GeneMapper® Software Version 4.1 Reference and Troubleshooting Guide (PN 4403673A)

GeneMapper

Software Version 4.1

Reference and

Troubleshooting Guide

Process Quality

Values and Basic

Troubleshooting

SNPlex™ System

Troubleshooting

Algorithms

GeneMapper

Software Version 4.1

Reference and

Troubleshooting Guide

Process Quality

Values and Basic

Troubleshooting

SNPlex™ System

Troubleshooting

Algorithms

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

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

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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, GeneScan, Primer Focus, and SNaPshot are registered trademarks,

and GeneScan 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.

rights reserved.

Oracle is a registered trademark of Oracle Corporation.

All other trademarks are the sole property of their respective owners.

Part Number 4403673 Rev. A

04/2009

Contents

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide iii

Preface v

How to Use This Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

How to Obtain More Information . . . . . . . . . . . . . . . . . . . . . . . . . . vi

How to Obtain Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

Chapter 1 Process Quality Values and Basic

Troubleshooting 1

Diagnosing and Resolving Basic Problems . . . . . . . . . . . . . . . . . . . 2

Common Troubleshooting Procedures . . . . . . . . . . . . . . . . . . . 5

ADO (Allele Display Overflow) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

AE (Allele Edit) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

AN (Allele Number) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

BD (Broad Peak) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

BIN (Out of Bin Allele) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

CC (Control Concordance) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

DP (Double Peak) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

GQ (Genotype Quality) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

LPH (Low Peak Height) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

MNF (Matrix Not Found) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

NB (Narrow Bin) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

OBA (One Basepair Allele) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

OS (Offscale) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

OVL (Overlap) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

PHR (Peak Height Ratio) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

SFNF (Sample File Not Found) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

SHP (Sharp Peak) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

SNF (Size Standard Not Found) . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Contents

iv GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

SP (Split Peak) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

SPA (Single Peak Artifact) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

SPU (Spectral Pull-Up) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

SQ (Sizing Quality) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

XTLK (Cross Talk) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Chapter 2 SNPlex

™

System Troubleshooting 33

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Chapter 3 Algorithms 35

Genotyping Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Peak Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Optimizing Peak Detection Sensitivity . . . . . . . . . . . . . . . . . . . . . 41

Example 1: Reducing Window Size . . . . . . . . . . . . . . . . . . . . 41

Example 2: Reducing Window Size/Increasing Polynomial Degree

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Example 3: Extreme Settings . . . . . . . . . . . . . . . . . . . . . . . . . 43

Slope Thresholds for Peak Start/End Parameters . . . . . . . . . . . . 44

Slope Threshold Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Size-Matching/Size-Calling Algorithm . . . . . . . . . . . . . . . . . . . . . . 46

Size-Calling Methods (Classic and Advanced Modes) . . . . . . . . . 47

Least Squares Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Cubic Spline Interpolation Method . . . . . . . . . . . . . . . . . . . . . 49

Local Southern Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Global Southern Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Allele-Calling Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Microsatellite Analysis Methods . . . . . . . . . . . . . . . . . . . . . . . 54

SNPlex

™

System Analysis Methods . . . . . . . . . . . . . . . . . . . . 55

Glossary 59

Index 67

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide v

Preface

How to Use This Guide

Purpose of This

Guide

This guide describes the function of the Process Quality Values

(PQV) for the supported analyses of the GeneMapper

Software,

explains the fundamental algorithms used by the software, and

provides basic troubleshooting techniques.

Audience This guide is intended for trained laboratory personnel. Applied

Biosystems is not liable for damage or injury that results from use of

this guide by unauthorized or untrained parties.

Assumptions This guide assumes that:

• You have installed GeneMapper

Software Version 4.1 as described

in the

GeneMapper

Software Version 4.1 Installation and

Administration Guide

(PN 4403614).

• You have a working knowledge of the Microsoft

Windows

operating system.

Te xt Co nv e nt io n s 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.

Preface

How to Obtain More Information

vi GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

User Attention

Words

Two user attention words appear in Applied Biosystems user

documentation. Each word implies a particular level of observation

or action as described below:

Note: Provides information that may be of interest or help but is not

critical to the use of the product.

IMPORTANT! Provides information that is necessary for proper

instrument operation, accurate chemistry kit use, or safe use of a

chemical.

Examples of the user attention words appear below:

Note: The size of the column affects the run time.

Note: The Calibrate function is also available in the Control

Console.

IMPORTANT! To verify your client connection to the database, you

need a valid Oracle user ID and password.

IMPORTANT! You must create a separate Sample Entry Spreadsheet

for each 96-well plate.

How to Obtain More Information

Safety

Information

See the

GeneMapper

Software Version 4.1 Installation and

Administration Guide (PN 4403614)

for safety information.

Software

Warranty and

License

See the GeneMapper

Software Version 4.1 Installation and

Administration Guide (PN 4403614) for warranty and licensing

information.

Preface

How to Obtain More Information

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide vii

Documentation

The following related documents are shipped with the software:

• GeneMapper

Software Version 4.1 Installation and

Administration Guide (PN 4403614) – Provides procedures for

installing, securing, and maintaining version 4.1 of the

GeneMapper Software.

• GeneMapper

Software Version 4.1 Getting Started Guides for

microsatellite analysis (PN 4403672), loss of hetereozygosity

(LOH) analysis (PN 4403621), AFLP

system analysis

(PN 4403620), SNaPshot

kit analysis (PN 4403618), and

SNPlex

™

system analysis (PN 4403617) – Five guides that

explain how to analyze the application-specific example data

provided with the GeneMapper Software. The guides provide

brief, step-by-step procedures for the analysis of microsatellite,

LOH, AFLP

system, SNaPshot

kit, and SNPlex

™

system data

generated by compatible Applied Biosystems electrophoresis

instruments and Data Collection Software. The guides are

designed to help you quickly learn to use basic functions of the

GeneMapper Software.

• GeneMapper

Software Version 4.1 Online Help – Describes

the GeneMapper Software and provides procedures for common

tasks. Access online help by pressing F1, selecting Help

Contents and Index, or clicking in the toolbar of the

GeneMapper window.

• GeneMapper

Software Version 4.1 Quick Reference Guide

(PN 4403615) – Provides workflows for specific analysis types

and lists instruments, software, and analysis applications

compatible with the GeneMapper Software.

• GeneMapper

Software Version 4.1 Reference and

Troubleshooting Guide (PN 4403673) – Provides reference

information such as theory of operation and includes

troubleshooting information.

Portable document format (PDF) versions of this guide and the other

documents listed above are available on the GeneMapper

Software

Version 4.1 Documentation CD.

Note: For additional documentation, see “How to Obtain Support”

on page viii.

Preface

How to Obtain Support

viii GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Send Us Your

Comments

Applied Biosystems welcomes your comments and suggestions for

improving its user documents. You can e-mail your comments to:

techpubs@appliedbiosystems.com

Obtaining

Information from

the Online Help

The GeneMapper

Software features an online help system that

describes how to use each feature of the user interface. To access the

online help, click in any window or dialog box (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

http://www.appliedbiosystems.com

, then click the link for

Support

At the Support page, you can:

• Search through frequently asked questions (FAQs)

• Submit a question directly to Technical Support

• Order Applied Biosystems user documents, MSDSs, certificates

of analysis, and other related documents

• Download PDF documents

• Obtain information about customer training

• Download software updates and patches

In addition, the Support page provides access to worldwide telephone

and fax numbers to contact Applied Biosystems Technical Support

and Sales facilities.

Chapter 1

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 1

Chapter 1

Process Quality Values

and

Basic Troubleshooting

ter

orithms

hapter 2

NPlex

™

stem

Troubleshootin

Process Quality Values and

Basic Troubleshooting

In this chapter:

■ Diagnosing and Resolving Basic Problems . . . . . . . . . . . . . . 2

■ ADO (Allele Display Overflow) . . . . . . . . . . . . . . . . . . . . . . 8

■ AE (Allele Edit) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

■ AN (Allele Number). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

■ BD (Broad Peak) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

■ BIN (Out of Bin Allele) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

■ CC (Control Concordance). . . . . . . . . . . . . . . . . . . . . . . . . . . 11

■ DP (Double Peak). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

■ GQ (Genotype Quality) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

■ LPH (Low Peak Height). . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

■ MNF (Matrix Not Found) . . . . . . . . . . . . . . . . . . . . . . . . . . 16

■ NB (Narrow Bin) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

■ OBA (One Basepair Allele) . . . . . . . . . . . . . . . . . . . . . . . . . 18

■ OS (Offscale) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

■ OVL (Overlap) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

■ PHR (Peak Height Ratio) . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

■ SFNF (Sample File Not Found) . . . . . . . . . . . . . . . . . . . . . . 22

■ SHP (Sharp Peak). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

■ SNF (Size Standard Not Found). . . . . . . . . . . . . . . . . . . . . . 23

■ SP (Split Peak) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

■ SPA (Single Peak Artifact). . . . . . . . . . . . . . . . . . . . . . . . . . 24

■ SPU (Spectral Pull-Up) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

■ SQ (Sizing Quality) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

■ XTLK (Cross Talk) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Chapter 1 Process Quality Values and Basic Troubleshooting

Diagnosing and Resolving Basic Problems

2 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Diagnosing and Resolving Basic Problems

PQVs and the

Troubleshooting

Process

The GeneMapper

Software has a system of Process Quality Values

(PQVs) that provide the basis for troubleshooting fragment analysis

data using the software. PQVs are application-specific metrics, where

each evaluates the data for a specific quality that is consistent with a

problem associated with the type of analysis. In this way, the PQV

system can alert you to potential problems and provide you with a

starting point for investigation.

About Process

Quality Values

(PQVs)

Each individual PQV displays the result of a unique algorithmic test

that evaluates a specific property of the fragment analysis data. The

software performs the PQV tests in a specific sequence during the

analysis. With the exception of the Sizing Quality (SQ) PQV, the

software completes the analysis of each sample in a project even if a

sample fails one or more PQV tests.

The majority of PQV metrics yield numeric values between 0 and 1,

where 1 indicates that the related sample data or genotype completely

passed the associated test. Following the analysis, the software uses

the upper and lower thresholds for each PQV to translate the numeric

score into one of three symbols, displayed in the Samples or

Genotypes tab of the GeneMapper window.

Note:

If the thresholds of a PQV can be customized, the software

displays the parameters in the Quality Flags tab of the analysis method.

Note: Applied Biosystems recommends examining all samples that

produce (Check) or (Low Quality).

Symbol Definition

Default

Range

Pass: The sample or genotype passed the PQV

test.

0.75 to 1.0

Check: A possible problem exists for the sample

or genotype.

0.25 to 0.75

Low Quality/Fail: There is a strong possibility that

a problem exists for the sample or genotype.

0.0 to 0.25

Chapter 1 Process Quality Values and Basic Troubleshooting

Diagnosing and Resolving Basic Problems

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 3

Note: The Allele Display Overflow (ADO) and Allele Edit (AE)

PQVs of the Genotypes tab report their results as instead of the

colored icons.

Note:

The GeneMapper Software does not complete the analysis of

samples that display (Low Quality) for the Sizing Quality (SQ)

PQV test.

Adjusting the Threshold Settings of PQV Tests

Some PQV metrics include components that can be customized. In

those cases, the user-defined parameters for the PQV appear in the

Peak Quality tab of analysis methods for the applicable analysis type.

Adjusting the Weights of PQV

The majority of PQV contribute to the Genotype Quality (GQ) PQV, a

metric used to gauge the confidence of each genotype call. In those

cases, some PQV contain user-defined weights that determine how

significantly the associated PQV affect the GQ PQV calculation. For

those PQV, the user-defined weights appear in the Quality Flag tab of

analysis methods for the applicable analysis type. For more information

on the calculation of the GQ PQV, see “GQ (Genotype Quality)” on

page 13.

Note:

You can configure a PQV so that it does not contribute to the

GQ (by setting the weight to 0). However, the PQV remains active.

Rules for PQV Columns

• Quality metrics with Pass/ Check values and no Low

Quality values are warning flags. Analysis does not stop if problems

are detected with these properties, but Applied Biosystems

recommends examining results flagged as (Check).

• Holding the cursor over a column header displays a tooltip that lists

the full name of the column (the default names are often acronyms).

Chapter 1 Process Quality Values and Basic Troubleshooting

Diagnosing and Resolving Basic Problems

4 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Tabl e 1-1 PQV values by application

PQVs

Analysis

See Page

AFLP

System

Microsatelite

SNaPshot

Kit

SNPlex

™

System

Abb. Name

Other

ADO Allele Display Overflow

✔ ✔ ✔ ✔ ✔

AE Allele Edit

✔ ✔ ✔ ✔ ✔

AN Allele Number

✔ ✔ ✔ ✔

BD Broad Peak

✔ ✔ ✔ ✔ ✔

BIN Out of Bin Allele

✔ ✔ ✔

CC Control Concordance

✔ ✔ ✔ ✔

DP Double Peak

✔

GQ Genotype Quality

✔ ✔ ✔ ✔ ✔

LPH Low Peak Height

✔ ✔ ✔ ✔

MNF Matrix Not Found

✔ ✔ ✔ ✔

NB Narrow Bin

✔

OBA One Basepair Allele

✔

OS Off-Scale

✔ ✔ ✔ ✔ ✔

PHR Peak Height Ratio

✔ ✔ ✔ ✔

SFNF Sample File Not Found

✔ ✔ ✔ ✔ ✔

SHP Sharp Peak

✔

SNF Size Standard Not Found

✔ ✔ ✔ ✔ ✔

SP Split Peak

✔

SPA Single Peak Artifact

✔

SPU Spectral Pull-Up

✔ ✔ ✔ ✔ ✔

SQ Sizing Quality

✔ ✔ ✔ ✔ ✔

XTLK Cross Talk

✔ ✔ ✔

Chapter 1 Process Quality Values and Basic Troubleshooting

Diagnosing and Resolving Basic Problems

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 5

Common Troubleshooting Procedures

About the

Procedures

Procedures commonly used to troubleshoot errors and irregularities

in fragment analysis data:

■ Displaying Sample Information/Raw and EPT Data . see below

■ Displaying Numeric PQV Metrics . . . . . . . . . . . . . . . . . . . . . . 7

Displaying Sample

Information/Raw

and EPT Data

1. In the GeneMapper window, select the Samples tab.

2. In the Navigation Pane of the GeneMapper window:

a. Click to expand the contents of the project folder.

b. From the list of samples, select a sample that displayed

(Check) or (Low Quality).

3. Select the Info, Raw Data, and EPT Data tabs as needed to

display the sample information for the selected file.

• Info tab – Displays a summary of all information for the

associated sample file (see Table 1-2 on page 6).

•

Raw Data tab

– Displays an electropherogram of spectral

data collected during the run of the associated sample. The

spectral data is displayed in relative fluorescent units (RFU).

• EPT Data tab – Displays the EPT (electrical, power, and

temperature) data for the associated sample throughout the

course of the run.

Note: Table 1-2 lists only the information contained in the Info tab

that is relevant to troubleshooting. For a complete description of the

elements listed in the Info tab, see the GeneMapper

Software Online

Help.

Chapter 1 Process Quality Values and Basic Troubleshooting

Diagnosing and Resolving Basic Problems

6 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Table 1-2 Info Tab information relevant to troubleshooting

Group

Description/Information

Sample

Information

Describes the source and status of the imported sample data.

Sample Origin Path – Displays the path to the associated sample file at the time it

was imported (provided that the sample was added to the project from a sample

file).

Status Message – Displays information related to any events that occurred when

the sample was imported into the project.

Error Message

Displays any errors the software encounters during the analysis of the associated

sample. You can use the information in this group to verify the source of several

analysis problems.

Current Settings

Describes the analysis settings currently applied to the associated sample.

All of the data displayed in this group is useful for troubleshooting problems with

the GeneMapper Software. Although, most of the information can be viewed in

various parts of the software, the Current Settings group summarizes all the

information in a single location for easier access.

Run Information

Provides basic information about the configuration of the compatible

Applied Biosystems electrophoresis instrument and the run itself.

Instrument Name – Displays the name of the instrument used to run the sample.

The instrument name is important when diagnosing trends in analysis errors that

can be traced back to the instrument used to run the failed sample(s).

Data Collection Ver – Displays the version number of the Data Collection

Software used to run the sample.

Data Collection

Settings

Describes the configuration of the Data Collection Software at the time the sample

was run.

The data displayed in this group can be used to troubleshoot problems caused by

modifications to run modules. By comparing the

Data Collection Settings

information from passing and failing runs, you can identify any changes made to

the run module (intentional or unintentional) that may have caused or contributed

to the failure.

Note: The data displayed in the EPT tab provides a log of the actual parameters

throughout the run.

Capillary

Information

Provides the basic specifications of the capillary array used to run the sample.

Capillary Number – Displays the number of the capillary used to run the

associated sample. Like the instrument name, the capillary number is important

when diagnosing trends in analysis errors that can be traced back to the capillary

used to run the failed sample(s).

Chapter 1 Process Quality Values and Basic Troubleshooting

Diagnosing and Resolving Basic Problems

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 7

Displaying

Numeric PQV

Metrics

By default, the GeneMapper Software displays (Pass), (Check),

or (Low Quality) in some PQV columns to represent the numeric

score of the associated quality metric. When troubleshooting quality

errors, it is often more useful to configure the software to display

numeric representations of the quality values.

For example, the Sizing Quality (SQ) PQV evaluates the similarity

between the fragment pattern defined by the size standard definition and

the actual size standard peak distribution pattern 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, the software displays (Pass), (Check), or

(Low Quality) to indicate the result of the Sizing Quality calculation.

To display numerical representations of the quality metrics:

1. Select ToolsOptions, then select the Analysis tab.

2. In the Quality Metrics Display settings, select Numbers.

3. Click OK to apply the settings.

Sizing Quality Representation Samples Table Example

Symbols

(default)

Numbers

(recommended for troubleshooting)

Chapter 1 Process Quality Values and Basic Troubleshooting

ADO (Allele Display Overflow)

8 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

ADO (Allele Display Overflow)

Description/

Function

The ADO PQV indicates that the number of alleles called for the

associated sample at the specified marker exceeds the Allele Setting in

the Genoptypes tab of the table setting. Because the software is

configured to display fewer alleles than are present, the data for the

additional allele is hidden from view.

Note:

For each allele detected by the software, the Genotypes tab

displays six columns: name, size, height, area, mutation, and comments.

Expected Values – Indicates that the associated sample contains a number of

alleles at the specified marker that is greater than the user-defined

limit.

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the affected sample for miscalled peaks.

AE (Allele Edit)

Description/

Function

The

AE PQV

indicates whether or not a user modified the allele call

for the associated genotype.

Note: Allele calls can be modified in the Samples Plot, the

Genotypes Plot, and the Cluster Plot.

Expected Values – Indicates that the associated genotype call has been edited.

Chapter 1 Process Quality Values and Basic Troubleshooting

AN (Allele Number)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 9

AN (Allele Number)

Description/

Function

The AN PQV indicates that the associated sample contains either:

• A number of alleles at the specified marker that exceeds the

Max Expected Alleles setting (in the Peak Quality tab of the

analysis method)

• No alleles are present at the specified marker

Expected Values (Pass) or (Check)

Troubleshooting

Select the affected genotype(s), click (

Analysis



Display Plots

then review the sample data at the affected marker for additional peaks

or for the absence of peaks.

BD (Broad Peak)

Description/

Function

The BD PQV indicates that the width of the peak for the associated

genotype exceeds the Max peak width setting (in the Peak Quality tab of

the analysis method).

Note: When the BD PQV is triggered, the software reduces the GQ

PQV by 50% because the default multiplier is 0.5.

Expected Values (Pass) or (Check)

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the associated peak for irregularities.

Chapter 1 Process Quality Values and Basic Troubleshooting

BIN (Out of Bin Allele)

10 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

BIN (Out of Bin Allele)

Description/

Function

The BIN PQV indicates that the apex of the peak for the associated

genotype is outside of the boundaries that define the associated bin.

Note: When the BIN PQV is triggered, the software reduces the GQ

PQV by 80% because the default multiplier is 0.8.

Note:

For human identification (HID) analysis, the BIN PQV is

displayed as the OL (Off-Ladder Alleles) PQV.

Expected Values (Pass) or (Check)

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the allele(s) at the appropriate bin location.

Symptom Possible Cause Solution

BIN PQV displays

(Check)

After using Auto Bin to generate bins,

the software did not create a bin for an

allele peak because it considered the

peak to be a single peak artifact. The

SPA flag was triggered because the

software did not detect any stutter

peaks to the left of the allele peak; a

result of the minimum fragment length

for the marker being set too high

Correct the SPA flag by editing the

marker minimum fragment length,

then reanalyze and perform the Auto

Bin again.

The GeneMapper Software detected

an allele peak that did not fit into any

of the defined bins because the bins

were not calibrated to the allelic

ladder; a result of a sample file

containing an allelic ladder that is not

designated as an allelic ladder in the

Samples tab.

In the Samples tab of the

GeneMapper window, set the Sample

Type of the sample containing the

allelic ladder to

Allelic Ladder

You generated bins using the Auto Bin

function but the GQ value for a marker

was less than the Minimum Quality

Value of 0.1 (as set in the Auto Bin

dialog box).

View the allele peak(s) for the marker

in the Genotypes Plot window.

Determine if the allele peaks(s) are

valid. If so, manually create bin(s) for

the peak(s).

Chapter 1 Process Quality Values and Basic Troubleshooting

CC (Control Concordance)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 11

CC (Control Concordance)

Description/

Function

The CC PQV indicates that the associated control sample does not

exactly match the defined alleles for the related marker.

IMPORTANT! Applied Biosystems recommends running the control

sample at least once for every panel.

Note: The CC PQV serves primarily as an internal control for quality

assurance.

Expected Values (Pass) or (Check)

Troubleshooting

Select the affected control sample in the Samples tab of the GeneMapper

window, click (

Analysis



Display Plots

), then review the positions

of the peaks relative to the bins.

Symptom Possible Cause Solution

CC PQV displays

(Check)

The allele calls of the sample defined

as the Positive Control in the Samples

tab do not match the Positive Control

allele calls in the marker definition

because the well contains the

incorrect positive control sample.

Run the correct positive control and

add the sample file to the project,

then define the sample as the

Positive Control in the Samples tab

of the GeneMapper window.

The allele calls of the sample defined

as the Positive Control in the Samples

tab do not match the positive control

allele calls in the marker definition

because the alleles were defined

incorrectly.

Edit the Positive Control allele calls in

the marker definition in the Panel

Manager.

The sample defined as the Negative

Control contains an “allele peak” due

to the presence of a spike caused by

dust or a gas bubble.

Rerun the negative control and add

the sample file to the project, then

define the sample as the Negative

Control in the Samples tab of the

GeneMapper window.

Chapter 1 Process Quality Values and Basic Troubleshooting

DP (Double Peak)

12 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

DP (Double Peak)

Description/

Function

The DP PQV indicates that the peak for the associated genotype:

• Resides in a bin with another peak of the same dye color

and

• The ratio of the peak height (minor/major peak height) is greater

than the Double Peak setting in the Peak Quality tab of the

Analysis Method

Expected Values (Pass) or (Check)

Troubleshooting

Select the affected genotype, click (

Analysis



Display Plots

), then

review the sample data at the appropriate bin for additional peaks

Symptom Possible Cause Solution

DP PQV displays

(Check)

A problem with the chemistry is

causing peaks from two different

markers not to resolve, possibly

because either of the primers are too

similar in length or the mobilities of

the two primer fragments are similar.

Check primer lengths and

electrophoresis conditions and

adjust as necessary.

Chapter 1 Process Quality Values and Basic Troubleshooting

GQ (Genotype Quality)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 13

GQ (Genotype Quality)

Description/

Function

The GQ PQV provides a summary of the quality metrics for each

genotype. The GQ value is a calculated combination of the relevant,

weighted PQVs and the Marker Quality value for the genotype.

Calculation of the Genotype Quality (GQ) Metric

The formulas used by the GeneMapper Software to calculate the GQ

value are analysis-specific, and differ largely based on the PQVs

supported by each application. The following general formula

describes the genotype quality calculation:

where the Marker Quality (MQ) value is modified by the user-defined

PQVs to generate the final GQ value, and the PQVs are weighted from 0

to 1. The actual value of each PQV in the equation is 1 minus the weight

assigned in the Quality Flags tab of the analysis method used to analyze

the data.

IMPORTANT! The filtering of individual PQVs is controlled by the

threshold settings in the Peak Quality tab of the analysis method.

Also, the PQVs remain fully functional regardless of the weights

used.

PQV Weight Net Effect on GQ Calculation

No effect on the GQ calculation

The initial value of 1 minus the weight of 0 yields a PQV of

1. When used in the GQ calculation, the PQV has no effect

since 1

∞

MQ = MQ.

Reduces the GQ value to 0

The initial value of 1 minus the weight of 1 yields a PQV of

0. When used in the GQ calculation, the PQV automatically

causes the GQ to fail since 0

∞

MQ = 0.

0<x <1

Reduces the GQ value to the fraction specified by the

weight. The higher the value, the greater the effect on GQ.

GQ MQ 1 BD–()1OS–()…1SPU–()×××()×=

Chapter 1 Process Quality Values and Basic Troubleshooting

GQ (Genotype Quality)

14 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Calculation of the Marker Quality Metric

Figure 1-1 shows how the GeneMapper Software generates a Marker

Quality (MQ) value from sample peak data with assigned Allele

Quality (AQ) values. AQ values are a function of quality value

assignments for: sizing quality, allele calling quality, bin assignment

quality, and bin quality.

Note:

When analyzing SNPlex

™

System sample data, the GeneMapper

Software calculates GQ values depending on the method (Model or

Rules) selected to perform allele calling. The following figure

illustrates the derivation of GQ values using the Rules method.

Figure 1-1 Calculation of the Marker Quality metric

Expected Values (Pass), (Check), or (Fail)

Note: The software assigns the GQ PQV flags based on the PQV

threshold settings in the Quality Flags tab of the analysis method.

Troubleshooting

Review the PQV for the affected genotype to determine the metric that

is causing the GQ PQV to fail.

Note:

To better determine how individual PQV contribute to the GQ

PQV, configure the software to display the PQV numerically, as

explained in “Displaying Numeric PQV Metrics” on page 7.

x (AQ) = MQ

GR2119

Chapter 1 Process Quality Values and Basic Troubleshooting

LPH (Low Peak Height)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 15

LPH (Low Peak Height)

Description/

Function

The LPH PQV indicates that the height of the peak for the associated

genotype is lower than the associated heterozygous or homozygous

height limit that is specified in the analysis method. You can set

homozygous value (default is 200) and heterozygous value (default is

100) in the Peak Quality tab of the analysis method.

Note: When the LPH PQV is triggered, the software reduces the GQ

PQV by 50% (the default multiplier is “0.5”).

Expected Values (Pass) or (Check)

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the associated peak for irregularities.

Chapter 1 Process Quality Values and Basic Troubleshooting

MNF (Matrix Not Found)

16 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

MNF (Matrix Not Found)

Description/

Function

The MNF PQV indicates whether or not the GeneMapper Software

can access the matrix specified in the Matrix column of the Samples

tab for the associated sample.

IMPORTANT! Because recent models of Applied Biosystems

instruments save matrix data to the sample files they create, the MNF

flag is applicable only to sample files created by the ABI P

RISM

310

and 377 instruments.

Expected Values (Pass) or (Check)

Troubleshooting Determine the name of the missing matrix file(s) by reviewing the

sample information for the affected samples, as explained in

“Displaying Sample Information/Raw and EPT Data” on page 5.

Importing a

Matrix File

(Windows

Only)

IMPORTANT!

You must convert matrix files created by Macintosh

computers before importing them. The conversion utility is free and

available from the software support section of the Applied Biosystems

website (

www.appliedbiosystems.com/support/software/

1. Click (ToolsGeneMapper Manager).

2. In the GeneMapper Manager, select the Matrices tab, then

click Import.

3. In the Importing Matrix dialog box, navigate to and select the

matrix file, then click Import.

4. Click Done to close the GeneMapper Manager, then analyze the

project.

Symptom Possible Cause Solution

MNF PQV displays

(Check)

The software could not

access the matrix file

specified in the Matrix

column for the associated

sample file.

Locate the missing matrix file, then import it as

explained in “Importing a Matrix File (Windows®

Only)” on page 16

If the sample files for the matrix standards used

to create the missing matrix are available, re-

create the matrix as explained in “Generating a

Matrix” on page 17.

Chapter 1 Process Quality Values and Basic Troubleshooting

MNF (Matrix Not Found)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 17

Generating a

Matrix

1. Click (ToolsGeneMapper Manager).

2. In the GeneMapper Manager, select the Matrices tab, then

click New.

3. In the Matrix Editor dialog box:

a. Type a name and description for the matrix.

b. In the Number of Dyes drop-down list, select the number of

dyes present in the matrix (4 or 5).

c. Click B, navigate to and select the sample file for the blue

matrix standard, then click Open.

d. Repeat step 3c for the remaining dyes in the matrix (Green,

Yellow, Red, and Orange if applicable).

e. Click Create to create the matrix.

f. Click OK.

4. Click Done to close the GeneMapper Manager.

5. In the Matrix column of the Samples tab, select the new matrix,

then analyze the project.

Size standard

name

Fragment sizes

for the size

standard

Size standard dye

channel

Generated matrix

data

Chapter 1 Process Quality Values and Basic Troubleshooting

NB (Narrow Bin)

18 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

NB (Narrow Bin)

Description/

Function

The NB PQV indicates that the apex of the associated peak for the

associated genotype is present within 0.5 base pairs of a bin that does

not contain a peak. This PQV is designed to capture peaks that are

outside of bin boundaries because of incorrect bin definitions.

Expected Values (Pass) or (Check)

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the peak at the appropriate bin location.

OBA (One Basepair Allele)

Description/

Function

The OBA PQV indicates, for the associated genotype, that the apex

of the associated peak is present at a position within 1 base pair of

another peak.

Expected Values (Pass) or (Check)

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the allele at the appropriate bin location for a

microvariant peak or an invalid allele call.

Symptom Possible Cause Solution

NB PQV displays

(Check)

You created a bin that is too narrow

to contain its associated allele peak.

In the Panel Manager, edit the bin

width and/or location so that it

contains the allele peak.

Chapter 1 Process Quality Values and Basic Troubleshooting

OS (Offscale)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 19

OS (Offscale)

Description/

Function

The OS PQV is displayed in both the Samples and Genotypes tabs of

the GeneMapper window, but the function of the OS PQV in each tab

differs in the following way:

• OS PQV for the Samples tab – The signal associated with the

size standard of the specified sample contains one or more peaks

that exceed the maximum detectable range.

• OS PQV for the Genotypes Tab – The signals associated with

the given sample contain one or more peaks that exceed the

maximum detectable range.

Note:

When the OS PQV of the Genotypes tab is triggered, the

software reduces the GQ PQV by 50% (the default multiplier is “0.5”).

Expected Values (Pass) or (Check)

Troubleshooting 1.

In the GeneMapper window, select the

Samples

tab.

In the Navigation Pane, click to expand the project folder, then

select the sample that displays (Check) in the OS column.

Select the

Raw Data

tab to display the electropherogram of

normalized spectral data collected during the associated sample run.

The spectral data is displayed in Relative Fluorescent Units (RFU).

Review the data for offscale peaks.

5. Use Table 1-3 on page 20 to determine an appropriate corrective

action.

Offscale peak

(primer peak)

Chapter 1 Process Quality Values and Basic Troubleshooting

OVL (Overlap)

20 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

OVL (Overlap)

Description/

Function

The OVL PQV indicates that the peak for the associated genotype

has been called twice by the GeneMapper Software. If the ranges of

two bins overlap, a peak can reside in both bins and, therefore, be

called twice, once for each allele.

Expected Values (Pass) or (Check)

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the peak and associated bins at the appropriate location.

Table 1-3 OS PQV Troubleshooting

Symptom Possible Cause Solution

• MNF PQV in the Samples tab displays

(Check)

• Raw data contains multiple off-scale

size standard peaks

Too much size

standard injected

into the capillary

No action necessary. The data

cannot be manipulated to remove

the oversized peaks.

Decrease the quantity of size

standard used in subsequent runs.

Also, make sure to use Hi-Di

Formamide as the loading reagent.

IMPORTANT! Water loading can

produce artificially high signal and

is not recommended.

• MNF PQV in the Genotypes tab

displays (Check)

• Raw data contains multiple off-scale

peaks in the signal(s) associated with

the sample fragments

Too much sample

injected into the

capillary

No action necessary. The data

cannot be manipulated to remove

the oversized peaks.

Decrease the quantity of sample

used in subsequent runs.

Chapter 1 Process Quality Values and Basic Troubleshooting

PHR (Peak Height Ratio)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 21

PHR (Peak Height Ratio)

Description/

Function

The PHR PQV indicates that the apex of the peak for the associated

genotype is:

• Present at a position within 1 base pair of another peak

and

• The ratio of the height of the lower peak to that of the higher

peak is less than the Minimum Peak Height Ratio setting in the

Peak Quality tab of the analysis method.

Note: For LMS markers, the ratio is calculated based on the peak

heights of the called allele peaks.

Note: For SNaPshot

kit analysis, the ratios are calculated as they

are for microsatellite markers except that they span two different

colors, and only two peaks are used in the calculation.

Expected Values (Pass) or (Check)

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the peaks at the appropriate location.

Symptom Possible Cause Solution

PHR PQV displays

(Check)

The sample has undergone Loss of

Heterozygosity (LOH). A difference

in peak heights between alleles is

expected.

Normal occurrence. No action

necessary.

Further evaluate the sample for LOH

using the Report Settings Editor and

Report Manager.

For more information, see the

GeneMapper

Software Version 4.1

LOH Analysis Getting Started Guide

(PN 4403621).

Chapter 1 Process Quality Values and Basic Troubleshooting

SFNF (Sample File Not Found)

22 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

SFNF (Sample File Not Found)

Description/

Function

The SFNF PQV indicates whether or not the software can access the

sample file (*.fsa) shown in the Sample File column of the associated

sample. When the software adds a sample to a project from a sample file,

it retains a link to the original file. The software displays (Check) in

the SFNF column if the sample file is deleted, renamed, or moved.

Expected Values (Pass) or (Check)

Troubleshooting Determine the name and location of the missing sample file:

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 project folder, then select the sample

that display (Check) in the SFNF column.

3. Select the Info tab, then note the name (Sample File) and

location (Sample Origin Path) of the sample.

Symptom Possible Cause Solution

SFNF PQV displays

(Check)

Sample file has been

renamed, moved, or

deleted.

Search the local drives of the computer for the

sample file, then do one of the following:

• If you cannot find the file, no further action can

be taken to resolve the PQV flag.

• If you find the file, use the Associate Sample

feature to direct the software to the new

location as follows:

a. In the Samples tab of the GeneMapper

window, select the affected samples.

b. Select FileAssociate Samples.

c. In the Select Folder dialog box, select the

folder containing the missing files, then

click Select.

Chapter 1 Process Quality Values and Basic Troubleshooting

SHP (Sharp Peak)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 23

SHP (Sharp Peak)

Description/

Function

The

SHP

PQV indicates

that the peak for the associated genotype is

part of a cluster of peaks with a large, narrow peak in the middle

whose width is 50% less than either of the neighboring peaks.

Expected Values (Pass) or (Check)

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the peak at the appropriate location.

SNF (Size Standard Not Found)

Description/

Function

The

SNF PQV

indicates whether or not the GeneMapper Software

can access the

size standard definition

specified in the Size Standard

column for the associated sample.

Expected Values (Pass) or (Check)

Troubleshooting Verify that the software does not contain the desired size standard:

1. In the Samples tab of the GeneMapper window, note the name

of the size standard assigned to the affected sample.

2. Click (ToolsGeneMapper Manager).

3. In the GeneMapper Manager, select the Size Standards tab.

4. Verify that the Size Standard tab does not list the missing size

standard, or that it has not been renamed.

Symptom Possible Cause Solution

SNF PQV displays

(Check)

(Autoanalysis only) The size

standard may have been set

incorrectly in the plate record of the

Data Collection Software.

Do one of the following:

• If using an Applied Biosystems

size standard, click Import to

import the definition from the

default Panels folder.

•Click New to create a custom

size standard of the same name.

The size standard definition has been

renamed, deleted, or does not exist.

Chapter 1 Process Quality Values and Basic Troubleshooting

SP (Split Peak)

24 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

SP (Split Peak)

Description/

Function

The

SP PQV

indicates

that the peak for the associated genotype is

part of a pair of overlapping peaks that are less than 0.25 base pairs

apart (the horizontal distance between two peak apexes).

Expected Values (Pass) or (Check)

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the peak at the appropriate location.

SPA (Single Peak Artifact)

Description/

Function

The

SPA PQV

indicates

that no peaks are present within a two-base-

pair range before the peak for the associated genotype. This PQV is

designed to detect the absence of stutter peaks that accompany

microsatellite peaks.

Expected Values (Pass) or (Check)

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the peak at the appropriate location.

Symptom Possible Cause Solution

SPA PQV displays

(Check)

The GeneMapper Software did not

detect any stutter peaks to the left of

the allele peak because the

minimum fragment length for the

marker was set too high.

In the Panel Manager, edit the

marker minimum fragment length,

then reanalyze.

Chapter 1 Process Quality Values and Basic Troubleshooting

SPU (Spectral Pull-Up)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 25

SPU (Spectral Pull-Up)

Description/

Function

The SPU PQV indicates that the apex of the peak for the associated

genotype is at a position where the marker signal contains pull-up

peaks (also called bleed-through peaks). Pull-up peaks occur when

the peak height of the called allele is less than X% of the larger peak

that is within ±1 data point.

Expected Values (Pass) or (Check)

Troubleshooting Select the affected genotype, click (AnalysisDisplay Plots),

then review the peak at the appropriate location.

SQ (Sizing Quality)

Description/

Function

The SQ PQV reports the result of the Sizing Quality test, which gauges

the similarity between the fragment pattern defined by the size

standard definition, and the actual distribution of size standard peaks in

the sample data. The metric of the Sizing Quality test is a combination

of several values which measure the success of the algorithms that:

• Identify and eliminate primer peaks based on peak shape

• Perform size matching (ratio matching)

• Make a size-calling curve using the chosen sizing method

The Sizing Quality metric yields a value between 0 and 1. Based on the

PQV Threshold settings in the analysis method used to analyze the

data, the software translates the metric into the (Pass), (Check),

or (Low Quality) flags to indicate the result of the test.

Note: The GeneMapper Software does not complete the analysis of

samples that fail the Sizing Quality test (samples that display ).

Expected Values (Pass), (Check), or (

Low Quality

)

Note: When performing size calling using the Classic sizing method,

the software cannot determine Sizing Quality and, therefore, SQ is

always

(Check)

Chapter 1 Process Quality Values and Basic Troubleshooting

SQ (Sizing Quality)

26 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Troubleshooting Review the data of the size standards that failed the SQ PQV:

1. In the Samples tab of the GeneMapper window, click

(AnalysisLow Quality to Top) to sort the data so that the

samples that produced errors appear at the top of the table.

2. In the Samples tab, select the

rows for the

sample(s) that display

(Check) or (Fail) in the SQ column.

3. Click (AnalysisSize Match Editor) to view the sizing

information for the selected sample(s).

In the Navigation Pane of the Size Match Editor, select a sample

file

to display the sizing data for the associated sample.

Review the data for the following qualities

• Signal Strength – The signal strength (peak height) of all

peaks must exceed the Peak Detection Threshold defined in

the analysis method used to analyze the data.

• Correct Size Calls/Labels – All peaks must be correctly

identified by the software. The labels above the peaks must

be in sequential order from left to right, least to greatest.

• Evenness of Signal Strength – All peaks should have

relatively uniform signal strengths.

• Sizing Quality – The sizing quality of each sample should

be within the passing range for your chemistry application.

Note: To magnify the plot of the Size Matches tab, drag the

mouse cursor ( ) across a region of the x- or y-axis.

6. Use Table 1-4 on page 27 to determine an appropriate corrective

action.

7. Repeat steps 4 through 6 for each sample file.

Chapter 1 Process Quality Values and Basic Troubleshooting

SQ (Sizing Quality)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 27

Table 1-4 SQ PQV Troubleshooting

Symptom Possible Cause Solution

•SQ PQV displays or

• Size Match Editor does not

display peak data

The Size Standard Dye setting for

the size standard definition is not

set to the correct dye.

1. Verify that the correct size

standard definition is in use.

2. Open the size standard

definition and verify that the:

– Dye setting is set to the

correct dye

– Fragment sizes are correct

3. Modify the size standard

definition as necessary.

•SQ PQV displays or

• Peaks do not contain size

labels

The fragment sizes of the size

standard definition do not match

the positions of the detected

peaks.

•SQ PQV displays or

• One or more miscalled peaks

Peak detection threshold

associated with the size standard

is set too high or low.

Adjust the analysis method so

that the peak detection threshold

associated with the size standard

is greater than the height of the

miscalled peak. See “Adjusting

Peak Detection Thresholds” on

page 29 for more information.

•SQ PQV displays or

• Peaks are clear and

distinguishable, but have

low signal strength

Peak detection threshold

associated with the size standard

is set too high or low.

Electrophoresis or pipetting error

•SQ PQV displays or

• Size standard peaks occur

within a primer peak

Insufficient cleanup step Create and analyze the data

using a custom size standard

that does not include the

undetectable peak. See

“Correcting Miscalled Peaks” on

page 28 for more information.

•SQ PQV displays or

• Size standard peaks are clear

and distinguishable, but

consistently have low signal

strength

Incorrect concentration of size

standard in sample loading

reagent

Increase the concentration of

size standard added to

subsequent runs.

Incorrect injection settings Review the injection settings of

the run module for errors.

•SQ PQV displays or

• Peaks are clear and

distinguishable, but have

low signal strength

• Sizing failures occur in a

regular pattern (the same

wells fail repeatedly)

• Electrophoresis or pipetting

error

• Defective capillaries/arrays

See the user manual for your

Applied Biosystems

electrophoresis instrument for

information on troubleshooting

defective capillaries/arrays.

•SQ PQV displays or

• Size calling errors occur for

different samples on the same

capillary over multiple runs

Defective capillary

Chapter 1 Process Quality Values and Basic Troubleshooting

SQ (Sizing Quality)

28 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Correcting

Miscalled Peaks

You can use the Size Match Editor to correct peaks that are miscalled

by the GeneMapper Software.

To correct a miscalled size standard:

1. In the Navigation Pane of the Size Match Editor, select the

sample file containing the miscalled peak.

2. Remove the label from the miscalled peak:

a. Select the peak with the label by clicking inside the body of

the peak.

b. Select EditDelete Size Label (or right-click the peak,

then select Delete).

3. Apply the label to the correct peak:

a. Select the correct peak.

b. Select EditAdd Size Label (or right-click the peak, then

select Add).

c. In the Select Size dialog box, double-click the label to

apply to the selected peak.

4. Click (ToolsCheck Sizing Quality) to verify that the

sample sizes correctly.

5. Click Apply to save the changes, then click OK.

IMPORTANT! You must click Apply to reanalyze the sample.

Note: Observe that the cell in the Status column for the sample

now displays (Analysis Required).

6. Reanalyze the sample using the new setting to verify that the

problem is resolved.

Chapter 1 Process Quality Values and Basic Troubleshooting

SQ (Sizing Quality)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 29

Adjusting Peak

Detection

Thresholds

You can resolve a significant number of sizing failures by adjusting

the peak detection thresholds of the analysis method for a project.

The software identifies peaks that exceed the threshold for each

associated dye channel, but it cannot identify peaks that fall below it.

Samples that exhibit low signal intensity (low peak heights) can

occasionally fail sizing because one or more peaks fall below the

threshold defined in the analysis method. By lowering the threshold

of the appropriate dye channel, the software can call the peak(s)

correctly.

In contrast, when the signals of the size standard peaks are very high,

the software may misidentify a shoulder preceding a peak as the main

peak (see Figure 1-2). Because the shoulder peak does not occur at

the correct position relative to the other peaks, sizing fails. By

adjusting the analysis method so that the threshold value is greater

than the height of the shoulder, you can achieve good sizing.

Figure 1-2 Size standard with shoulder incorrectly labeled as a peak

To lower the peak detection thresholds of an analysis method:

1. In the GeneMapper window, click (ToolsGeneMapper

Manager).

2. In the GeneMapper Manager, select the Analysis Methods tab.

25-bp shoulder

incorrectly labeled

as a peak

Chapter 1 Process Quality Values and Basic Troubleshooting

SQ (Sizing Quality)

30 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

3. Do one of the following, to:

Modify the current analysis method, go to step 4.

Create a copy of the current analysis method:

a. Select the current analysis method.

b. Click Save As.

c. In the Save As dialog box, type a name for the new method,

then click OK.

4. Select the analysis method you want to modify, then click Open.

5. In the Analysis Method Editor, select the Peak Detector tab.

6. Modify the appropriate Peak Amplitude Threshold settings as

needed. Ideally, you should set the threshold of the appropriate

dye channel to a value less than the signal intensity of the

shortest size standard peak.

Note: Applied Biosystems recommends using Peak Amplitude

Threshold settings of no less than 50 RFU.

7. Click OK to save the analysis method.

8. Click Done to close the GeneMapper Manager, then reanalyze

the samples using the new analysis method.

Peak Amplitude

Threshold settings

Chapter 1 Process Quality Values and Basic Troubleshooting

SQ (Sizing Quality)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 31

Customizing a

Size Standard

Definition

You can create a custom size standard definition to correct some

problems that consistently cause samples to fail sizing. Examples of

problems that you can resolve using custom size standards include

series of samples that fail sizing because

• The primer peak prevents the software from detecting and sizing

the peaks of the smaller size standard fragments

• A fragment of a custom size standard does not migrate as expected

during electrophoresis

About GeneMapper Software Size Standards

Before the GeneMapper Software can size fragment analysis data, it

must contain information about the size standard that was run with

the samples. The size standard definition supplies the software with

two pieces of information: the color of the dye associated with the

size standard, and the sizes (in bp) of the fragments that comprise the

size standard. Although the software provides definitions for all

Applied Biosystems size standards, you may need to create your own

definition if you choose to use a third-party standard, or experience

difficulty analyzing your data.

To create a custom size standard definition:

1. In the GeneMapper window, click (ToolsGeneMapper

Manager).

2. In the GeneMapper Manager, select the Size Standards tab.

3. Click New.

4. In the Select Dye and Analysis Method dialog box, select Basic

or Advanced, then click OK.

Size standard peaks

eclipsed by the primer peak

Primer peak

Chapter 1 Process Quality Values and Basic Troubleshooting

XTLK (Cross Talk)

32 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

5. In the Size Standard Editor:

a. In the Name field, type a name for the custom standard.

b. Select Size Standard Dye<appropriate dye>.

c. In the Size Standard table, type the size values for the size

standard (press Enter after typing each value).

IMPORTANT! After typing the last value, you must press Enter

to ensure 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.

6. Click OK to save the size standard.

7. Click Done to close the GeneMapper Manager.

8. In the Samples tab of the GeneMapper window, apply the new

size standard to the samples of the project.

9. Reanalyze the sample using the new setting to verify that the

problem has been resolved.

XTLK (Cross Talk)

Description/

Function

The XTLK PQV indicates that, at the peak position of the associated

genotype, the ratio of the signals collected from the neighboring

capillaries exceed the Cross-talk ratio setting in the Peak Quality tab

of the analysis method.

Note: When the

XTLK

PQV is triggered, the software reduces the

GQ PQV by 50% (the default multiplier is “0.5”).

Expected Values (Pass) or (Check)

Chapter 2

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 33

Chapter 2

SNPlex™ System

Troubleshooting

ter

Algorithm

ter

rocess Quality Values

Basic

roubleshootin

SNPlex

™

System

Troubleshooting

In this chapter:

■ Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Chapter 2 SNPlex

™

System Troubleshooting

Overview

34 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Overview

Identifying

Potential

Problems

You can use tools in the GeneMapper

Software to identify potential

problems at both the project and study levels of the SNPlex

™

System

analysis.

Identifying Potential Problems in SNPlex System Studies

Version 4.1 of the GeneMapper Software emphasizes the use of

studies for analyzing data generated using the SNPlex

™

Genotyping

System Chemistries. You can use the tools of the Study Manager to

maintain system-wide quality control and visualize potential

problems in SNPlex System data. After you identify a potential

problem, the software allows you to review the applicable run and

resolve the issues that can be corrected.

Note: See the GeneMapper

Software Version 4.1 SNPlex

™

System

Analysis Getting Started Guide (PN 4403617) for a detailed

explanation of the study management system.

Identifying Potential Problems in SNPlex Projects

SNPlex projects, like the other analyses supported by the

GeneMapper Software, contain a variety of Process Quality Values

(PQVs), which can aid you in identifying potential problems.

Chapter 1 explains the functions of all PQVs relevant to the analysis

of SNPlex System data.

Resolving

Problems and

Errors

The SNPlex

™

Genotyping System 48-Plex User Guide (PN 4360856)

describes how to resolve common chemistry- and software-related

problems. The user guide addresses all aspects of the SNPlex

System, not just those issues that are exclusive to the GeneMapper

Software analysis.

Chapter 3

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 35

Chapter 3

Algorithms

ter

rocess Quality Values

Basic

roubleshootin

ter 2

NPlex

™

System

rou

oot

Algorithms

In this chapter:

■ Genotyping Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

■ Peak Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

■ Optimizing Peak Detection Sensitivity . . . . . . . . . . . . . . . . 41

■ Slope Thresholds for Peak Start/End Parameters. . . . . . . . . 44

■ Slope Threshold Example . . . . . . . . . . . . . . . . . . . . . . . . . . 45

■ Size-Matching/Size-Calling Algorithm . . . . . . . . . . . . . . . . 46

■ Size-Calling Methods (Classic and Advanced Modes) . . . . 47

■ Allele-Calling Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Chapter 3 Algorithms

Genotyping Algorithms

36 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Genotyping Algorithms

Overview This chapter discusses the following algorithms:

• Peak Detection – Uses the Basic, Advanced, or Classic mode

algorithms to detect peaks and process data

•

Size-Matching/Calling

– Matches detected peaks to size standards

•

Binning

– Determines bin centers for genotyping

•

Allele-Calling

– Produces a consensus call based on several allele-

calling algorithms

Data Flow Figure 3-3 and Figure 3-4 on page 37 show the data flow in

GeneMapper

Software. Standard signal processing is applied to the

data before the data are delivered to the GeneMapper Software

algorithms.

Figure 3-3 Microsatellite analysis data flow

Input Sample

Supported Applied Biosystems

Genetic Analysis Systems

Baselining

Peak Detection

Binning

Size-Matching/Calling

Caller n

Arbitrator

Bin Assignment

Final Quality Value Determination

Report Results to User

Caller 1

Chapter 3 Algorithms

Genotyping Algorithms

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 37

Figure 3-4 SNPlex

™

System analysis data flow

Input Sample

Supported Applied Biosystems

Genetic Analysis Systems

Baselining

Peak Detection

Binning

Size-Matching/Calling

Allele Calling

Final Quality Value Determination

Report Results to User

Chapter 3 Algorithms

Peak Detection

38 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Peak Detection

Polynomial

Degree and Peak

Window Size

Parameters

Two peak-detection parameters are used in the polynomial detection

algorithm: Polynomial Degree, and Peak Window Size.

Use the Polynomial Degree and the Peak Window Size settings to

adjust the sensitivity of the peak detection. You can adjust these

parameters to detect a single base pair difference while minimizing

the detection of shoulder effects or noise.

Sensitivity increases with larger polynomial degree values and

smaller window size values. Conversely, sensitivity decreases with

smaller polynomial degree values and larger window size values.

How They Work

The peak window size functions with the polynomial degree to set

the sensitivity of peak detection. The peak detector calculates the first

derivative of a polynomial curve fitted to the data within a window

that is centered on each data point in the analysis range.

Using curves with larger polynomial degree values allows the curve

to more closely approximate the signal and, therefore, the peak

detector captures more peak structure in the electropherogram.

The peak window size sets the width (in data points) of the window

to which the polynomial curve is fitted to data. Higher peak window

size values smooth out the polynomial curve, which limits the

structure being detected. Smaller window size values allow a curve to

better fit the underlying data.

How to Use the Peak Detection Parameters

Use the table below to adjust the sensitivity of detection.

Function Polynomial Degree Value Window Size Value

Increase sensitivity Higher Lower

Decrease sensitivity Lower Higher

Chapter 3 Algorithms

Peak Detection

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 39

Guidelines for Use

To detect well-isolated, baseline-resolved peaks, use polynomial degree

values of 2 or 3. For finer control, use a degree value of 4 or greater.

As a guideline, set the peak window size (in data points) to be about 1

to 2 times the full width at half maximum height of the peaks that you

want to detect.

Examining Peak

Definitions

To examine how GeneMapper Software has defined a peak, select

ViewShow Peak Positions. The peak positions, including the

beginning, apex, and end of each peak, are tick-marked in the

electropherogram.

Effects of Varying

the Polynomial

Degree

Figure 3-5 shows peaks detected with a window size of 15 data

points and a polynomial curve of degree 2 (green), 3 (red), and 4

(black). The diamonds represent a detected peak using the respective

polynomial curves.

Note that the smaller trailing peak is not detected using a degree of 2

(green). As the peak detection window is applied to each data point

across the displayed region, a polynomial curve of degree 2 could not

be fitted to the underlying data to detect its structure.

Figure 3-5 Electropherogram showing peaks detected with three

different polynomial degrees

Polynomial curve of degree 4

(black)

Polynomial curve of degree 3

(red)

Polynomial curve of degree 2

(green)

Chapter 3 Algorithms

Peak Detection

40 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Effects of

Increasing the

Window Size

Value

In Figure 3-6 both polynomial curves have a degree of 3 and the

window size value was increased from 15 (red) to 31(black) data

points.

As the cubic polynomial is stretched to fit the data in the larger

window size, the polynomial curve becomes smoother. Note that the

structure of the smaller trailing peak is no longer detected as a

distinct peak from the adjacent larger peak to the right.

Figure 3-6 Electropherogram showing the same peaks as in the

Figure 3-5 after increasing the window size value but keeping the

polynomial degree the same

Window size value of 15 (red)

Window size value of 31 (black)

Chapter 3 Algorithms

Optimizing Peak Detection Sensitivity

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 41

Optimizing Peak Detection Sensitivity

Examples ■ Example 1: Reducing Window Size. . . . . . . . . . . . . . . . . . . . 41

■ Example 2: Reducing Window Size/Increasing

Polynomial Degree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

■ Example 3: Extreme Settings . . . . . . . . . . . . . . . . . . . . . . . . . 43

Example 1: Reducing Window Size

Initial

Electropherogram

Figure 3-7 shows two resolved alleles of known fragment lengths

(that differ by one nucleotide) detected as a single peak. The analysis

was performed using a polynomial degree of 3 and a peak window

size of 19 data points.

Figure 3-7 Electropherogram showing two resolved alleles

detected as a single peak

Note: For information on the tick marks displayed in the

electropherogram, see “Examining Peak Definitions” on page 39.

Chapter 3 Algorithms

Optimizing Peak Detection Sensitivity

42 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Effects of

Reducing

Figure 3-8 shows that both alleles are detected after reanalyzing with

the polynomial degree set to 3 and the window size value decreased

to 15 (from 19) data points.

Figure 3-8 Electropherogram showing the alleles detected as

two peaks after decreasing the window size value

Example 2: Reducing Window Size/Increasing Polynomial Degree

Initial

Electropherogram

Figure 3-9 shows an analysis performed using a polynomial degree

of 3 and a peak window size of 19 data points.

Figure 3-9 Electropherogram showing four resolved peaks

detected as two peaks

Chapter 3 Algorithms

Optimizing Peak Detection Sensitivity

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 43

Effects of

Reducing the

Window Size Value

While Increasing

the Polynomial

Degree Value

Figure 3-10 shows the data presented in the figure above re-analyzed

with a window size value of 10 and polynomial degree value of 5.

Figure 3-10 Electropherogram showing all four peaks detected

after reducing the window size value and increasing the

polynomial degree value

Example 3: Extreme Settings

Effects of Extreme

Settings

Figure 3-11 shows the result of an analysis using a peak window size

value set to 10 and a polynomial degree set to 9. These extreme

settings for peak detection caused several peaks to be split and

detected as two separate peaks.

Figure 3-11 Electropherogram showing the result of an analysis

using extreme setting for peak detection

Chapter 3 Algorithms

Slope Thresholds for Peak Start/End Parameters

44 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Slope Thresholds for Peak Start/End Parameters

About These

Parameters

Use the Slope Threshold for Peak Start and Slope Threshold for Peak

End parameters to adjust the start and end points of a peak.

The values assigned to these parameters can be used to better position

the start and end points of an asymmetrical peak, or a poorly resolved

shouldering peak to more accurately reflect the peak position and area.

How These

Parameters Work

In general, from left to right, the slope of a peak increases from the

baseline up to the apex. From the apex down to the baseline, the slope

becomes decreasingly negative until it returns to zero at the baseline.

If either of the slope values you enter exceeds the slope of the peak

being detected, the software overrides your value and reverts to zero.

Guidelines for

Using These

Parameters

• For typical or symmetrical peaks, use a value of zero.

• For asymmetrical peaks, select values other than zero to better

reflect the beginning and end points.

• A value of zero does not affect the sizing accuracy or precision

of an asymmetrical peak.

Using These

Parameters

Note:

The size of a detected peak is the calculated apex between the

start and end points of a peak and does not change based on your settings.

Baseline

Apex

Increasingly

positive slope

(+)

Increasingly

negative slope

(Ð)

To move the… Then… Example

Start point

of a peak

closer to its apex

Change the Slope Threshold for Peak

Start value from zero to a positive

number.

End point of a peak

closer to its apex

Change the Slope Threshold for Peak

End value to a more negative number.

Chapter 3 Algorithms

Slope Threshold Example

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 45

Slope Threshold Example

Asymmetrical

Peak

Initial Electropherogram

The initial analysis using a value of 0 for both the Slope Threshold

for Peak Start and the Slope Threshold for Peak End produces an

asymmetrical peak with a noticeable tail on the right side.

Figure 3-12 Electropherogram showing an asymmetrical peak

Adjusting Slope Threshold for Peak End

After reanalyzing with a value of –35.0 for the Slope Threshold for

Peak End, the end point that defines the peak moves closer to its

apex, thereby removing the tailing feature. Note that the only change

to tabular data is the area (peak size and height are unchanged).

Figure 3-13 Electropherogram showing the effect of changing

the slope threshold for peak end

Chapter 3 Algorithms

Size-Matching/Size-Calling Algorithm

46 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Size-Matching/Size-Calling Algorithm

Size-Matching

Size-Calling

Algorithm

This algorithm uses a dynamic programming approach that is

efficient (runs in low polynomial time and space) and guarantees an

optimal solution. It first matches a list of peaks from the

electropherogram to a list of fragment sizes from the size standard. It

then derives quality values statistically by examining the similarity

between the theoretical and actual distance between the fragments.

Size-Matching

Algorithm

Example

Figure 3-14 shows an example of how the size-matching/calling

algorithm works using contaminated GeneScan

™

120 size standard

data.

Detected peaks (standard and contamination) are indicated by blue

lower bars along the x-axis. The size standard fragments as

determined by the algorithm (and their corresponding lengths in base

pairs) are designated by the upper green bars. Note that there are

more peaks than size standard locations because the standard was

purposely contaminated to test the algorithm. The algorithm correctly

identifies all the size standard peaks and removes the contamination

peaks (indicated by the black triangles) from consideration. The large

peak is excluded from the candidate list by a filter that identifies the

peak as being atypical with respect to the other peaks.

Figure 3-14 Size-matching example

Bars indicate detected peaks

Bars indicate size standard determined by algorithm

Chapter 3 Algorithms

Size-Calling Methods (Classic and Advanced Modes)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 47

Size-Calling Methods (Classic and Advanced Modes)

Typ es of S iz e

Calling Methods

The GeneMapper Software provides the following size calling methods:

■ Least Squares Method (2nd- and 3rd-Order) . . . . . . . . . . . . . 47

■ Cubic Spline Interpolation Method . . . . . . . . . . . . . . . . . . . . 49

■ Local Southern Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

■ Global Southern Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Least Squares Method

Overview Both Least Squares methods (2nd-Order and 3rd-Order) use

regression analysis to build a best-fit size calling curve. This curve

compensates for any fragments that may run anomalously.

Consequently, this method typically results in the least amount of

deviation for all the fragments, including the size standards and the

samples.

Depending on whether you choose the 2nd- or 3rd-Order Least

Squares Method in the Analysis Parameters dialog box, the resulting

size curve is either a quadratic or a cubic function. The software uses

the known standard fragments and the associated data points to

produce a sizing curve based on Multiple Linear Regression.

Advantages Figures 3-15 and 3-16 on page 48 show that in nearly all instances

the mobility of an individual DNA fragment is coincident with the

best curve fit of the entire data set. Stated differently, the mobility of

most DNA fragments is strictly length dependent. This method

automatically compensates for fragments that run anomalously.

The GeneMapper Software calculates a best-fit least squares curve

for all samples, regardless of the size-calling method you choose.

The curve is black in the Standard Sizing Curve window.

Note: The graphs in this section were generated using Version 3.5.1

of the GeneScan

Software. The results are similar to those obtained

when you use the GeneMapper Software.

Chapter 3 Algorithms

Size-Calling Methods (Classic and Advanced Modes)

48 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Figure 3-15 2nd-Order Least Squares size calling curve

Figure 3-16 3rd-Order Least Squares size calling curve

Chapter 3 Algorithms

Size-Calling Methods (Classic and Advanced Modes)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 49

Cubic Spline Interpolation Method

Overview

The Cubic Spline method forces the sizing curve through all the

known points of the selected size standard. Although this enforcement

produces exact results for the values of the standards themselves, it

does not compensate for standard fragments that may run anomalously.

Figure 3-17 Cubic Spline Interpolation Method

Possible Local

Sizing Inaccuracy

Mobility of any DNA fragment can be affected by its sequence, and

by secondary and tertiary structure formation. If any internal size

standard fragment has anomalous mobility, the Cubic Spline method

may exhibit local sizing inaccuracy.

For example, assume that a standard fragment is close in molecular

length to an unknown sample fragment. Assume further that the

standard fragment runs anomalously. The Cubic Spline method

assigns the official value to this standard fragment, even though it

may be slightly incorrect. The size of the unknown fragment is then

likely to be calculated incorrectly as well.

Note: This method does not determine the amount of sizing accuracy

error.

Chapter 3 Algorithms

Size-Calling Methods (Classic and Advanced Modes)

50 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Local Southern Method

Overview The Local Southern method determines the sizes of fragments by

using the reciprocal relationship between fragment length and

mobility, as described by E. M. Southern (1979).

Figure 3-18 Local Southern Method

Local Southern

Method Equation

The equation attempts to describe the reciprocal relationship between

the mobility, m, and the length, L0, of the standard fragments.

L = [c/(m–m0)] + L0

How The Local

Southern Method

Works

This method, which is similar to the Cubic Spline method, uses the

four fragments closest in size to the unknown fragment to determine

a best-fit line value. Only the region of the size ladder near the

fragment of unknown length is analyzed.

Note: Size estimates may be inaccurate if any of the standard

fragments run anomalously.

Chapter 3 Algorithms

Size-Calling Methods (Classic and Advanced Modes)

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 51

In the Local Southern method:

• The fitting constants of the curve are calculated for each group

of three neighboring points on the standard. A separate curve is

created for each set of three points.

• A curve is then created by using three standard points (two

points below and one point above the fragment), then a fragment

size is determined.

• Another curve is created by looking at an additional set of three

points (one point below and two points above the fragment),

then another value is assigned.

• The two size values are averaged to determine the unknown

fragment length.

Chapter 3 Algorithms

Size-Calling Methods (Classic and Advanced Modes)

52 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Global Southern Method

Overview This method is similar to the Least Squares method in that it

compensates for standard fragments that may run anomalously. The

method creates a best-fit line through all the available points, and

then uses values on that line to calculate the fragment values.

Figure 3-19 Global Southern Method

Global Southern

Method Equations

How the Global

Southern Method

Works

All points in the standard are weighted equally, and the curve is not

constrained to go through any specific point. The software can

analyze a large range of fragment sizes with this method. For best

results, use a standard that brackets all the fragments of interest.

Equation Description

Attempts to describe the reciprocal

relationship between the mobility, m,

and the length, L0, of the standard

fragments.

The fitting constants L0, m0, and c

are calculated by a least-squares fit

to minimize the left side quantity.

Lcmm0–()⁄[]L0+=

iLi c mi m0–()L0+()⁄[]–{}

∑

Chapter 3 Algorithms

Allele-Calling Algorithms

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 53

Allele-Calling Algorithms

Overview Final allele calls are based on a consensus between a variety of

different allele-calling algorithms. Each caller has a different design

philosophy such that it excels in a particular data regime but not in

others. Allele-calling algorithms involve envelope detection,

optimization of parametric models, and rule-based systems.

Types of Allele-

Calling Methods

The GeneMapper Software provides following allele-calling methods:

■ Microsatellite Analysis Methods . . . . . . . . . . . . . . . . . . . . . . 54

■ SNPlex™ System Analysis Methods . . . . . . . . . . . . . . . . . . . 55

– Rules Genotyping Algorithm (see page 55)

– Model Genotyping Algorithm (see page 56)

Chapter 3 Algorithms

Allele-Calling Algorithms

54 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Microsatellite Analysis Methods

Example Output of

Different Allele-

Calling Algorithms

Figure 3-20 on page 54 shows an example of three different allele-

calling algorithms for 16 samples. User annotations are indicated by the

(red) circles, and allele caller outputs are indicated by the (green, black,

and blue) asterisks. Note that consensus between multiple callers

virtually ensures that the calls are correct. In samples (i) and (p), the

algorithms have not made a call because they determined that the data

are too complex to act on. Here the blue asterisks show the calls

transmitted to the user. Low-quality values are reported because in both

cases the first algorithm did not call, and in (i), the black caller does not

agree with the blue. Despite these conditions, however, the calls are

correct. The low-quality values alert the user to potential problems such

as the spurious peak in (i) and the high background in (p).

Figure 3-20 The effect of three different allele-calling algorithms

on 16 different samples

Chapter 3 Algorithms

Allele-Calling Algorithms

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 55

SNPlex

™

System Analysis Methods

Overview The GeneMapper Software provides the following allele-calling

methods for SNPlex System Analysis:

■ Rules Genotyping Algorithm . . . . . . . . . . . . . . . . . . (see below)

■ Model Genotyping Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 56

Rules Genotyping

Algorithm

The Rules (Maximum-Likelihood or ML) SNP genotyping algorithm

consists of several processes: genotype cluster identification, sample

cluster classification, and confidence value assignment.

Figure 3-21 Rules algorithm block diagram

Because of small systematic differences in assay performance

between the alleles in a SNP, fixed classification boundaries between

the clusters do not give accurate results for most, if not all, current

SNP detection platforms. Clustering, like all calibration methods, can

correct for systematic errors, but not random errors. Random errors

are estimated and assigned appropriate p-values.

Input: Allele 1 and 2 Intensities

Allele Intensity Correction

Estimate Model Parameters

• Number of clusters

• Mean, variance of each cluster

• Allele frequency

Output: Genotypes and

Quality Values for all Points

and Plate Score (optional)

Filter Samples and Recluster

Done

Compute Most Likely Genotype

and Confidence Values

• Outlier probability

Detect Outliers

Resampling

(Exclude Outliers)

First Pass

Second Pass

Chapter 3 Algorithms

Allele-Calling Algorithms

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Genotype Cluster Identification

The cluster identification stage uses

a priori

knowledge of the

intensity relationship and frequency distribution of SNP alleles to

construct a most likely model of the SNP assays generating in a given

data set. Systematic parameters such as allele assay strength and

other chemistry factors are estimated. If the most likely model fit is

too poor, the entire data set is rejected.

Sample Cluster Classification and Confidence Value Assignment

The best fit model is used to classify each point into its most likely

class, or genotype. The likelihood of class membership (genotype)

can be derived in the same step.

Post Processing, Filtering, and Reiterations

Genotype and sample filters, bootstrap resampling, and several

reiterations are used to ensure the accuracy of the model fit and

classifications.

Model Genotyping

Algorithm

The Model genotyping algorithm consists of several processes:

normalization, allele calling, and confidence value generation.

Normalization

Normalization is a run-based algorithm that removes systematic

variation of peak height by allele and sample. This process is

accomplished by comparing the data to a model in which the sum of

allele peaks for any given sample is a constant. Over many repeated

measurements of a given sample or allele, systematic variations from

this a priori model can be quantified and later removed. In the

process of normalization, an overall scale factor can be selected,

which can be set to one. This utility is demonstrated in Figure 3-22

on page 57 where the output from 20 runs can be overlaid to

understand the random, as opposed to systematic, behavior of

signals. For each run 2 ∞ 48 ∞ 96 = 9216 measurements were

normalized with 193 parameters, or approximately one parameter for

each 48 measurements.

Chapter 3 Algorithms

Allele-Calling Algorithms

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 57

Figure 3-22 Pre- and post- normalization data. Shown are 80,000

measurements of SNPlex System data overlaid in normalized allele

coordinates. In the left plot, data are normalized only by run. In the

right, the data are fully normalized by sample and allele.

Genotyping

The random error remaining in the data after normalization is relatively

small when compared with the separation of the genotype coordinates,

as seen in Figure 3-23. Each measurement is assigned the genotype for

which it has the highest probability.

Figure 3-23 Training data distribution

Allele 2

Homozygotes

Heterozygotes

Allele 1

Homozygotes

Chapter 3 Algorithms

Allele-Calling Algorithms

58 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Final Genotyping and Confidence Value Assignment

The confidence value is a quantitative estimate of the probability of a

correct call (that is one minus the estimated error rate). This single

parameter can be used to optimize the trade-off between throughput

and accuracy for a particular data set, as shown in Figure 3-24.

Figure 3-24 Accuracy-throughput trade off

100

99.8

99.6

99.4

99.2

99.0

0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.0

Quality Value

Fraction Accuracy (%)Net Accuracy (%)

Fail Pass

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 59

Glossary

ABB (Automatic

Bin Builder)

The first step in accurate allele assignment. After sample files are

collected, bins are created by the ABB based on the chosen panel

information and successive allele calls from sample file collection.

As each sample file in the collection is processed, the bin definitions

are refined to reflect the actual data.

access control list (See Security Group)

Admin profile A pre-configured profile that cannot be removed and that has execute

access to all functions. Initially associated with the “admin” user. (A

user must always have an assigned profile.)

Admin security

group

A pre-configured security group that cannot be removed. This

security group has been granted all rights to all data, to provide a way

for at least one user to have “admin” access to all data.

Admin User Group A pre-configured user group that cannot be removed and that is

always associated with the Admin security group.

Administrator User A pre-configured user that cannot be removed and that is always

associated with the Admin user group.

AFLP

Amplified Fragment Length Polymorphism. A DNA fingerprinting

technique that allows the comparison of the DNA from different

organisms. DNA fragments of varying lengths are created by

cleaving an organism’s DNA with restriction enzymes; a specific

subset of these fragments are amplified and analyzed for comparison

purposes.

Glossary

60 GeneMapper

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Algorithm A set of ordered steps for solving a problem, such as a mathematical

formula or the instructions in a program. The terms algorithm and

logic are synonymous, where both refer to a sequence of steps to

solve a problem. However, an algorithm is an expression that solves a

complex problem rather than the overall input-process-output logic

of typical business programs.

All User group A user group that contains all users. A user cannot be disassociated

from the user group.

allele One of two or more alternate forms of a marker or gene.

Reference alleles are all alleles or bins created in a bin set in the

GeneMapper Software. They are denoted by a red cross hatch in

the Panel Manager.

Project alleles are all alleles detected in sample data in a project in

the GeneMapper Software. They are denoted by a blue asterisk in the

Panel Manager.

allele call Identification of the specific allelic form of a marker.

allele-calling Identification of alleles based on bin definitions; genotyping;

GeneMapper

Software analysis

allelic ladder A sample of DNA containing most possible alleles for a specific

marker or set of markers. Used to create a sample file that the

GeneMapper Software can use to genotype or make allele calls on

sample data.

Within the GeneMapper Software, you select a Sample Type of

Allelic Ladder for the sample file generated using an allelic ladder.

analysis method A collection of user-defined parameters that determine the bin set and

analysis algorithms.

analysis

parameters

A collection of user-defined settings (including analysis method, size

standard, and panel) that determine the sizing and genotyping

algorithms used by the GeneMapper Software to analyze all sample

files in a project. Also called project settings.

Glossary

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 61

association Two identifiers combined are said to be associated. A user can be

associated with a user group. A user group associated with a security

group yields a set of data rights.

audit event A single permanent change to one or more attributes of an object.

Includes creating a new instance of an object or deleting an exiting

one.

audit map An object associated with an object type; used to tell the audit

component how to audit an object type.

audit object A collection of data defined by an application. Also referred to as an

object.

audit record The description of a single audit event.

autopanelizer A feature that uses reference data generated by the Primer Focus

kit

to quickly define new SNP markers and bin sets.

bin Within the GeneMapper Software, a fragment size (bp) or basepair

range and dye color that define an allele within a marker. You create a

bin for each possible allele associated with a marker.

bin set Within the GeneMapper Software, a collection of bins (allele

definitions), typically specific to a set of experimental conditions,

usually an instrument. Bin sets are available inside a kit.

cache An “in memory” representation of the access control data. The

Admin Tool modifies the data in the cache. When the Admin Tool or

Admin API issues the “save” command, the data in the cache are

written to the data store.

challenge A term from user authentication indicating that the user is asked to

provide identification (typically by entering a password).

chromosome A long stretch of coiled strands of DNA and proteins containing

many genes. Human DNA is contained within 23 pairs of

chromosomes.

Glossary

62 GeneMapper

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control See “positive control” on page 64.

See “negative control” on page 64.

Control Security

Group

The security group assigned to an Access Control administrative

identifier (user, user group, security group, profile). This security

group is used to determine access by a user to the administrative data

in the Administrative GUI and API.

data access control The part of access control that administers access to user data.

Data group (See Security Group)

data rights Properties that define the type of access a user has to a piece of data.

database One form of offline storage.

diploid Having two sets of chromosomes and, therefore, having two alleles

per marker or gene. Human cells (other than egg and sperm cells) are

diploid.

dye set A set of four to five different colored dyes. A specific dye set is used

to label DNA fragments or markers in matrix standards, installation

standards, and chemistry kits.

electropherogram A graphical representation of the intensity (y-axis) of bands produced

in a single gel lane or capillary as a function of time (x-axis).

electrophoresis A technique used to separate molecules by using an electric field to

pass those molecules through a porous matrix.

gene The basic unit of heredity that carries the genetic information for a

given RNA molecule or protein.

genome All the DNA contained in an organism or cell, including both the

chromosomes in the nucleus and the DNA in the mitochondria.

genotype The set of allele calls for specific markers or genes within an

organism. (noun)

To determine the allele calls for specific markers or genes within an

organism. (verb)

Glossary

GeneMapper

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haploid Having one set of chromosomes and, therefore, having one allele per

marker or gene. Human egg and sperm cells are haploid.

heterozygous Having two different alleles for a specific marker or gene.

homozygous Having two identical alleles for a specific marker or gene.

installation

standard

A collection of known genetic markers, labeled with dyes from a

specific dye set, used to test the function of a genetic analyzer.

kit Within the GeneMapper Software, a group of panels.

LMS Linkage Mapping Set; Applied Biosystems chemistry using

dinucleotide repeat microsatellite markers

locus The chromosomal location of a genetic marker or gene.

marker A known segment of DNA that has two or more allelic forms. A

marker exists at a known chromosomal loci and can be a gene or a

non-gene. See also microsatellite and SNP.

Within the GeneMapper Software:

A microsatellite marker is defined by a name, fragment size range

(bp), dye color, and repeat length.

A SNaPshot kit analysis marker is defined by a name and fragment

size range (bp).

matrix standard A collection of known DNA fragments, labeled with four to five

different colored dyes from a specific dye set. The matrix standard is

run on a genetic analyzer and used for spectral calibration of sample

fragments that are run on the same instrument and labeled with the

same dye set.

microsatellite Microsatellite markers, also known as short tandem repeats (STRs),

are polymorphic DNA loci consisting of a repeated nucleotide

sequence. The repeat sequence can be from 2 to 7 base pairs long.

The number of repeat units varies within a population, thereby

creating multiple alleles for a microsatellite locus.

Glossary

64 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

negative control A blank sample that contains no DNA, but all other reagents used in

the experiment. It can indicate if any contamination came from

sample preparation.

In the Sample tab of GeneMapper Software, you select a Sample

Type of Negative Control for the sample file generated from the

negative control. Additionally, in the Panel Manager you define the

negative control when creating markers.

panel A group of markers. Within the GeneMapper Software, you associate

a panel with a bin set to provide bin definitions for the markers.

phenotype The physical manifestation of a genotype.

polymorphism Differences between organisms’ or individuals’ DNA. Variations of

allele calls.

positive control A sample that contains DNA with known alleles for specific markers.

Its purpose is to verify that the PCR amplification, electrophoresis,

and GeneMapper Software analysis worked correctly.

In the Sample tab of GeneMapper Software, you select a Sample

Type of Positive Control for the sample file generated from the

positive control. Additionally, in the Panel Manager you define the

positive control when creating markers.

primer A single-stranded piece of DNA or RNA that anneals to a

complementary section of DNA and serves as a starting point for

chain extension by DNA polymerase.

primer focus Within the GeneMapper Software, you select a Sample Type of

Primer Focus for the sample file generated using the Primer Focus

kit. The GeneMapper Software uses this file to automatically create

bins using the Auto Panel feature.

Primer Focus

kit An Applied Biosystems kit containing reagents used to create and

amplify all four possible alleles of any SNP marker. The kit allows

you to take advantage of the Auto Panel feature in the GeneMapper

Software to automatically create bins for each allele in a SNaPshot

kit analysis.

Glossary

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 65

probe A DNA or RNA fragment that has been labeled in some way (for

example, fluorescent or radioactive), then used in a molecular

hybridization assay to identify DNA or RNA sequences that are the

same or closely related to it in sequence.

profile An identifier that gives an administrator the ability to grant or revoke

access to functions.

project Within the GeneMapper Software, a collection of sample files and

the analysis parameters for genotyping them.

project settings See “analysis parameters” on page 60.

project settings Parameters set by the user to prepare a project for analysis.

reference samples All or a subset of the actual samples. The reference samples typically

contain all of the alleles present in the sample set and are used to

create a bin for each allele within the GeneMapper Software.

rights Properties that define whether a user has access to data or a function.

Security group An identifier that can be associated with a user group to confer a set

of data rights.

Security ID The universal identifier of the security group and the preferred name

of the column in an application table that holds the security group ID.

SFNF Sample File Not Found

silent auditing Automatic audit record creation (without prompting of the user).

size match editor A window in GeneMapper

Software that allows users to examine

size-standard electropherograms, edit the identification of size-

standard peaks, and view the size-calling curve.

size standard A collection of DNA fragments of known lengths within a range (for

example, 50 to 400 bp) all tagged with the same dye. The size

standard is co-injected into the genetic analyzer capillary with the

sample, then used to size the sample data. All Applied Biosystems

size standards are labeled with a red or orange dye.

Glossary

66 GeneMapper

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SNaPshot

kit An Applied Biosystems kit containing reagents used to PCR amplify

any SNP markers, using single-base-extension technology. Sample

files can then be sized and genotyped by using a SNaPshot analysis

in the GeneMapper Software.

SNaPshot

System

Multiplex Analysis

Primer extension-based chemistry for SNP validation

SNP Single Nucleotide Polymorphism. A marker consisting of a single

base pair that varies, thereby creating up to four alleles of the marker.

In this document, SNP refers to SNaPshot

system markers and

SNPlex

™

systems

SNP Single-Nucleotide Polymorphism (used)

SNPlex™ System High-throughput assay for genotyping.

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 67

Index

ABB (automatic bin builder), definition 59

access control list, definition 59

admin profile, definition 59

admin security group, definition 59

admin user group, definition 59

admin user, definition 59

ADO (Allele Display Overflow) PQV 8

AE (Allele Edit) PQV 8

algorithms

allele-calling 36, 53

binning 36

definition 60

overview 36

peak detection 36

size-matching 36

all user group, definition 60

Allele Display Overflow PQV 8

Allele Edit PQV 8

Allele Number PQV 9

allele-calling

algorithm 53

definition 60

AN (Allele Number) PQV 9

analysis method, definition 60

Applied Biosystems

contacting viii

customer feedback on documentation viii

Information Development

department viii

Technical Support viii

association, definition 61

assumptions for using this guide v

audit event, definition 61

audit map definition 61

audit object, definition 61

audit record, definition 61

autopanelizer, definition 61

BD (Broad Peak) PQV 9

BIN (Out of Bin Allele) PQV 10

bold text, when to use v

Broad Peak PQV 9

cache, definition 61

CC (Control Concordance) PQV 11

challenge, definition 61

Control Concordance PQV 11

control security group, definition 62

conventions

bold text v

for describing menu commands v

IMPORTANTS! vi

in this guide v

italic text v

Notes vi

user attention words vi

Cross Talk PQV 32

customer feedback, on Applied Biosystems

documents viii

data access control, definition 62

data flow, genotyping algorithms 36

data group, definition 62

Index

68 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

data rights, definition 62

database, definition 62

documentation, related vii

Double Peak PQV 12

DP 12

DP (Double Peak) PQV 12

EPT data, displaying 5

examining peak definitions 39

example output of different allele-calling

algorithms 54

Genotype Quality PQV 13

genotyping algorithms 36

GQ (Genotype Quality) PQV 13

help, online, accessing vii

Information Development department,

contacting viii

italic text, when to use v

LMS, definition 63

Low Peak Height PQV 15

LPH (Low Peak Height) PQV 15

Marker Quality 14

Matrix Not Found PQV 16

menu commands, conventions for

describing v

MNF (Matrix Not Found) PQV 16

MSDSs, obtaining viii

Narrow Bin PQV 18

NB (Narrow Bin) PQV 18

numeric quality metrics, displaying 7

OBA (One Basepair Allele) PQV 18

Offscale PQV 19

One Basepair Allele PQV 18

online help, accessing vii

optimizing peak detection sensitivity 41

OS (Offscale) PQV 19

Out of Bin Allele PQV 10

Overlap PQV 20

OVL (Overlap) PQV 20

peak definitions, examining 39

peak detection 38

effects of extreme settings 43

guidelines for use 39

optimizing sensitivity 41

parameters 38

peak window size 38

polynomial degree 38

slope threshold 44

peak detection sensitivity, reducing window

size 41

Peak Height Ratio PQV 21

PHR (Peak Height Ratio) PQV 21

polynomial degree 38

peak detection 38

varying 39

window size value 40

possible local sizing inaccuracy 49

Process Quality Values (PQV) 2 to 32

displaying as numbers 7

profile, definition 65

Index

GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide 69

project settings, definition 65

Raw Data, displaying 5

reducing window size and increasing

polynomial degree 42

rights, definition 65

Sample File Not Found PQV 22

sample information, displaying 5

security group, definition 65

security ID, definition 65

SFNF (Sample File Not Found) PQV 22

Sharp Peak PQV 23

SHP (Sharp Peak) PQV 23

silent auditing, definition 65

Single Peak Artifact PQV 24

size calling 53

advanced method 47

classic method 47

cubic spline interpolation method 49

global southern method 52

least square method 47

local southern method 50

overview 47

size match editor, definition 65

Size Standard Not Found PQV 23

size-matching/size-calling algorithm 46

Sizing Quality PQV 25

slope threshold

asymmetrical peak 45

peak end parameters 44

peak start parameters 44

SNaPshot

analysis, definition 66

SNF (Size Standard Not Found) PQV 23

SNP

definition 66

SNPlex™ system analysis, definition 66

SP (Split Peak) PQV 24

SPA (Single Peak Artifact) PQV 24

Spectral Pull-Up PQV 25

Split Peak PQV 24

SPU (Spectral Pull-Up) PQV 25

SQ (Sizing Quality) PQV 25

Technical Support, contacting viii

text conventions v

training, information on viii

user attention words, described vi

varying polynomial degree 39

window size value, increasing 40

XTLK (Cross Talk) PQV 32

Index

70 GeneMapper

Software Version 4.1 Reference and Troubleshooting Guide

Part Number 4403673 Rev. A 04/2009

Technical Resources and Support

For the latest technical resources and

support information for all locations,

please refer to our Web site at

www.appliedbiosystems.com/support

Applied Biosystems

850 Lincoln Centre Drive

Foster City, CA 94404 USA

Phone 650.638.5800 | Toll Free 800.345.5224