AFLP System Analysis Getting Started Guide

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1 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide Getting Started Setting Up the Analysis Analyzing and Examining the Data Exporting and Printing the Analyzed Data

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3 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide Getting Started Setting Up the Analysis Analyzing and Examining the Data Exporting and Printing the Analyzed Data

4 For Research Use Only. Not for use in diagnostic procedures. Information in this document is subject to change without notice. APPLIED BIOSYSTEMS DISCLAIMS ALL WARRANTIES WITH RESPECT TO THIS DOCUMENT, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO THOSE OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. TO THE FULLEST EXTENT ALLOWED BY LAW, IN NO EVENT SHALL APPLIED BIOSYSTEMS BE LIABLE, WHETHER IN CONTRACT, TORT, WARRANTY, OR UNDER ANY STATUTE OR ON ANY OTHER BASIS FOR SPECIAL, INCIDENTAL, INDIRECT, PUNITIVE, MULTIPLE OR CONSEQUENTIAL DAMAGES IN CONNECTION WITH OR ARISING FROM THIS DOCUMENT, INCLUDING BUT NOT LIMITED TO THE USE THEREOF, WHETHER OR NOT FORESEEABLE AND WHETHER OR NOT APPLIED BIOSYSTEMS IS ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. NOTICE TO PURCHASER: DISCLAIMER OF LICENSE Purchase of this software product alone does not imply any license under any process, instrument or other apparatus, system, composition, reagent or kit rights under patent claims owned or otherwise controlled by Applied Biosystems, either expressly, or by estoppel. GeneMapper Software has not undergone specific developmental validation for human identification applications. Human identification laboratories analyzing single-source or parentage samples which choose to use GeneMapper Software for data analysis should perform their own developmental validation studies. The AFLP process is covered by patents owned by Keygene N.V. 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 ( Copyright The Apache Software Foundation. All rights reserved. This product includes software developed by the ExoLab Project ( exolab.org/). Copyright 2000 Intalio Inc. All rights reserved. JNIRegistry is Copyright 1997 Timothy Gerard Endres, ICE Engineering, Inc., Oracle is a registered trademark of Oracle Corporation. All other trademarks are the sole property of their respective owners. Copyright 2009, Applied Biosystems. All rights reserved. Part Number Rev. A 04/2009 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

5 Contents 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 About the Example Data AFLP System Analysis Workflow GeneMapper Software Terms Starting the Software and Logging In Using This Guide With Your Own Sample Files Alternatives to the Procedures in This Guide Chapter 2 Setting Up the Analysis 11 Overview Creating a Project Adding Sample Data to the Project Creating an Analysis Method Configuring the Allele Tab Settings Configuring the Peak Detector Tab Settings Configuring the Peak Quality Tab Settings Configuring the Quality Flags Tab Settings Applying the Analysis Parameters GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide v

6 Contents Chapter 3 Analyzing and Examining the Data 33 Overview Analyzing the Project Examining the Off-Scale Data Examining the Size Quality Data Sizing Solution #1: Adjusting the Analysis Method Sizing Solution #2: Manually Correcting Miscalled Peaks Sizing Solution #3: Modifying the Size Standard Definition.. 47 Examining the Analyzed Data Reviewing the Analyzed Data in the Genotypes Table Displaying the Peak Data in the Samples Plot Reviewing the Size Standard Concordance Visualizing Polymorphic Peaks Saving the Generated Panel and Bin Set Editing the Results Modifying the Marker Modifying a Bin Modifying Genotype Calls Completing the Analysis Chapter 4 Exporting and Printing the Analyzed Data 77 Overview Exporting Results and Objects Exporting Samples and Genotypes Tabs Exporting Plots and Graphics Exporting Data for Use in a Spreadsheet Exporting Projects and Reference Data Printing Project Data Index 85 vi GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

7 Preface How to Use This Guide Purpose of This Guide Audience Assumptions Text Conventions 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. 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. 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 ). You have a working knowledge of the Microsoft Windows operating system. This guide uses the following conventions: Bold indicates 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 File Open Spot Set. Right-click the sample row, then select View Filter View All. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide vii

8 Preface How to Obtain More Information 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 Software Warranty and License See the GeneMapper Software Version 4.1 Installation and Administration Guide (PN ) for safety information. See the GeneMapper Software Version 4.1 Installation and Administration Guide (PN ) for warranty and licensing information. viii GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

9 Preface How to Obtain More Information Related Documentation The following related documents are shipped with the software: GeneMapper Software Version 4.1 Installation and Administration Guide (PN ) 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 ), loss of hetereozygosity (LOH) analysis (PN ), AFLP system analysis (PN ), SNaPshot kit analysis (PN ), and SNPlex system analysis (PN ) 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 ) 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 ) 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 on page x. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide ix

10 Preface How to Obtain Support Send Us Your Comments Obtaining Information from the Online Help Applied Biosystems welcomes your comments and suggestions for improving its user documents. You can your comments to: 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 (Help Contents and Index if available) for more information. How to Obtain Support For the latest services and support information for all locations, go to 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 In addition, the Support page provides access to worldwide telephone and fax numbers to contact Applied Biosystems Technical Support and Sales facilities. x GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

11 Chapter 1 Getting Started Chapter 1 Getting Started This chapter covers: About Supported AFLP Reagent Chemistries About the Example Data AFLP System Analysis Workflow GeneMapper Software Terms Starting the Software and Logging In Using This Guide With Your Own Sample Files Alternatives to the Procedures in This Guide Chapter 2 Setting Up the Analysis Chapter 3 Analyzing and Examining the Data Chapter 4 Exporting and Printing the Analyzed Data GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 1

12 Chapter 1 Getting Started About Supported AFLP Reagent Chemistries 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. Savelkoul, P. H. M., Aarts, H. J. M., de Haas, J., Dijkshoorn, L., Duim, B., Otsen, M., Rademaker, J. L. W., Schouls, L., and Lenstra, J. A., J Clin Microbio, 1999, 37(10), GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

13 Chapter 1 Getting Started About Supported AFLP Reagent Chemistries 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 (see Figure 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). Figure 1-2 Preselective amplification GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 3

14 Chapter 1 Getting Started About Supported AFLP Reagent Chemistries 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. Figure 1-3 Selective amplification with fluorescent, 5 dyelabeled primer 4 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

15 Chapter 1 Getting Started About Supported AFLP Reagent Chemistries 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 Supported AFLP Chemistry Kits See the GeneMapper Software Version 4.1 Quick Reference Guide (PN ) for a list of compatible Applied Biosystems electrophoresis instruments and chemistries. 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 Applied Biosystems web site ( GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 5

16 Chapter 1 Getting Started About the Example Data 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 Example Panel and Bin Set 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. The panel and bin set used to analyze the example AFLP data set are generated automatically by the GeneMapper Software. See Automatic Panel Generation on page 18 for more information. 6 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

17 Chapter 1 Getting Started AFLP System Analysis Workflow AFLP System Analysis Workflow Analysis Workflow Figure 1-5 summarizes the process for studying band patterns from many AFLP samples simultaneously. 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. Analyze and Examine the Data 1. Analyze the project. 2. Examine the results. Chapter 3 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. Figure 1-5 Analyzing AFLP system data GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 7

18 Chapter 1 Getting Started GeneMapper Software Terms GeneMapper Software Terms Table 1-1 Term Common terms used in this guide Definition analysis parameters bin bin set kit marker panel RFU 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 A fragment size and dye color that define an allele. You typically create a bin for each possible allele associated with a marker A set of bins (allele definitions), typically specific to a set of experimental conditions A group of panels 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. A group of markers, typically specific to a set of experimental conditions Relative Fluorescent Units Starting the Software and Logging In Starting the GeneMapper Software 1. In the desktop, double-click GeneMapper v4.1 (Start All Programs Applied Biosystems GeneMapper 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. 8 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

19 Chapter 1 Getting Started Using This Guide With Your Own Sample Files 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 GeneMapper Software Online Help. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 9

20 Chapter 1 Getting Started Alternatives to the Procedures in This Guide Alternatives to the Procedures in This Guide Overview Using Autoanalysis to Set Up Projects Using the Command Line Interface to Set Up Projects This guide presents one of several possible solutions for analyzing AFLP data using the GeneMapper Software. Once you have completed 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. 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 ). 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 Installation and Administration Guide (PN ). 10 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

21 Chapter 2 Setting Up the Analysis Chapter 1 Getting Started This chapter covers: Overview Creating a Project Adding Sample Data to the Project Creating an Analysis Method Applying the Analysis Parameters Chapter 2 Setting Up the Analysis Chapter 3 Analyzing and Examining the Data Chapter 4 Exporting and Printing the Analyzed Data GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 11

22 Chapter 2 Setting Up the Analysis Overview Overview In This Chapter For More Information 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. 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 features. You can access the online help as described in Obtaining Information from the Online Help on page x. 12 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

23 Chapter 2 Setting Up the Analysis Creating a Project 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 ) 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 (File New Project). 3. In the New Project dialog box, select AFLP, then click OK. AFLP project setting 4. Add sample files to the project as explained on page 14. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 13

24 Chapter 2 Setting Up the Analysis Adding Sample Data to the Project 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 the GeneMapper 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 ) for more information on the Autoanalysis feature. Adding Samples 1. Click (File Add 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 >>. Samples to be added to the project Sample files for the AFLP Tutorial 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 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

25 Chapter 2 Setting Up the Analysis Creating an Analysis Method Creating an Analysis Method Overview About Analysis Methods Creating the Analysis Method 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. 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 applicationspecific. 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. 1. In the GeneMapper window, click (Tools GeneMapper Manager). 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. Analysis method type Name of 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. Creates a new analysis method GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 15

26 Chapter 2 Setting Up the Analysis Creating an Analysis Method Configuring the Allele Tab Settings About the Settings Configuring 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). 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. 4. In the Panel settings, select Generate panel using samples to configure the software to generate a panel automatically. For a detailed explanation of the feature, see Automatic Panel Generation on page GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

27 Chapter 2 Setting Up the Analysis Creating an Analysis Method 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. Peak Height (h) Example Call h <50RFU 50 RFU h <100 RFU h 100 RFU 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. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 17

28 Chapter 2 Setting Up the Analysis Creating an Analysis Method 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. Bin Set menu (inactive) Analyze Dyes settings Panel settings (configured for panel generation) Allele calling settings Normalization settings 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 18 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

29 Chapter 2 Setting Up the Analysis Creating an Analysis Method 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-Dermis ). 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 performing an initial analysis only of the parent samples. The panel generated from the initial analysis could then be used to analyze the full sample set. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 19

30 Chapter 2 Setting Up the Analysis Creating an Analysis Method A B User defined (UD) columns 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. Run folder (selected) Samples belonging to the 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 20 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

31 Chapter 2 Setting Up the Analysis Creating an Analysis Method 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. Delete Common Alleles function Peak Height Ratio setting 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. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 21

32 Chapter 2 Setting Up the Analysis Creating an Analysis Method 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. Run folder (selected) Samples belonging to the 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. 22 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

33 Chapter 2 Setting Up the Analysis Creating an Analysis Method Normalization Scope setting Normalization Method setting Figure 2-9 Allele calling normalization settings Configuring the Peak Detector Tab Settings About the Settings Configuring the Settings The settings of the Peak Detector tab determine the methods that the GeneMapper Software uses to detect and size the peak data. 1. Select the Peak Detector tab. 2. At the top of the Peak Detector tab, select Peak Detection Algorithm Advanced. 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. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 23

34 Chapter 2 Setting Up the Analysis Creating an Analysis Method Peak Detection Algorithm Analysis and Sizing Range settings Peak Detection Thresholds 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. 24 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

35 Chapter 2 Setting Up the Analysis Creating an Analysis Method 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 The Peak Amplitude Threshold settings define the minimum signal intensities that the software uses to identify peaks in the raw signal data of each dye color. During peak detection, the software compares the signal data of each sample to the threshold setting of the associated dye color. When the signal equals or exceeds the threshold value, the software evaluates the signal at that location as a potential peak. The Peak Amplitude Threshold settings can be particularly important when analyzing AFLP samples because of the dramatic variation in the peak heights of AFLP data. Depending on chemistries used to generate the AFLP fragments, the peak heights of AFLP sample data can vary widely. While the software does compensate for signal variation to some extent, you may need to adjust the Peak Amplitude Threshold settings for optimal performance. For each color, the threshold setting should be low enough to detect the weakest peaks, but high enough above the background to eliminate noise. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 25

36 Chapter 2 Setting Up the Analysis Creating an Analysis Method Configuring the Peak Quality Tab Settings About the Settings Configuring 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. 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 neighboring dyes. For example, if a given peak occurs at data point 3760 and the Pull-Up Scan setting is 1, then software tests data points 3759 to 3761 in neighboring dye signals for pull-up peaks. 26 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

37 Chapter 2 Setting Up the Analysis Creating an Analysis Method Configuring the Quality Flags Tab Settings About the Settings Configuring the Settings The Quality Settings tab contains the weights and threshold values for the PQV. 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 on page 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 Guide (PN ). GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 27

38 Chapter 2 Setting Up the Analysis Creating an Analysis Method 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. 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 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: SQ/GQ Value (v) Icon Displayed in the SQ/GQ Column 0.75 v (Pass) 0.25 < v <0.75 (Check) v 0.25 (Low Quality) 28 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

39 Chapter 2 Setting Up the Analysis Applying the Analysis Parameters 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 ) 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 Setting AFLP Default to apply the default AFLP table setting to the new project. AFLP Default table setting GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 29

40 Chapter 2 Setting Up the Analysis Applying the Analysis Parameters 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 Analysis Method, Panel, and Size Standard columns. 3. Select Edit Fill 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. Column headings Content is filled down 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. 30 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

41 Chapter 2 Setting Up the Analysis Applying the Analysis Parameters 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 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. 5. When finished viewing the Info tab, select the project folder to display the Samples/Genotypes tab. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 31

42 Chapter 2 Setting Up the Analysis Applying the Analysis Parameters 32 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

43 Chapter 3 Analyzing and Examining the Data Chapter 1 Getting Started This chapter covers: Overview Analyzing the Project Examining the Off-Scale Data Examining the Size Quality Data Examining the Analyzed Data Saving the Generated Panel and Bin Set Chapter 2 Setting Up the Analysis Editing the Results Completing the Analysis Chapter 3 Analyzing and Examining the Data Chapter 4 Exporting and Printing the Analyzed Data GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 33

44 Chapter 3 Analyzing and Examining the Data Overview Overview In This Chapter Where You Are in the Procedure For More Information 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. 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. 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 contains comprehensive descriptions of all features. You can access the online help as described in Obtaining Information from the Online Help on page x. 34 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

45 Chapter 3 Analyzing and Examining the Data Analyzing the Project Analyzing the Project Overview About the Automatic Panel Generation In this procedure, you will analyze the project and conduct a preliminary review of the PQV in the Samples and Genotypes tabs. 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 alleles (see step 6 on page 17), the software does not generate bins for peaks that are common to all samples. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 35

46 Chapter 3 Analyzing and Examining the Data Analyzing the Project Analyzing the Project 1. Click (Analyze Analyze) 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 (Analysis Low 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. Genotypes tab (active) Status bar Check flag Off-Scale PQV Sizing Quality PQV 36 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

47 Chapter 3 Analyzing and Examining the Data Analyzing the Project 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_). Generated panel Generated marker Generated panel Generated marker 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. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 37

48 Chapter 3 Analyzing and Examining the Data Examining the Off-Scale Data 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 Reviewing the Raw Data for Offscale Peak(s) 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. 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) Raw data of the selected sample 38 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

49 Chapter 3 Analyzing and Examining the Data Examining the Off-Scale Data 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 dyelabeled 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 bp peak (~1380) Off-scale peak (blue signal, ~7900 RFU) Off-scale peak (red signal, ~8200 RFU) 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. Run folder (selected) Samples and Genotypes tabs displayed 8. Troubleshoot the Size Quality data as explained on page 40. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 39

50 Chapter 3 Analyzing and Examining the Data Examining the Size Quality Data Examining the Size Quality Data Overview Troubleshooting Techniques in This Section 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. 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 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 ). 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 samples that fail the sizing quality test (samples that display ). 40 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

51 Chapter 3 Analyzing and Examining the Data Examining the Size Quality Data Examining the Sizing Data 1. In the GeneMapper window, select Edit Select All. Note: Alternatively, you can select individual samples by pressing the Ctrl or Select key and clicking sample files. 2. Click (Analysis Size 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 View Y-Axis Scale Scale 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. Sample (selected) Sizing Quality value Miscalled Off-scale peak Sizing data for the selected sample Uncalled 35-bp peak 7. Adjust the analysis method to resolve the miscalled peak as explained on page 42. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 41

52 Chapter 3 Analyzing and Examining the Data Examining the Size Quality Data Sizing Solution #1: Adjusting the Analysis Method Overview Modifying the Analysis Method 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). 1. In the Navigation Pane of the Size Match Editor dialog box, select AFLP_sample_A01_001_ fsa. 2. Magnify the plot, and note the positions (in data points) of the primer peak and the 35-bp peak. Primer peak (~1265) 35-bp peak (~1380) 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 Analysis Partial Range. b. In the Start Pt. field, type Leave all other settings the same. Analysis Range (Partial Range) 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. 42 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

53 Chapter 3 Analyzing and Examining the Data Examining the Size Quality Data 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 (Analyze Analyze) to reanalyze the data. 9. Click (Analysis Low 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. 11. Verify that the software sized the miscalled peaks correctly as explained on page 44. Status bar (Analysis Complete) Genotypes tab (active) PQV Columns (all passed) GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 43

54 Chapter 3 Analyzing and Examining the Data Examining the Size Quality Data Sizing Solution #2: Manually Correcting Miscalled Peaks Overview Reviewing the Corrected Sizing Data In this procedure, you will correct the miscalled 35-bp peak for AFLP_sample_H02_016_ 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. 1. In the GeneMapper window, select Edit Select All to select all of the samples. 2. Click (Analysis Size 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_ 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_ 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. Sizing Quality value Miscalled peak 35-bp peak Figure 3-11 Miscalled Peak of AFLP_sample_H01_015_ fsa 44 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

55 Chapter 3 Analyzing and Examining the Data Examining the Size Quality Data Note: Because of the miscalled peak s proximity to the 35-bp peak, the sample yields a Sizing Quality value of 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_ 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 (Edit Delete Size Label). Miscalled peak (selected) 3. Apply the 35-bp label to the correct peak: a. Select the correct 35-bp peak. b. Select Edit Add 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 (Tools Check Sizing Quality) to verify that the sample sizes correctly. Observe that the Sizing Quality value for the sample is now GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 45

56 Chapter 3 Analyzing and Examining the Data Examining the Size Quality Data Sizing Quality value (corrected) 35-bp peak (corrected) 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_ 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. SQI PQV (modified) SQ PQV (corrected) 7. Click (Analyze Analyze). 8. Modify the size standard definition to resolve the sizing problem as explained on page GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

57 Chapter 3 Analyzing and Examining the Data Examining the Size Quality Data Sizing Solution #3: Modifying the Size Standard Definition Overview About the Custom Size Standard In this procedure, you will create a custom size standard definition to accommodate 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. 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. 35-bp peak (excluded) 250-bp peak (excluded) Figure bp size standard run with the example data (shaded region represents the 50- to 500-bp Analysis/Sizing Range) GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 47

58 Chapter 3 Analyzing and Examining the Data Examining the Size Quality Data Creating a Custom Size Standard 1. In the GeneMapper window, click (Tools GeneMapper Manager). 2. In the GeneMapper Manager, select the Size Standards tab. 3. Click New. Note: Alternatively, you can create a size standard by selecting New Size Standard from the pull-down menu of the first sample in the Size Standard column of the Samples tab. Size Standard column pull-down menu 4. In the Select Dye and Analysis Method dialog box, select Basic or Advanced, then click OK. 5. In the Size Standard Editor dialog box: a. In the Name field, type AFLP50-500(-250) b. Select Size Standard Dye Red. c. In the Size Standard Table, type the following size values: 50, 75, 100, 139, 150, 160, 200, 300, 340, 350, 400, 450, 490, and 500 (press Enter after typing the last value). IMPORTANT! After typing the last value of 500, you must press Enter to ensure that the final value is included in the definition. IMPORTANT! The values for the Analysis Range and Sizing Range defined in the Allele and Peak Detector tabs of the Analysis Method must match the peak range defined by the associated size standard. 48 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

59 Chapter 3 Analyzing and Examining the Data Examining the Size Quality Data Note: The 35- and 250-bp peaks have intentionally been excluded from the size standard definition (see Creating a Custom Size Standard on page 48 for an explanation). Size standard name Size standard dye channel Fragment sizes for the size standard 6. Click OK to save the size standard definition. 7. Click Done to close the GeneMapper Manager. Applying the Custom Size Standard 1. Reset the analysis range of the analysis method: a. In the Samples tab, double-click any cell in the Analysis column to edit the AFLP Tutorial analysis method. b. In the Analysis Method Editor, select the Peak Detector tab. c. In the Ranges settings, select Analysis Full Range. d. Click OK to save the analysis method and close the Analysis Method Editor. Analysis Range (Full Range) GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 49

60 Chapter 3 Analyzing and Examining the Data Examining the Size Quality Data 2. In the GeneMapper window, select the Samples tab. 3. Click the cell in the Size Standard column and select AFLP50-500(-250) (created on page 48). 4. Apply the selections to the other samples: a. Select the column heading for the Size Standard column. b. Select Edit Fill Down (or press Ctrl + D) to apply the selections to all samples. Column headings Content has been filled down 5. Click (Analyze Analyze) to begin the analysis. 6. Scroll the Samples table horizontally, and observe the PQV columns (SFNF, MNF, SNF, OS, and SQ) display (Pass). The GeneMapper Software has successfully sized the example data. 7. (Optional) Review the sizing calls in the Size Match Editor. 8. Examine the analyzed data as explained on page GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

61 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data Examining the Analyzed Data Overview Where You Are in the Procedure In this procedure, you will examine the analyzed data using the features and tools of the GeneMapper Software. The process for reviewing analyzed data is case-specific, and the order of the tasks depends on the individual needs of each user. Consequently, the order presented in this document is for illustration only. The major tasks in reviewing the analyzed data include: 1. Reviewing the analyzed data and PQV in the Genotypes tab (see page 52). 2. Displaying the analyzed data in the Samples Plot (see page 54). 3. Reviewing the concordance of the size standard (and replicate) peak data in the Samples Plot (see page 58). In this section you will learn to perform a formal review of the genotypes called by the software. You will use the tools of the GeneMapper Software to confirm the generated binary data and make corrections to the calls and bins. Following the analysis, you will learn to edit and export the panel and bin set generated by the software for use in other projects. In the previous procedure, you used the Size Match Editor to conduct a preliminary review of the sizing calls made by the software. After correcting the sizing errors, you can now view the Genotypes table with some confidence in the peak sizing and binary scores made by the GeneMapper Software. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 51

62 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data Reviewing the Analyzed Data in the Genotypes Table Overview Reviewing the Generated Columns and PQV of the Genotypes Tab In this procedure, you will review the contents of the table in the Genotypes tab. Following the analysis, the software activates the Genotypes tab and populates it with the genotype data and the results of the PQV tests. 1. In the GeneMapper window, select the Genotypes tab, and observe that the Navigation Pane and the Panel and Marker columns displays the panel (_Internal_Panel_) and marker (_Internal_Marker_Dye_Blue_) generated by the software. 2. Scroll the Genotypes table horizontally and observe the columns generated by the software for the bin set. The software displays one column of each of the following types for every bin. Column Category Allele Size Height Peak Area Data Point If a sample contains a peak that occurs in the associated bin, the cell Displays the label of the allele according to the Label settings of the analysis method (see Configuring the Allele Tab Settings on page 16). Displays the calculated size (in bp) of the associated peak. Displays the height (in RFU) of the associated peak. Displays the calculated area underneath the associated peak. Displays the data point that defines the center of the associated peak in the sample data. Example 3. Click (Analysis Low Quality to Top) to sort the data. 4. Scroll the Genotypes table to the right and observe the PQV columns for the Genotypes tab (AE, OS, SPU, AN, BD, and GC). Similar to the PQV columns of the Samples tab, the software displays (Pass), (Check), or (Low Quality) in each row to indicate the success of the operation defined by the PQV for the associated sample. 52 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

63 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data OS column All samples display (Pass) indicating that the software did not detect off-scale peaks in the analyzed data. Although the samples of the example data set contain offscale primer peaks, the software displays (Pass) for all samples because the peaks occur outside of the limits defined by the Analysis Range settings of the analysis method. GQ column All samples display (Pass) indicating that the samples passed the PQV tests and achieved Genotype PQV greater than the Pass Range value defined in the analysis method (see PQV Thresholds on page 28). PQV columns 5. Examine the data in the Samples Plot as explained on page 54 Note: For more information on any column of the Samples or Genotypes tab, see the GeneMapper Software Online Help (see Obtaining Information from the Online Help on page x). Note: For more information on troubleshooting failures of the Genotype tab PQV tests, refer to the GeneMapper Software Version 4.1 Reference and Troubleshooting Guide (PN ). GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 53

64 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data Displaying the Peak Data in the Samples Plot Overview Comparing the Samples and Genotypes Plots In this procedure, you will display the samples of the analyzed project in the Samples Plot. The Samples Plot provides several useful features that allow you to: Verify the concordance of the size standard peaks for the samples. Verify and edit the genotypes called by the GeneMapper Software. Modify the panel and bin set used to perform the analysis. The GeneMapper Software provides the Samples and Genotypes Plots for visualizing the peak data of analyzed projects. In regard to AFLP analyses, both plots provide very similar information. This guide explains the use of the Samples Plot for reviewing the analyzed AFLP sample data because it offers several features not found in the Genotypes Plot. Note: For more information on the Samples or Genotypes Plots, see the GeneMapper Software Online Help (see Obtaining Information from the Online Help of page x). Customizing the Plot Settings You can customize the appearance of the Samples and Genotypes Plots by creating your own plot settings. For this guide, you will use the default plot setting for AFLP projects (called AFLP Default) that installs with the software. When examining your own data, you may want to create your own plot setting to customize the plot information. Note: For more information on creating plot settings, click (Help Contents and Index) to open the GeneMapper Software Online Help. Displaying the Samples Plot IMPORTANT! You must be viewing the Samples tab to open the Samples Plot. The type of plot (Genotypes or Samples) that the software opens depends on which tab is foremost in the GeneMapper window. 1. In the GeneMapper window, select the Samples tab. 2. In the Samples tab, select Edit Select All to select all of the samples displayed in the table. 54 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

65 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data 3. Click (Analysis Display Plots) to display the electropherograms for the samples. 4. Configure the Samples Plot settings for the AFLP data: a. Select Plot Setting AFLP Default to configure the plot to display only those plot features that are relevant to the analysis of AFLP data. b. Select Panes 2 to configure the software to display the electropherogram data from two samples simultaneously. c. Click (View Tables Genotypes Table) to display the Genotypes table in bottom half of the Samples Plot. 5. Magnify the both plots to display the data between 50 and 100 bp: a. Right-click the x-axis of the upper plot, then select Zoom to. b. In the X-Axis Zooming dialog box, type 50 and 100 in the minimum and maximum fields, then click OK. c. Magnify the lower plot to 50 and 100 bp. 6. Magnify the plots to display the data below 500 RFU: a. Right-click the y-axis of the upper plot, then select Zoom to. b. In the Y-Axis Zooming dialog box, type 500 in the Zoom To field, select Apply to all electropherograms, then click OK. 7. Scroll the plot horizontally, and observe: Bins The greyed regions of the plot represent the bins generated from the example data. The software does not display bins for all peaks because the Delete common alleles function is active (see step 6 on page 17). Allele Labels For each peak that occurs within a bin, the software displays a label below the peak that contains the name of the allele, the calculated size of the fragment (in bp), and the calculated height of the peak (in RFU). Marker Range/Range Indicators The bar at the top of the plot represents the marker range specified in the Analysis Method in the Alleles tab. The software displays the maximum and minimum limits of the marker as a set of indicators ( ) at either side of the plot. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 55

66 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data Peak Selection mode By default, the Samples Plot opens in Peak Selection mode ( ). Consequently, when you select a peak, the software highlights the peak and provides the options for manipulating it. Note: Alternatively, you can modify bins from the Samples Plot by clicking to place the software in Binning Mode. Range indicators (shown) Marker Range (shown) Header (shown) Allele Changes (shown) Peak Selection Mode Horizontal Labels (shown) Bins (shown) Sample Plot (x-axis in bp, y-axis in RFU) Marker range Bin (grey bar) Range indicator Horizontal allele labels Genotypes Table (binary data) 56 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

67 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data About the Bins and Genotype Calls of the Samples Plot Observe the following while viewing the electropherogram data: Called Peaks According to the height of each detected peak, the software applies one of the three calls (0, Check, and 1) (configured in step 5 on page 17). Generated Bins The software created bins for all peaks except: Peaks outside of the range defined by the Analysis Range settings of the analysis method. Peaks common to all samples in the project (see A in Figure 3-13). Because the Delete common alleles option is selected (see step 6 on page 17), the software did not generate bins for peaks that are present in all samples.) Marker Range Length The length of the marker (displayed at the top of each electropherogram) is determined by the Analysis Range settings of the analysis method. Because the default range is 50 to 500 bp, software created a marker that begins at 50 bp and ends at 500 bp. Uncalled peaks The software genotyped all samples except: Peaks outside of the range defined by the Analysis Range settings of the analysis method. Peaks that are common to all samples in the project. Peaks below the B(lue) Peak Amplitude Threshold setting of the analysis method. A A A Figure 3-13 Called and uncalled peaks of the Samples Plot GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 57

68 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data Reviewing the Size Standard Concordance Overview In this procedure, you will review the concordance (agreement) of the size standard peak data for the samples of the example project. This step is optional but recommended since it allows you to visualize deviations in the peak positions of the size standard and replicates samples in a project. Note: If running replicates, you can evaluate the concordance of the samples by selecting and overlying the data for the replicate samples as explained in the following procedure (without step 2). Overlaying the Size Standard Data 1. In the Samples Plot, select Panes Configure the dye settings so that the Samples Plot displays data from the red dye signal: a. Select (Red signal) (View Dyes Red Dye). b. Deselect (Blue signal) (View Dyes Blue Dye). 3. Click (View Tables Sizing Table) to display the Sizing table in bottom half of the Samples Plot. 4. Click (View Plots Overlay All) to overlay the red dye signals for all samples of the project. 5. Magnify the plot to display the data between 25 and 125 bp: a. Right-click the x-axis of the plot, then select Zoom to. b. In the X-Axis Zooming dialog box, type 25 and 125 in the minimum and maximum fields, then click OK. 6. Magnify the plot to display the data below 500 RFU: a. Right-click the y-axis of the plot, then select Zoom to. b. In the Y-Axis Zooming dialog box, type 500 in the Zoom To field, then click OK. 58 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

69 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data 7. Scroll the plot horizontally and observe the uniformity of the size standard peaks. When the software sizes all samples correctly, the Samples Plot displays a tight cluster of overlaid peaks for each size standard fragment. If the software miscalls the size standard for one or more samples, the effect can be evident in the overlaid sizing data of the Sample Plot. 8. Select File Close Plot Window (or press Esc) to close the plot, then visualize polymorphisms as explained on page 60. Selected size standard peaks Associated rows for selected peaks GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 59

70 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data Visualizing Polymorphic Peaks Overview Overlaying the Size Standard Data In this procedure, you will review an overlay of the AFLP sample data to visualize polymorphic peaks and to visually confirm the genotype calls made by the software. 1. In the Samples tab, select Edit Select All to select all of the samples displayed in the table. Note: In practice, you may want to select and review batches of samples instead of all at samples once to avoid cluttering the plot. 2. Click (Analysis Display Plots). 3. Configure the Samples Plot settings for the AFLP data: a. Click (View Tables Genotypes Table) to display the Genotypes table. b. Click (View Plots Overlay All) to overlay the blue dye signals for the selected samples of the project. c. Click (View Bins) to hide the bins. 4. Magnify the plot to display the data between 50 and 75 bp: a. Right-click the x-axis of the plot, then select Zoom to. b. In the X-Axis Zooming dialog box, type 50 and 75 in the minimum and maximum fields, then click OK. 5. Magnify the plot to display the data below 800 RFU: a. Right-click the y-axis of the plot, then select Zoom to. b. In the Y-Axis Zooming dialog box, type 800 in the Zoom To field, then click OK. 6. Configure the software to display the sample electropherograms in multiple colors so they become more distinguishable: a. Select View Plot Colors Custom to display the sample electropherograms in multiple, custom colors. 60 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

71 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data b. (Optional) Select View Legends to display or hide the legend for the custom color assignments. Note: To change one or more of the custom colors: click (Tools Plot Settings), select the Display Settings tab in the Plot Settings Editor, then click (Custom Colors). Note: You can customize the color of any sample by doubleclicking the corresponding color box and selecting the desired color. 7. Scroll the plot horizontally, and scan the electropherograms for polymorphic peaks. 8. Without closing the plot, go to Editing the Results on page 69 to learn how to modify the allele calls, bin set, and panel settings. Note: Modifications to the panels and bins are saved only if the panel is exported and then imported into the Panel Manager. For more information, go to Saving the Generated Panel and Bin Set on page 63. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 61

72 Chapter 3 Analyzing and Examining the Data Examining the Analyzed Data Blue Dye (selected) Overlay All (selected) Genotypes Table (selected) Selected peak (potential polymorphism) Row of the selected peak 62 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

73 Chapter 3 Analyzing and Examining the Data Saving the Generated Panel and Bin Set Saving the Generated Panel and Bin Set Overview Saving the Generated Panel In this procedure, you will save the panel and bin set generated by the GeneMapper Software, modify them (optional), then apply the panel and bin set to the AFLP Tutorial project for an analysis. If you intend to reuse a panel and bin set generated by the GeneMapper Software, perform the procedures in this section to save the panel and bin set and apply them for use by other projects. 1. Select File Close Plot Window (or press Esc) to close the Samples Plot. 2. In the GeneMapper window, select File Export Project Panel. 3. In the Specify Names dialog box, enter the following: a. In the Chemistry Kit Name field, type AFLP Tutorial Kit. b. In the Panel Name field, type AFLP Tutorial Panel. c. Click OK. 4. In the Select Export Folder dialog box, click Export to save the panel file to the default location. Note: By default, the software stores all panel files in the Panels folder (<drive>:\appliedbiosystems\genemapper\panels). Note: The software names the exported file automatically. 5. In the GeneMapper window, click (Tools Panel Manager). 6. Import the Panel file (AFLP Tutorial Kit_Panel.txt): a. In the Navigation Pane of the Panel Manager, select Panel Manager. b. Select File Import Panels. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 63

74 Chapter 3 Analyzing and Examining the Data Saving the Generated Panel and Bin Set c. In the Import Panels dialog box, select the AFLP Tutorial Kit_Panel.txt, then click Import. The Navigation Pane displays the AFLP Tutorial Kit, the AFLP Tutorial Panel, and the _Internal_Marker_Dye_Blue_ marker. Imported kit (selected) Imported panel Imported marker 7. Import the Bin Set file (AFLP Tutorial Kit_Bin.txt): a. In the Navigation Pane, select AFLP Tutorial Kit. b. Select File Import Bin Set. c. In the Import Panels dialog box, select the AFLP Tutorial Kit_Bin.txt, then click Import. 8. Click Apply to save the changes made to the AFLP Tutorial Panel and Bin Set. Note: The exported panel and marker files can be copied to other computers for use with the GeneMapper Software. Displaying the Bin Set 1. In the Navigation Pane of the Panel Manager: a. Expand AFLP Tutorial Kit (the imported kit). b. Select AFLP Tutorial Panel (the imported panel), and observe: Bin Set menu Displays the name of the imported bin set. Panel Manager table Displays information for the selected panel. Imported bin set Imported panel (selected) 64 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

75 Chapter 3 Analyzing and Examining the Data Saving the Generated Panel and Bin Set 2. Display the bins between 45 and 75 base pairs for the panel: a. In the upper Navigation Pane, expand AFLP Tutorial Panel, then select Blue (the imported marker). The plot displays the bin set for the Blue marker of the imported panel. b. Right-click the x-axis of the plot, then select Scale to. c. In the X-Axis Scale dialog box, type 45 and 75 in the Minimum and Maximum fields, then click OK. Bin name Imported marker (selected) Marker range handle Bin (52) Marker range (selected) 3. Scroll the plot horizontally, and observe: Bins The greyed regions of the plot that represent the bins generated from the example data. Marker Range/Range Handles The colored bar at the bottom of the plot represents the marker generated from the example data. The software represents the maximum and minimum limits of the marker with a set of handles (vertical lines) at either side of the plot. Note: The width of the marker is equal to the Analysis Range defined in the analysis method that you created (see Configuring the Peak Detector Tab Settings on page 23). GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 65

76 Chapter 3 Analyzing and Examining the Data Saving the Generated Panel and Bin Set (Optional) Modifying the Bin Set Note: This procedure is an alternative to modifying bins. It differs only in that you use the Panel Manager instead of the Samples Plot. 1. Create a bin at 54 bp: a. Right-click the Panel Manager plot, then select New Bin. b. Click the plot at 54 bp (approximately). c. In the Add Bin to Blue dialog box: In the Name field, type Custom. In the Location field, type 54. Marker to which the bin will be added Bin name Location of the bin in base pairs d. Click OK to create the bin. 2. Modify the Custom bin so that it spans 1 bp: a. Double-click the bin (or right-click it, and select Edit Bin). b. In the Left offset and Right offset fields of the Edit Bin dialog box, type 0.5, then click OK to apply the settings. Note: Alternatively, you can also adjust the size of the bin by selecting it, then dragging the bin handles to the desired positions. Custom bin (selected) Bin handle (Right offset) Bin location line 3. Right-click the Custom bin, then select Delete Bin to delete it. 4. Click OK to apply the changes and close the Panel Manager. 66 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

77 Chapter 3 Analyzing and Examining the Data Saving the Generated Panel and Bin Set Editing the Analysis Method for Use with the Generated Panel Before you can use the generated panel to analyze the project, you must modify the analysis method to use the generated bin set. In this procedure, you will configure the AFLP Tutorial analysis method to use the bin set you just imported, instead of regenerating the data. To modify the analysis method to use the generated bin set: 1. In the GeneMapper window, click (Tools GeneMapper Manager). 2. In the GeneMapper Manager, select the Analysis Methods tab. 3. Select the AFLP Tutorial, then click Open. 4. Select the Allele tab, and modify the settings: a. In the Panel settings, select Use specified panel so that the software uses the bin set displayed in the Bin Set menu. b. Select Bin Set AFLP Tutorial Kit_Bin_Set so that the software uses the bin set generated in the previous section. Generated bin set Use specified panel option 5. Click OK to save the AFLP Tutorial Analysis Method. 6. Click Done to close the GeneMapper Manager. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 67

78 Chapter 3 Analyzing and Examining the Data Saving the Generated Panel and Bin Set Applying the Generated Panel to the Project 1. In the GeneMapper window, select the Samples tab. 2. Apply the panel to the sample in the first row of the table: a. Click the first cell in the Panel column. b. In the Select a Panel dialog box, expand AFLP Tutorial Kit. c. Double-click AFLP Tutorial Panel. Generated panel 3. Apply the selections to the other samples: a. Select the column heading for the Panel column. b. Select Edit Fill Down (or press Ctrl + D) to apply the selections to all samples. Column headings Content has been filled down 4. Click (Analyze Analyze) to analyze the project using the AFLP Tutorial Panel. 68 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

79 Chapter 3 Analyzing and Examining the Data Editing the Results Editing the Results Overview In this procedure you will use the functions of the Samples Plot to modify the panel and bin set for the project, and to edit the genotype calls made by the software. Review the data until you find a peak or bin of interest and go to the appropriate page: Modifying the Marker Modifying a Bin Modifying Genotype Calls When you are finished modifying the peaks, bins, and marker of the Samples Plot, go to Saving the Generated Panel and Bin Set on page 63 to complete the analysis. Note: Since the controls of the Samples and Genotypes Plots are similar, the procedures in this section can also be performed using the Genotypes Plot. Modifying the Marker Modifying the Marker Range IMPORTANT! The Samples Plot must be in binning mode ( modify a marker. ) to 1. Click (View Plots Separate Dyes) to display the combined dye signals for each sample in separate plots. 2. Select Panes 2 to configure the plot to display the peak data for two samples simultaneously. 3. In the toolbar of the Samples Plot, click (Alleles Editing Mode Binning) to place the software in Binning mode. 4. In the top plot, click the _Internal_Marker_Dye_Blue_ marker indicator to select it. 5. If necessary, scroll the plot horizontally (to the left) to view the end of the marker. 6. At the bottom of the plot, click the marker indicator ( ) at the bottom of the plot. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 69

80 Chapter 3 Analyzing and Examining the Data Editing the Results 7. When the plot displays a red line above the marker indicator ( ), drag the indicator to the desired position. Marker indicator Mouse cursor Range indicator Modifying a Bin 8. Save the changes to the panel: a. Select File Save Panel. b. In the Save Panel dialog box, select Save Panel, then click OK. c. In the Save Panel message box, click OK. 9. Click (View Plots Overlay All) to return the plot to the overlaid view. Adding a Bin to the Bin Set IMPORTANT! The Samples Plot must be in binning mode ( modify a bin. ) to 1. Determine a region of the Samples Plot to add the bin. If necessary, scroll and magnify the plot to display the region. 2. Click (View Plots Separate Dyes) to display the combined dye signals for each sample in separate plots. 3. Select Panes 2 to configure the plot to display the peak data for two samples simultaneously. 4. In the toolbar of the Samples Plot, click (Alleles Editing Mode Binning) to place the software in Binning mode. 5. If necessary, scroll the Sample Plots vertically to display a sample of interest. Note: It is not necessary to select a specific plot to add a bin. 70 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

81 Chapter 3 Analyzing and Examining the Data Editing the Results 6. In the sample plot of interest, click the _Internal_Marker_Dye_Blue_ marker indicator to select it. 7. Create the bin: a. Right-click the plot and select Add Bin. b. Using the mouse, move the vertical line to the desired location (approximately), then click the plot. c. In the Add Bin dialog box, enter the name, location (in bp), and left and right offsets (also in bp) for the new bin. Marker indicator Location of the new bin d. Click OK to create the bin. Note: You can edit a bin by double-clicking it (or by rightclicking the selected bin, then selecting Edit Bin). GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 71

82 Chapter 3 Analyzing and Examining the Data Editing the Results 8. If necessary, manually adjust the bin offsets: a. Click the bin to select it. b. Drag the bin handles to the desired positions. Binning mode (on) Marker Blue (selected) Bin 37 (selected) Bin handle (Right offset) Bin location line Note: You can also delete bins by right-clicking a selected bin, then selecting Delete Bin. 9. Save the changes to the panel: a. Select File Save Panel. b. In the Save Panel dialog box, select Save Panel, then click OK. c. In the Save Panel message box, click OK. 10. Click (View Plots Overlay All) to return the plot to the overlaid view. 72 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

83 Chapter 3 Analyzing and Examining the Data Editing the Results Modifying Genotype Calls Selecting the Peak for Modification 1. Identify a peak of interest: a. Click (View Bins) to hide the bins. b. While viewing the overlaid data in the Samples Plot, scroll to and magnify the plot to display the peak of interest. c. Click the peak of interest to select it. 2. Click (View Plots Separate Dyes) to display the combined dye signals for each sample in separate plots. 3. Select Panes In the Samples Plot, click (Alleles Editing Mode Peak Selection) to place the software in Peak Selection mode. 5. If necessary, magnify the plot. 6. If necessary, scroll the Sample Plots vertically to display the plot for the sample identified in step Choose from the following, to: Add an allele call, see page 74 Change an allele call, see page 75 View the history of an allele call, see page 75 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 73

84 Chapter 3 Analyzing and Examining the Data Editing the Results Adding an Allele Call 1. Click inside the peak of interest to select it. 2. Right-click the selected peak, and select Add Allele Call. 3. In the Add Allele Comment dialog box, type a description of the change, then click OK. Peak Selection mode (on) Bin 37 Peak bp (selected) Horizontal allele label (peak bp) Data for selected peak (peak 37.19) 4. Click (View Plots Overlay All) to return the plot to the overlaid view. Note: Because you have modified the allele calls manually, the software displays greyed the PQV flags for the associated sample with the edited allele(s). 74 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

85 Chapter 3 Analyzing and Examining the Data Editing the Results Changing an Allele Call 1. Click inside the peak of interest to select it. 2. Right-click the peak, and select Rename Allele Custom. 3. In the Create Custom Allele Name dialog box, enter a new name for the allele, and click OK. 4. In the Edit Allele Comment dialog box, enter a brief comment, then click OK. Bin 37 Peak bp (selected) Horizontal allele label (peak bp) 5. Click (View Plots Overlay All) to return the plot to the overlaid view. Viewing an Allele History 1. Click inside the peak of interest to select it. 2. Click (View Plots Separate Dyes) to display the combined dye signals for each sample in separate plots. 3. Right-click the selected peak, and select History. 4. Observe that the Allele History. Click OK when finished. 5. Click (View Plots Overlay All) to return the plot to the overlaid view. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 75

86 Chapter 3 Analyzing and Examining the Data Completing the Analysis Completing the Analysis After the Analysis Repeating the Analysis Using Different Analysis Parameters After you finish reviewing the genotype data and are satisfied with the results, you can print or export the analyzed data as explained in Chapter 4. The remainder of this guide explains how to perform various operations for extracting the analyzed data from the GeneMapper Software for further use. However, before you proceed, consider repeating the analysis of the example data set as described below. To better understand the analysis performed by the GeneMapper Software, consider modifying the analysis parameters for the example project and repeating the analysis described in this guide. For example, by modifying the settings of the analysis method and reanalyzing the study, you can get a better understanding of how the settings of the Allele tab affect the resulting genotypes. IMPORTANT! Do not modify the default analysis methods, size standards, and other analysis parameters. 76 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

87 Chapter 4 Exporting and Printing the Analyzed Data Chapter 1 Getting Started This chapter covers: Overview Exporting Results and Objects Exporting Samples and Genotypes Tabs Exporting Plots and Graphics Exporting Data for Use in a Spreadsheet Exporting Projects and Reference Data Chapter 2 Printing Project Data Setting Up the Analysis Chapter 3 Analyzing and Examining the Data Chapter 4 Exporting and Printing the Analyzed Data GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 77

88 Chapter 4 Exporting and Printing the Analyzed Data Overview Overview In This Chapter For More Information In this chapter, you will learn to: Export analysis results and data objects Export the contents of the samples and genotypes tabs Export plots and graphics Export the data using the Report Manager Export projects and reference data Print project data Nearly all views, plots, and tables of the GeneMapper Software can be printed or exported for further use. This chapter describes how to perform the export 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 features of the user interface, the GeneMapper Software Online Help contains comprehensive descriptions of all features. You can access the online help as described in Obtaining Information from the Online Help on page x. 78 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

89 Chapter 4 Exporting and Printing the Analyzed Data Exporting Results and Objects Exporting Results and Objects Overview The GeneMapper Software can export the data displayed in the tabs of the GeneMapper windows as tab-delimited or comma-separated text. The exported files can be imported by most database and spreadsheet applications for further analysis and manipulation. Table 4-2 Exportable elements of the GeneMapper Software User Interface Element Exported Data/Objects How to export See Page GeneMapper Window Samples and/or Genotypes tables Specific columns of the Samples and/or Genotypes tables Basic export function 80 Report Manager 81 Samples Plot Electropherograms Screen capture 80 Genotypes Plot GeneMapper Manager Sizing or Genotypes table Basic export function Electropherograms Screen capture 80 Projects Analysis Methods Size Standard Definitions Table, Plot, and Report Settings GeneMapper Manager export functions Exporting Genotypes Table for Use by Spreadsheet Applications The GeneMapper Software provides the Report Manager as a solution to the column limitations of spreadsheet applications. Depending on the complexity of the AFLP banding patterns for a sample set, the exported Genotypes table of an AFLP project can contain more columns than are supported by some spreadsheet applications. For example, Microsoft Excel software supports no more than 256 columns in a sheet. The exported Genotypes table can sometimes produce errors when imported because the column count exceeds the limit. See Exporting Data for Use in a Spreadsheet on page 81 for more information. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 79

90 Chapter 4 Exporting and Printing the Analyzed Data Exporting Samples and Genotypes Tabs Exporting Samples and Genotypes Tabs Overview The software can perform a simple export of all the columns in the Samples/Genotypes tabs to a tab-delimited or comma-separated file. IMPORTANT! The text files created by the Export Table functions contain the entire table (or combined tables) and may be too large to import directly into a spreadsheet. If you are exporting the table data for use with a spreadsheet application, consider exporting the data as explained in Exporting Data for Use in a Spreadsheet on page 81. Exporting the Samples or Genotypes Tables 1. In the GeneMapper window, select the Samples tab or Genotypes tab containing the data that you want to export. 2. Export the table data by selecting the following commands: File Export Table Exports information displayed in the selected tab. File Export Combined Table Exports information displayed in both tabs. Note: The Export Combined Table option is active only when viewing the Samples tab. Exporting Plots and Graphics Overview Taking a Screenshot Using Alt-Print Screen The best method for exporting plots of the GeneMapper Software is through the use of a third-party screen capture utility, or by using the Ctrl-Print Screen function of the Windows operating system. The procedure for using the Alt-Print Screen function is explained below. 1. While viewing the plot, adjust the settings so that the plot appears as desired. 2. Press Alt-Print Screen to copy a bitmap image of the active window to the clipboard of the Windows operating system. 3. Paste the bitmap image directly into the desired application (such as Microsoft Word or Excel software). 80 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

91 Chapter 4 Exporting and Printing the Analyzed Data Exporting Data for Use in a Spreadsheet Exporting Data for Use in a Spreadsheet Overview The Report Manager allows you to generate reports from any combination of the columns that make up the Samples and Genotypes tables. Once a report is complete, you can save the report, print it, or export it as tab-delimited or comma-separated text. The Report Manager provides a solution to the column limitations of spreadsheet applications by allowing you to pivot the content of the Samples and Genotypes tables so that the columns fit vertically in a spreadsheet. Note: The Report Manager also allows you to configure the report with Calculation, Analysis, and Custom columns for performing basic, comparative mathematical manipulations of the report data. In addition, the software can save the settings used to create the report so that they can be used to generate the same report for other projects. Creating a Report Setting 1. Determine the number of alleles generated for the project: a. In the GeneMapper window, select the Genotype tab and scroll the table horizontally to display the last Allele column. b. Record the number of the last allele. 2. Click (Tools GeneMapper Manager). 3. In the GeneMapper Manager, select the Report Settings tab, then click New to open the Report Settings Editor. 4. In the Name field, type AFLP Tutorial. 5. In the Number of Alleles field, type the number of alleles determined in step Add the columns from the Genotypes table to the report setting: a. In the Available Columns settings, select the Genotype tab. b. Using the Ctrl and Shift keys, select the Sample File, Sample Name, Sample ID, and Allele 1 through Allele 59 rows. c. Click to add the columns to the report. d. Click OK to save the report setting. 7. Click Done to close the GeneMapper Manager. GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide 81

92 Chapter 4 Exporting and Printing the Analyzed Data Exporting Data for Use in a Spreadsheet Generating the Report 1. In the GeneMapper window, select Edit Select All to select all of the samples in the project. 2. Click (Analysis Report Manager). 3. In the Report Manager, select Report Setting AFLP Tutorial to apply the report setting to the report. Observe that the content of the report has been replaced with the binary data from the AFLP project. Note that the data is displayed horizontally, so it must be pivoted before use. 4. Select Edit Flip Table to pivot the table data (columns become rows and vice versa). Observe that the content of the report is now displayed vertically, so the data can be successfully imported into a spreadsheet application. 5. Export the report: a. In the Report Manager, select File Export Table. b. In the left pane of the Export Report dialog box, click Desktop to save the exported file to the desktop. c. In the File name field, type AFLP Tutorial. d. In the Export File As settings, select an export format: Tabdelimited text (.txt) or Comma-separated values (.csv). e. Click Export. 6. (Optional) In the Report Manager, save the report: a. Select File Save. b. In the Save dialog box, type AFLP Tutorial, then click OK. 7. In the Report Manager, select File Exit. 82 GeneMapper Software Version 4.1 AFLP System Analysis Getting Started Guide

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