruments and chemistries.
Supported AFLP
Chemistry Kits
The GeneMapper Software can analyze data generated using several AFLP fragment analysis chemistry kits. For a complete list of AFLP chemistries available from Applied Biosystems, visit the
About the Example Data
Location and
Function of the
Sample Files
This guide explains how to analyze AFLP sample data by guiding you through the analysis of an example data set from an AFLP experiment. The example sample files referred to in the exercises of this guide install automatically with the GeneMapper Software and can be found at the following location:
<drive>:\AppliedBiosystems\GeneMapper\ Example Data\AFLP Data
Note: The location shown above may vary depending on the
installation of the software.
About the
Experiment
The example AFLP sample files provided with the GeneMapper Software were generated as part of a sample identification study. The samples were prepared using a the Applied Biosystems AFLP® Plant
Mapping Kit that included 5′ FAM™ dye-labeled selective primer.
The samples were run on an Applied Biosystems 3100 Genetic Analyzer using the GeneScan™-500 (ROX™) size standard available
from Applied Biosystems.
Example Panel
and Bin Set
The panel and bin set used to analyze the example AFLP data set are generated automatically by the GeneMapper Software. See
AFLP System Analysis Workflow
Analysis
Workflow
Figure 1-5 summarizes the process for studying band patterns from many AFLP samples simultaneously.
Figure 1-5 Analyzing AFLP system data
Chapter 2 Set Up the AFLP Analysis
1. Create a panel for the project.
2. Create a new project and add samples to it. 3. Set the analysis parameters in the Samples tab. 4. Set the table settings.
5. Perform an initial analysis.
6. Create a new bin set and generate bins.
Chapter 4 Print or Export the Results (Optional)
If necessary:
• Print or export the desired views, plots, and tables for use in reports or presentations.
• Export desired views, plots, and tables for further analysis by third-party software.
Chapter 3 Analyze and Examine the Data
GeneMapper Software Terms
Starting the Software and Logging In
Starting the
GeneMapper
Software
1.
In the desktop, double-click GeneMapper v4.1 (StartAll ProgramsApplied BiosystemsGeneMapper GeneMapper v4.1).
2.
In the Login to GeneMapper dialog box, type the User Name and Password assigned by your system administrator.3.
Click OK.Table 1-1 Common terms used in this guide Term Definition analysis
parameters
A collection of user-defined settings (including an analysis method) that determine the sizing and genotyping
algorithms used by the GeneMapper Software to analyze all sample files in a project
bin A fragment size and dye color that define an allele. You typically create a bin for each possible allele associated with a marker
bin set A set of bins (allele definitions), typically specific to a set of experimental conditions
kit A group of panels
marker A known segment of DNA that has two or more allelic forms. A marker exists at a known chromosomal loci and can be a gene or a non-gene. A marker is defined by a name, fragment size range (bp), dye color, and repeat length. panel A group of markers, typically specific to a set of
Using This Guide With Your Own Sample Files
In addition to using this guide to analyze the example data provided with the software, you can use this guide to lead you through the general AFLP analysis workflow when analyzing your own sample files. For information on advanced software features, see the
Alternatives to the Procedures in This Guide
Overview
This guide presents one of several possible solutions for analyzing AFLP data using the GeneMapper Software. Once you havecompleted the exercises in this guide, this section provides you with a summary of several alternatives and other resources for customizing the process to fit the requirements of your laboratory.
Using
Autoanalysis to
Set Up Projects
The GeneMapper Software includes an Autoanalysis feature that can eliminate most of the tasks leading up to the analysis of an AFLP project. Much of Chapter 2, “Setting Up the Analysis,” explains how to manually create, add samples to, and analyze projects. When configured for Autoanalysis, the GeneMapper Software can
accomplish these tasks automatically by coordinating with the Data Collection Software. For a more detailed explanation of how to use the Autoanalysis feature to set up AFLP projects, see the
GeneMapper® Software Version 4.1 Installation and Administration
Guide (PN 4403614).
Using the
Command Line
Interface to Set
Up Projects
The GeneMapper Software features a command line interface that can perform most of the major functions of the software. The command line interface can be a useful tool when analyzing AFLP projects because it automates many of the tasks explained in Chapter 2, “Setting Up the Analysis.” For a complete description of the command line interface and how it can be used to automate the functions of the GeneMapper Software, see the GeneMapper® Software Version 4.1
Chapter 2 Setting Up the Analysis
Chapter 3
Analyzing and Examining the Data
Chapter 4 Exporting and Printing the Analyzed Data Chapter 1 Getting Started
Setting Up the Analysis
This chapter covers:■ Overview . . . 12
■ Creating a Project . . . 13
■ Adding Sample Data to the Project . . . 14
■ Creating an Analysis Method. . . 15
Overview
In This Chapter
In this chapter, you will learn to: • Create a project.• Add samples to the project from sample files.
• Create an analysis method and customize it for the analysis. • Set the analysis parameters for the project in preparation for the
analysis.
For More
Information
This chapter describes the limited number of software features that pertain to the analysis of an AFLP® system data set. If you want to
know more about the features of the user interface, the GeneMapper®
Software Online Help contains comprehensive descriptions of all
Creating a Project
Overview
In this procedure, you will create the project that you will use to analyze the example AFLP data set provided with the GeneMapper Software. The software uses projects to organize sample data and analysis parameters for analysis, but does not lock or modify them. Consequently, the same sample data, analysis methods, size standard definitions, and panels/bin sets can be shared by multiple projects.Note: If the GeneMapper Software is installed for Autoanalysis, the
software can be configured to create projects automatically. See the
GeneMapper® Software Installation and Administration Guide
(PN 4403614) for more information about the Autoanalysis feature.
Creating a Project
1.
Start and log into the GeneMapper Software (see “Starting the Software and Logging In” on page 8).2.
In the GeneMapper window, click (FileNew Project).3.
In the New Project dialog box, select AFLP, then click OK.4.
Add sample files to the project as explained on page 14.Adding Sample Data to the Project
Overview
In this procedure, you will add the example AFLP data set to the project from sample files (*.fsa) provided with the GeneMapper Software. When you add sample files to a project, the software saves the data to the database and populates the Samples tab of theGeneMapper window with the associated sample information.
Note: If the GeneMapper Software is installed for Autoanalysis, the
software can add sample data to projects automatically. See the
GeneMapper® Software Installation and Administration Guide
(PN 4403614) for more information on the Autoanalysis feature.
Adding Samples
1.
Click (FileAdd Samples to Project).2.
In the Add Samples to Project dialog box, select the Files tab.3.
Navigate to the Example Data folder:<drive>:\AppliedBiosystems\GeneMapper\Example Data\
4.
Select the AFLP folder.5.
Click Add to List >>.6.
Click Add to add all of the files in the selected folder to the project.7.
Create an analysis method as described on page 15.Creating an Analysis Method
Overview
In this procedure, you will create an analysis method that you will use to perform an initial analysis of the example data set and to automatically generate a panel and bin set for the project.About Analysis
Methods
An analysis method is a collection of settings that control how the GeneMapper Software performs most aspects of an analysis (peak detection, allele calling, peak quality assessment, and process quality determination). The settings of analysis methods are application-specific. For example, the automatic panel and bin generation feature explained in this section is available only to AFLP analysis methods. Also, analysis methods are independent of project and sample data, so they can be applied to and used by multiple projects simultaneously.
Creating the
Analysis Method
1.
In the GeneMapper window, click (ToolsGeneMapperManager).
2.
In the GeneMapper Manager, select the Analysis Methods tab.3.
In the Analysis Methods tab, click New.4.
In the New Analysis Method dialog box, select AFLP, then click OK.5.
In the General tab of the Analysis Method Editor dialog box, type AFLP Tutorial as the name for the analysis method.Note: You can also create an analysis method by clicking the first cell
in the Analysis Method column, and selecting New Analysis Method.
Configuring the Allele Tab Settings
About the
Settings
The settings of the Allele tab determine how the GeneMapper Software generates allele calls from the sample data following the peak detection phase of the analysis. The settings include two features that are unique to the analysis of AFLP data: Automatic Panel Generation (see page 18) and allele calling normalization (see page 21).
Configuring the
Settings
Note: Figure 2-6 on page 18 illustrates the modifications made to the
Allele tab in this procedure.
1.
In the Analysis Method Editor dialog box, select the Allele tab.2.
In the Analyze Dyes settings, select Blue.The Analyze Dyes settings determine the dye signal data that the software uses to call alleles. For this procedure, Blue is selected because the samples of the example data set were amplified using FAM™ dye-labeled selective primers.
3.
In the Analysis Range settings, observe the default base pair range specified by the software (50 to 500 bp).The Analysis Range settings specify the limits that the software uses to perform the allele calling analysis. For this procedure, the software does not call peaks less than 50 bp or greater than 500 bp because the default range is used.
IMPORTANT! The Analysis Range settings do not limit the
scope of the peak sizing and detection functions.
5.
In the Allele Calling settings, configure the software to perform binary scoring of the AFLP alleles:a. Click Edit Labels.
b. In the Thresholds column of the Thresholds and Labels Details
dialog box, type 50 in the first row, then 100 in the second.
c. In Labels column, type 0 in the first row, Check in the
second, then 1 in the third (see the figure below).
d. Click OK to close the dialog box.
Note: Alternatively, you can select Name alleles using bin
names to have the software label each allele using the rounded
position (in bp) of the associated bin.
The following figure illustrates how the software applies the settings of the Thresholds and Labels Details dialog box.
6.
In the Allele Calling settings, select Delete common alleles so that the software removes from the analyzed data set, the genotype calls for peaks that are present in all samples of the project. For an explanation of the feature, see “About the Delete Common Alleles Function” on page 20.Note: If you selected the Delete common alleles option but
want the software to call peaks that exceed a specific peak height ratio, select Do not delete if Peak Height Ratio exceeds, then type a value to serve as a threshold.
Peak Height (h) Example Call
h < 50 RFU
50 RFU≤ h <100 RFU
7.
Configure the Normalization settings:a. (Optional) In the Normalization Scope settings, select
Project.
b. (Optional) In the Normalization Method settings, select
Sum of Signal.
For an explanation of the normalization feature, see “Allele Calling Normalization” on page 21.
Note: To gain a better understanding of the normalization
settings, repeat the analysis explained in this guide while changing the options in steps 7a and 7b. The differences between normalization methods become more apparent when the allele calls of the resulting Genotypes tables are compared.
8.
Configure the Peak Detector settings as explained on page 23.Figure 2-6 Allele calling settings of AFLP analysis methods
Automatic Panel
Generation
The Automatic Panel Generation feature enables the software to algorithmically generate panels and bins based on the collective peaks present in the samples of an AFLP project. Because genomic sequence information is typically unavailable for AFLP experiments, the number
Panel settings (configured for panel generation) Analyze Dyes settings Allele calling settings Normalization settings
and distribution of resulting fragment sizes are often difficult to predict. Since AFLP data can consist of several hundred peaks, manual generation of panels and bin sets is often time consuming and
impractical. The Automatic Panel Generation feature offers a solution by configuring the software to algorithmically create a panel and bin set for a project based on the set of rules defined in the Allele tab.
Note: You do not need to regenerate the panel and bin set for each
AFLP analysis. As explained in this guide, the generated panel and bin set can be saved for use in other projects and edited if necessary.
Modifying the Panel Generation Settings for Backcross Analysis
If you are performing a backcross comparison of AFLP data, you can configure the Panel settings so that the software generates the panel using only the parent samples of a project. However, to generate the panel from a subset of the project data, the parent samples must be named according to a convention that distinguishes them from the other samples. For example, the “A” Panel settings of Figure 2-7 on page 20 could be used to generate a panel from a project in which all parent samples contain the “P” prefix (such as P1-Dermis20051105). Alternatively, you can also identify parental samples for panel generation by entering labels in one of the user defined (UD) columns of the Samples tab (right side of the table). For example, the “B” Panel settings of Figure 2-7 on page 20 could be used to generate a panel for a project in which the rows of the parent samples contained the labels “P1” or “P2” in the UD1 column of the Samples tab.
Note: If the parent sample names are indistinguishable from the
other samples of a project, a panel could also be created by
Figure 2-7 Example Panel settings for a backcross analysis of (A) parent samples containing the “P” prefix, (B) parental samples with the P1 and P2 labels in the UD columns
About the Delete
Common Alleles
Function
You can use the Delete common alleles function of the Allele Calling settings (see Figure 2-8 on page 21) to simplify the allele calls generated for an AFLP project. Depending on the complexity of the sample banding patterns, the bin set of an AFLP project can consist of more than a hundred alleles. The Delete common alleles option reduces the number of bins to a more manageable number by configuring the software to call only those peaks that differentiate the samples.
The software applies the Delete common alleles function based on a Scope setting:
• Within run – The software deletes alleles that are common to the other samples in the same run folder.
• Project – The software deletes alleles that are common to all samples of the project.
Using the Peak Height Ratio Setting
The Peak height ratio setting restricts the deletion of common alleles based on a threshold defined by the ratio of peak heights for common peaks (maximum/minimum). For example, a project containing 10 samples with common peaks for a given bin is analyzed using an analysis method with the Allele Calling settings shown in Figure 2-8 on page 21. During the analysis, the software calculates the ratio of the
User defined (UD) columns
A B
maximum peak height over the minimum peak height for the 10 peaks. If the calculated ratio is greater than 1.8 (the setting in the analysis method) then the software retains all peaks as potential polymorphisms. Otherwise, the software removes the common peaks from the analysis.
Figure 2-8 Peak Height Ratio feature of the Allele Calling settings
Allele Calling
Normalization
The normalization feature of the AFLP analysis method minimizes the effects of differences in signal strengths between AFLP samples prior to the allele calling phase of the analysis. Variations in chemistry (such as initial template concentration or differences in amplification efficiency) and certain run conditions can influence the strength of the fluorescent signals collected from AFLP samples. During the analysis, these peak heights differences between samples can affect allele calling unless the software compensates for them algorithmically. When configured for normalization, the GeneMapper Software performs the following operations independently for each dye signal after peak detection and just prior to the allele calling phase of the analysis:
1.
Based on the Normalization Method setting (Sum of Signal or Maximum Signal), the software calculates a normalization factor for each sample of the population defined by the Normalization Scope setting (Within Run or Within Project).2.
The software generates an averaged normalization factor for the sample population defined by the Normalization Scope setting.3.
For each sample, the software:a. Calculates the ratio of the sample normalization factor to
the averaged normalization factor for the population.
b. Multiplies the sample signal by the calculated
normalization factor ratio.
Note: The normalization factors (individual or averaged) cannot be
visualized using version 4.1 of the GeneMapper Software.
Note: AFLP normalization is different from Size Standard
Normalization, which is a new feature added to analyze data normalized on the 3500 series genetic analyzers.
Normalization Scope
The Normalization Scope setting determines the population of samples that the software includes in the normalization operation:
• None – The software does not normalize the data.
• Within Run – The software normalizes the dye signals for each sample to the other samples in the same run folder.
• Project – The software normalizes the dye signals for each sample to all samples in the project collectively.
Normalization Method
The Normalization Method setting determines how the software calculates the normalization factor for the sample population defined by the Normalization Scope setting:
• Sum of Signal – The software sums the signals within the analysis range for each sample, calculates the average for all samples defined by the Normalization Scope setting, then calculates the normalization factor for each sample as the ratio of the sample's sum over the average.
• Maximum Signal – The software identifies the maximum signal within the analysis range for each sample, calculates the average for all samples defined by the Normalization Scope setting, then calculates the normalization factor for each sample as the ratio of the sample's maximum signal over the average.
Figure 2-9 Allele calling normalization settings
Configuring the Peak Detector Tab Settings
About the
Settings
The settings of the Peak Detector tab determine the methods that the GeneMapper Software uses to detect and size the peak data.
Configuring the
Settings
1.
Select the Peak Detector tab.2.
At the top of the Peak Detector tab, select Peak DetectionAlgorithmAdvanced.
3.
Observe, but do not modify, the default peak detection settings. The default settings of the Advanced Peak Detection Algorithm are adequate to analyze the example data set (although you will change them later in this guide). The information provided below is a reference to enable you to edit the settings for your own projects.Note: See the GeneMapper® Software Online Help for a detailed
description of the Peak Detection settings.
4.
Configure the Peak Quality settings as explained on page 26.Normalization Scope setting
Settings
Important to the
AFLP Analysis
The Peak Detection settings are common to all analyses performed by the GeneMapper Software; however, the Range and the Peak Amplitude settings are particularly important to AFLP analysis.
Ranges Settings
The Range settings determine data point range that the software uses when detecting peaks in the raw data of the sample files for the project. The settings consist of two pairs of limits that control the ranges of two distinctly different aspects of the peak detection analysis.
• Analysis Range – Defines the range of data points within which the software detects peaks in the raw data. If the Partial Range option is selected, the software analyzes only the data points in the range specified by the Start Pt and Stop Pt fields.
• Sizing Range – Defines the range of fragment sizes within which the software detects peaks in the processed data. If the Partial Range option is selected, the software analyzes only peaks in the range specified by the Start Size and Stop Size fields.
IMPORTANT! Take care not to set the Analysis Range limits so that
the software excludes size standard peaks from the analysis. The Sizing Range setting in the Peak Detector tab must match the range specified by the size standard definition. Otherwise, the software will exclude one or more size standard peaks from the analysis and will not size the associated samples correctly.
Peak Amplitude Thresholds
Configuring the Peak Quality Tab Settings
About the
Settings
The Peak Quality tab contains the settings that control how the GeneMapper Software performs several of the Process Quality Value (PQV) tests that appear in the Genotypes tab.
Configuring the
Settings
1.
Select the Peak Quality tab.2.
Observe, but do not modify, the default peak quality settings. The default settings are adequate to analyze the example data set, so the information provided below serves as a reference to enable you to edit the settings when analyzing your own projects.3.
Configure the Quality Flags settings as explained on page 27.Peak Morphology Settings
The Max Peak Width setting defines the limit that the software uses to perform the Broad Peak PQV test. When the area under a peak is broader than the specified value, the software displays (Check) for the associated sample in the BD PQV column of the Genotypes tab.
Peak Settings
The Pull-Up Ratio setting defines the ratio threshold that the software uses to detect pull-up peaks. During an analysis, the software computes the ratios between analyzed dye signals at each individual peak location. When the ratio of dye signals at a peak is greater than the Pull-Up Ratio value, the software displays (Check) for the sample in the SPU PQV column of the Genotypes tab to indicate the possible presence of a pull-up peak.
The Pull-Up Scan setting defines the distance in data points from detected peaks that the software scans for pull-up peaks in
Configuring the Quality Flags Tab Settings
About the
Settings
The Quality Settings tab contains the weights and threshold values for the PQV.
Configuring the
Settings
1.
Select the Quality Flags tab.2.
Observe, but do not modify, the default quality flags settings. The default quality settings are adequate to analyze the example data set, so the information provided below serves as a reference to enable you to edit the settings when analyzing your own projects.3.
When you are finished reviewing the settings, click OK to save the AFLP Tutorial Analysis Method.4.
Click Done to close the GeneMapper Manager.5.
Apply the analysis parameters to the project as explained onpage 29.
Quality Flag Settings
The quality flag settings determine the extent that the associated PQV affect the Genotype Quality (GQ) values for the project. Each setting consists of a value (weight) between 0 and 1 that determines the contribution of the associated PQV based on the following equation: GQ = MQ× ( (1 − SPU) × (1 − BD) × (1 − OS) )
where:
• BD – Quality flag setting of the Broad Peak PQV test • GQ – Genotype Quality PQV for the given genotype • MQ – Marker Quality value for the given genotype • OS – Quality flag setting of the Off-Scale PQV test
• SPU – Quality flag setting of the Spectral Pull-Up PQV test
Note: For a detailed description of the BD, GQ, MQ, OS, SPU PQV,
see the GeneMapper® Software Version 4.1 Reference and Troubleshooting
For example, if a sample fails the Broad Peak (BD) test because it contains a peak that exceeds the Max Peak Width threshold, then the software applies the BD quality flag setting to the equation used to generate the GQ value. The following table summarizes the effects of various values assigned as the weight for the BD PQV.
PQV Thresholds
The PQV Thresholds define the limits that determine when the
software displays (Pass), (Check), or (Low Quality) in the SQ (Sizing Quality) and GQ (Genotype Quality) columns. The SQ and GQ evaluations performed by the software yield values of 0 to 1. Based on the PQV Threshold settings in the Peak Quality tab of the analysis method, the software translates each value into the appropriate icon.
Based on the default PQV Threshold settings shown in the figure above, the software would apply the SQ and GQ icons as follows:
BD Quality Flag Setting Effect on GQ PQV
0 No effect
0.5 Reduces the GQ value by 1/2
1 Reduces the GQ value to 0
SQ/GQ Value (v) Icon Displayed in the SQ/GQ Column
0.75≤ v (Pass)
0.25 < v < 0.75 (Check)
Applying the Analysis Parameters
Overview
In this procedure, you will apply the analysis method and size standard to the samples in the AFLP Tutorial project.Note: If the GeneMapper Software is installed for Autoanalysis, the
software can be configured to apply the analysis parameters automatically. See the GeneMapper® Software Installation and
Administration Guide (PN 4403614) for more information on the
Autoanalysis feature.
Applying a
Table Setting
The AFLP Default table setting configures the Samples and
Genotypes tabs so that they display only columns that are relevant to the analysis of AFLP data. For information on creating custom table settings, see the GeneMapper® Software Online Help as explained in
“Related Documentation” on page ix.
To apply the AFLP Default table setting:
In the toolbar, select Table SettingAFLP Default to apply the default AFLP table setting to the new project.
Applying the
Analysis
Parameters
1.
In the Samples tab of the GeneMapper window, configure the settings for the first sample in the project:a. Click the first cell in the Analysis Method column and
select AFLP Tutorial (created on page 15).
b. Click the first cell in the Size Standard column and
select GS500(-250)ROX.
IMPORTANT! Because the AFLP Tutorial analysis method is
configured to generate the panel automatically, the cells in the Panel column must be set to None.
2.
While pressing Ctrl, select the headings for the AnalysisMethod, Panel, and Size Standard columns.
3.
Select EditFill Down (Ctrl + D) to apply the settings in the first sample (row) to the remaining samples.IMPORTANT! The analysis method, panel, and size standard
settings must be identical for all samples.
4.
Perform the analysis as described in Chapter 3.Verifying the
Agreement of the
Analysis
Parameters
Analysis parameters conflicts are a common source of problems when analyzing projects. Mismatched settings can prevent the software from sizing or genotyping samples, or even from beginning an analysis. Therefore, before performing any analysis, check the following potential problem areas for conflicts.
• Analysis Parameter Agreement – The analysis method, panel, and size standard settings must be identical for all samples. • Analysis Range/Sizing Range Agreement – The maximum and
minimum limits for the Analysis Range and Sizing Range of the analysis method applied to the samples in the project must match.
• Analysis Method/Size Standard Agreement – The size standard definition applied to the samples in the project cannot contain a fragment size that is outside of the Analysis/Sizing Ranges of the analysis method.
• Panel and Bin Set Agreement – The bin set specified in the analysis method used by the sample must also be used by the panel displayed in the Panel column of the Samples tab.
Note: When the software encounters an analysis parameter or
panel/bin set conflict, it displays the message shown in Figure 2-10. When the software encounters an analysis/sizing range conflict, it does not display a warning although the problem may be apparent itself in other ways (such as a project-wide PQV failure).
Figure 2-10 Alert dialog box displayed for some data entry errors To view the Error Message information for a specific sample:
1.
In the GeneMapper window, select the Samples tab.2.
In the Navigation Pane of the GeneMapper window, click to expand the project folder, then select the desired sample.3.
Select the Info tab to display a summary of all information for the associated sample file.4.
In the Info tab, scroll the text to display the Error Message heading and confirm source of the failure.Chapter 3 Analyzing and Examining the Data
Chapter 1 Getting Started Chapter 2 Setting Up the Analysis Chapter 4 Exporting and Printing the Analyzed Data
Analyzing and
Examining the Data
This chapter covers:■ Overview . . . 34
■ Analyzing the Project . . . 35
■ Examining the Off-Scale Data . . . 38
■ Examining the Size Quality Data . . . 40
■ Examining the Analyzed Data . . . 51
■ Saving the Generated Panel and Bin Set . . . 63
■ Editing the Results . . . 69
Overview
In This Chapter
In this chapter, you will learn to: • Analyze the project.• Create a custom size standard definition. • Edit the results:
– Use the Samples or Genotypes plot to add, remove, delete bins and alleles.
– Use the Panel Manager to add, remove, delete bins. • Examine the analyzed data:
– Review the PQV in the Samples and Genotypes table. – Use the Raw Data plot to review the raw data of individual
samples for off-scale peaks.
– Use the Size Match Editor to review the sizing data for individual samples.
– Use the Samples Plot to review the concordance of the size standard data (and replicates).
– Review the analyzed data in the Genotypes table. • Save and apply generated panels and bin sets. • Troubleshoot and correct common sizing errors.
Where You Are in
the Procedure
In the previous chapter, you performed all of the tasks necessary to prepare an AFLP® project for analysis. The project you constructed
for the example data now is ready for the analysis performed in this chapter.
For More
Information
This chapter describes the limited number of software features that pertain to the analysis of an AFLP data set. If you want to know more about the user interface, the GeneMapper® Software Online Help
Analyzing the Project
Overview
In this procedure, you will analyze the project and conduct a preliminary review of the PQV in the Samples and Genotypes tabs.About the
Automatic Panel
Generation
Because the analysis method is configured to generate a panel automatically (see step 4 on page 16), the software generates a panel and bin set from the samples following the peak calling and sizing. After filtering the data set, the software creates a panel containing a single marker that spans the analysis range defined in the Alleles tab of the analysis method. Then, for each peak in the filtered data set, the software creates a 0.8-bp bin centered on the apex of the associated peak. After the analysis, the generated panel and associated bin set can be exported for use in other projects.
IMPORTANT! Do not use the AutoBin function of the Panel Manager
to generate bins for AFLP data. The AutoBin function is used to analyze microsatellite data and cannot generate bins for AFLP projects.
Note: Because the analysis method is configured to delete common
Analyzing the
Project
1.
Click (AnalyzeAnalyze) to begin the analysis.2.
When prompted to save, type AFLP Tutorial, then click OK.During the analysis, the software highlights (in green) the row of the sample currently undergoing analysis. If a sample fails analysis, the software highlights the row of the failed sample in red.
3.
After the analysis is complete, observe:• Status bar – Displays “Analysis Completed” indicating that the software has finished analyzing the project.
• Cells in the Status column – Are empty, indicating that the software successfully processed each sample.
• Genotypes tab – Becomes available indicating that the software completed the analysis.
4.
Click (AnalysisLow Quality to Top) to sort the data so that the samples that produced lower PQV scores appear at the top of the table in the Samples tab.5.
In the Samples tab, scroll horizontally and observe: • OS column – Nearly all cells display (Check)indicating that the associated samples failed the off-scale (OS) Process Quality Value (PQV) test.
• SQ column – All cells display (Check), indicating that the software completed the analysis but it encountered one or more problems when sizing the samples.
Status bar Genotypes tab (active)
6.
Select the Genotypes tab, and observe that the Navigation Pane and the Panel and Marker columns display the generated panel and marker (_Internal_Panel_ and _Internal_Marker_Dye_Blue_).Note: Had the example data analyzed normally and the OS and
SQ columns displayed (Pass), you would begin reviewing the genotypes as described in “Examining the Analyzed Data” on page 51 at this point in the procedure.
7.
Troubleshoot the off-scale data as explained page 38.Generated marker Generated panel
Examining the Off-Scale Data
Overview
In this procedure, you will confirm the presence of off-scale peaks in the raw data of the samples that display (Check) in the Off-Scale (OS) PQV column of the Samples tab.Note: The Raw Data tab/plot (and the neighboring Info and EPT Data
tabs) used in this procedure can be very useful when troubleshooting samples that do not display (Pass) in one or more PQV.
About the
OS PQV
The OS PQV evaluates the dye signals of each sample for off-scale data (peaks that exceed the maximum detectable range). The software displays the OS PQV in both tabs of the GeneMapper window; however, the function of the PQV differs in each.
• Samples Tab OS PQV – Evaluates the size standard dye signal for off-scale data.
• Genotypes Tab OS PQV – Evaluates the analyzed dye signal(s) for off-scale data.
When the software detects off-scale data in the dye signals of a sample, it displays (Check) in the OS column of the appropriate tab.
Reviewing the
Raw Data for
Off-scale Peak(s)
1.
In the GeneMapper window, select the Samples tab.2.
In the Navigation Pane of the GeneMapper window, click to expand the contents of the AFLP run folder, then select a sample that displays (Check) in the OS column.3.
Select the Raw Data tab to display an electropherogram of the raw fluorescence data collected for the sample when it was run.Project folder (expanded) Sample file (selected)
4.
In the plot of the Raw Data plot, drag the mouse cursor ( ) across the 1000 to 2000 data point region of the x-axis.5.
Observe the off-scale data in the Raw Data plot:• Off-scale peak (~1140) in the dye signal (blue) for the FAM™ dye-labeled AFLP fragments
• Off-scale peak (~1265) in the dye signal (red) for the GeneScan™-500 (ROX™) size standard
The off-scale peaks present in the samples of the example data set are “primer” peaks that consist primarily of unincorporated dye-labeled primer left over from the selective amplification (blue peak), and from the size standard manufacturing process (red peak).
Note: The red off-scale peak is interfering with the sizing of the
35-bp peak. You will correct the problem later in “Sizing Solution #3: Modifying the Size Standard Definition” on page 47.
6.
(Optional) Select additional samples in the Navigation Pane and observe the off-scale data.7.
When finished, select the AFLP run folder in the Navigation Pane to display the Samples tabs.8.
Troubleshoot the Size Quality data as explained on page 40.Off-scale peak (red signal, ~8200 RFU) Off-scale peak
(blue signal, ~7900 RFU)
35-bp peak (~1380)
Run folder (selected)
Examining the Size Quality Data
Overview
In this procedure, you will examine the sizing data for the project and troubleshoot the samples that did not size correctly. In general, it is good practice to review the sizing data following each analysis. When analyzing samples with high background or low signal intensity, the size calling algorithm can miscall peaks.Troubleshooting
Techniques in
This Section
This section presents three common solutions to resolve sizing errors. Sizing problems are common to fragment analysis applications and can usually be remedied using the techniques described in this guide.
■ Sizing Solution #1: Adjusting the Analysis Method. . . .42
■ Sizing Solution #2: Manually Correcting Miscalled Peaks. . .44
■ Sizing Solution #3: Modifying the Size Standard Definition .47
Note: For more information on troubleshooting sizing errors and
other problems, see the GeneMapper® Software Version 4.1
Reference and Troubleshooting Guide (PN 4403673).
About the
SQ PQV
The SQ PQV displayed in the Samples tab of the of the GeneMapper window reports the result of the sizing quality metric for each sample. The metric gauges the similarity between the fragment pattern defined by the size standard definition assigned to the sample and the actual distribution pattern of size standard peaks in the sample data. The sizing quality metric yields a value between 1 and 0 that represents a combination of statistical measures for the size calling method used to perform the analysis. Based on the PQV Threshold settings of the Quality Flags tab (see page 28), the software displays (Pass), (Check), or (Low Quality) to indicate the result of the sizing quality calculation.
Note: When performing size calling using the Classic sizing method, the
software cannot determine sizing quality so SQ is always (Check).
Note: The GeneMapper Software does not complete the analysis of
Examining the
Sizing Data
1.
In the GeneMapper window, select EditSelect All.Note: Alternatively, you can select individual samples by
pressing the Ctrl or Select key and clicking sample files.
2.
Click (AnalysisSize Match Editor) to view the sizing information for the selected samples.3.
In the Size Match Editor dialog box, select the first sample in the Navigation Pane to display the associated sizing data.4.
In the Size Matches tab, drag the mouse cursor ( ) across the 1000 to 2000 region of the x-axis to magnify the plot.5.
Magnify the y-axis of the plot to display data below 1000 RFU:a. Select ViewY-Axis ScaleScale to.
b. In the Enter Maximum Y-Axis Value dialog box, type 1000
then click OK.
Note: Alternatively, you can also magnify the y-axis of the plot
the same way you adjusted the x-axis by dragging the mouse cursor across the desired region.
6.
Scroll the plot horizontally, and observe:• Uncalled 35-bp peak – The software incorrectly identified the off-scale peak as the 35-bp peak for the size standard. • Sizing Quality Value – Displays ≤0.45 indicating that the metrics of the size calling method used to create the sizing calling curve (the Local Southern Method) indicate a poor fit.
7.
Adjust the analysis method to resolve the miscalled peak as explained on page 42.Sample (selected)
Sizing Quality value
Miscalled Off-scale peak
Uncalled 35-bp peak
Sizing Solution #1: Adjusting the Analysis Method
Overview
In this procedure, you will modify the Analysis Range settings of the analysis method to exclude the primer peaks that interfere with the sizing of the example data set. Although this technique corrects the sizing errors, it excludes all fragment data from the analysis that occur prior to the Start Point setting (see step 6).Modifying the
Analysis Method
1.
In the Navigation Pane of the Size Match Editor dialog box, select AFLP_sample_A01_001_2004-11-22.fsa.2.
Magnify the plot, and note the positions (in data points) of the primer peak and the 35-bp peak.3.
Click OK to close the Size Match Editor.4.
In the Samples tab, double-click any cell in the Analysis column to edit the AFLP Tutorial analysis method.5.
In the Analysis Method Editor, select the Peak Detector tab.6.
In the Ranges settings of the Peak Detector tab:a. Select AnalysisPartial Range. b. In the Start Pt. field, type 1325.
Leave all other settings the same.
Note: The goal in configuring the Analysis range is to set the
Start Pt setting so that the software begins the analysis immediately after the primer peak and prior the 35-bp peak.
Primer peak (~1265) 35-bp peak (~1380)
7.
Click OK to save the analysis method.Note: When an analysis method is modified, the changes affect
all samples that are assigned the method (including those in other projects). For all affected samples, the software displays in the status column indicating that the software requires reanalysis.
8.
Click (AnalyzeAnalyze) to reanalyze the data.9.
Click (AnalysisLow Quality to Top) to sort the data.10.
In the Samples tab, scroll horizontally and observe that the SQ values for all samples display (Pass), indicating that the software has sized the samples correctly.By starting the analysis at data point 1325, you have excluded the primer peaks from the sizing process. Consequently, the software appears to have correctly identified the 35-bp peaks and has sized the samples successfully.
IMPORTANT! Although the software displays (Pass) for all
samples, you should manually inspect the sizing data again to verify that all of the miscalled peaks have been called correctly.
Sizing Solution #2: Manually Correcting Miscalled Peaks
Overview
In this procedure, you will correct the miscalled 35-bp peak forAFLP_sample_H02_016_2004-11-22.fsa so that the sample sizes correctly. This technique is effective for correcting the sizing errors of individual samples, but is generally impractical when dealing with recurring sizing problems that affect multiple samples.
Reviewing the
Corrected
Sizing Data
1.
In the GeneMapper window, select EditSelect All to select all of the samples.2.
Click (AnalysisSize Match Editor) to view the sizing information for the selected samples.3.
In the Navigation Pane of the Size Match Editor dialog box, select AFLP_sample_H01_015_2004-11-22.fsa, then magnify the plot and observe the miscalled 35-bp peak (see Figure 3-11).About the Miscalled Peak
The AFLP_sample_H01_015_2004-11-22.fsa sample emphasizes the importance of manually reviewing the sizing data of AFLP projects following each analysis. Although the sample displays (Pass) in the SQ column of the Samples table, the plot of the Size Match Editor shows that the software has miscalled the 35-bp peak by applying the label to the shoulder of the primer peak.
In this example, you could remedy the problem by increasing the Analysis Range Start Point setting (see step 6 on page 42). However, if only one or two samples in the project contain miscalled peaks, a better solution might be to manually correct the labels as described in this procedure.
Figure 3-11 Miscalled Peak of AFLP_sample_H01_015_2004-11-22.fsa
Miscalled peak
Note: Because of the miscalled peak’s proximity to the 35-bp peak, the
sample yields a Sizing Quality value of 0.8099. The software displays (Pass) in the SQ column because the Sizing Quality value is greater than the Pass Range threshold setting for the Sizing Quality PQV of the analysis method (see “PQV Thresholds” on page 28).
Correcting the
Miscalled Peak
1.
In the Navigation Pane of the Size Match Editor dialog box, select AFLP_sample_H01_015_2004-11-22.fsa to display the associated sizing data.2.
Remove the label from the miscalled 35-bp peak:a. Select the peak with the 35-bp label (click inside the body
of the peak to select it).
b. Right-click the peak, and select Delete (EditDelete Size
Label).
3.
Apply the 35-bp label to the correct peak:a. Select the correct 35-bp peak.
b. Select EditAdd Size Label (or right-click the peak, and
select Add).
c. In the Select Size dialog box, double-click 35.0 to apply the
35-bp label to the selected peak.
4.
Click (ToolsCheck Sizing Quality) to verify that the sample sizes correctly. Observe that the Sizing Quality value for the sample is now 0.8969.5.
Click Apply to save the changes, then click OK.IMPORTANT! You must click Apply to reanalyze the sample.
6.
In the Samples tab, observe the following in the row for sample AFLP_sample_H02_016_2004-11-22.fsa:• Status column (not shown below) – Displays (Reanalyze) indicating that the sample must be analyzed again for the changes to take effect.
• SQI column – Displays indicating that the sizing settings for the sample have been modified manually. • SQ column – Displays (Pass) indicating that the
software sized the sample correctly.
7.
Click (AnalyzeAnalyze).8.
Modify the size standard definition to resolve the sizing problem as explained on page 47.Sizing Solution #3: Modifying the Size Standard Definition
Overview
In this procedure, you will create a custom size standard definition toaccommodate the miscalled 35-bp peak that interferes with the sizing of all samples in the example data set. This technique is an alternative to
“Sizing Solution #1: Adjusting the Analysis Method” on page 42. Unlike the first solution, which excludes fragment data from the analysis, this solution allows you to retain the full analysis range. However, because it entails the removal of the 35-bp peak and therefore a data point from the size calling curve, it can affect size calling accuracy.
About the
Custom Size
Standard
Before the GeneMapper Software can size fragment analysis data, it must contain information about the size standard that was run with the associated samples. The size standard definition used by the software supplies two crucial pieces of information: the color of the dye associated with the size standard, and the sizes (in bp) of the fragments that comprise it. The definitions for all Applied Biosystems size standards install automatically with the GeneMapper Software. If you use a third-party size standard or consistently encounter sizing failures for one or more peaks, you may need to create your own as explained in this section.
The size standard definition that you create will not contain the 35-bp peak present in the GS500(-250)ROX definition because the peak is obscured by the neighboring primer peak in the example data set.
Figure 3-12 500-bp size standard run with the example data (shaded region represents the 50- to 500-bp Analysis/Sizing Range)
