Section 1 - Gene Mapper ID

NIJ EXPERT SYSTEMS TESTBED PROJECT

APPLIED BIOSYSTEMS GeneMapper®ID WORKFLOW

GMID 3.2 Workflow 10/26/2007 Page 1 of 1

Import by plate Import AB Data Collection (.fsa) file • Samples compared to composite ladder.

Import by run • Samples compared to ladder within run.

Sample Type Sample Sheet Setup Sample Export (For CODIS) Analysis Method Panel (Must Match Analysis Method)

• General Settings - assigns alleles per ladder designation • CODIS Settings - assigns alleles per CODIS import specifications

Size Standard

Analyze data

Investigate “Low Quality” Sample Size Standards Sample Tab

Investigate “Low Quality” Sample Genotypes Genotypes Tab

General Table – includes all information displayed in GeneMapper® ID table Export Table CODIS Table – includes complete sample information only – partial profiles, ladders and controls have been removed

National Institute of Justice’s Expert Systems Testbed Project

GeneMapper® ID v 3.2

Background Advanced functions of GeneMapper® ID such as PQV’s (Process Component- Based Quality Values) allow GeneMapper® ID to be considered an expert system by some. The PQV system automatically assigns values to the quality of data based on size and allele calling. Using the software as an expert system an analyst evaluates PQV’s as a method of identifying samples that fall outside defined thresholds. Only the samples with PQV flags would require analyst review.

• HID analysis requires the presence of at least one allelic ladder per project. If using the 3100 genetic analyzer and saving a run file as 16 samples then one ladder must be present within each 16 samples. When multiple ladders are present in a run folder the average of all ladders is used to calculate allelic bin offsets.

• Allelic ladder samples from a single run folder are considered to be from a single run.

• When multiple run folders are imported into a GeneMapper® ID project only ladders within the same run folder are used for calculating allelic bin offsets and genotyping.

• Allelic bin definitions are stored in the AmpFlSTR panels in the panel manager.

• Allelic ladder samples must be identified as “allelic Ladder” in the sample type column in a project. Failure to do so will result in failed analysis.

• Injections containing the allelic ladder should be analyzed with the same analysis method and parameters used for all other samples.

• Alleles not found in the AmpFlSTR allelic ladders do exist. These off-ladder alleles may contain full and/or partial repeat units. An off-ladder allele is defined as an allele falling outside of the ±0.5 b.p. window of any known allelic ladder allele or virtual bin.

Procedure Aspects of analyzing data using GeneMapper® ID

• Import data; Define project parameters • Evaluate Data

o Check Size Standard o Review Genotypes o Concordance Check

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Import Data; Define Project Parameters

1. Launch GeneMapper® ID using the icon on the Desktop. Enter User ID as analyst and Password as dna1.

2. From the Toolbar go to File > Add Samples to Project. Navigate to the

DEMO DATA folder on the desktop. Double click to open the file folder. Select the Identifiler folder containing the .fsa files.

3. Click on “Add to list”. Click ‘Add” to import the samples files into the GMID

project.

4. Select NEST Table in the Table Settings drop down menu.

5. Within the Samples Tab, designate the sample types (i.e. Allelic Ladder, Positive Control, Negative Control, etc.) using the drop down option for each sample in the Sample Type column.

Note: Allele ladder samples must be identified as “allelic ladder” in the sample type column for proper analysis to occur. Note: Only one Positive Control type is allowed. The chosen profile is set up in the Panel Manager options.

6. Click on row one of the Specimen Category column. Using the drop down option, select Convicted Offender in the first cell. Click in the Specimen Category header cell to highlight the column. Use control + D to fill down the remaining cells. Using the drop down option, select No Export in the Specimen Category column cells for all ladders, controls, samples that should not be included for the CODIS cmf import.

Note: Using Specimen Category is only necessary when using GMID to analyze data for CODIS upload.

7. Click on row one of the Analysis Method column. Using the drop down

option, select NEST in the first cell. Click in the Analysis Method header cell to highlight the column. Use control + D to fill down the remaining cell.

Note: If analyzing data for eventual CODIS uploading purposes, then select the NEST CODIS Analysis Method.

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8. Click on row one of the Panel column. Double click on the AmpFLSTR_Panels_v1. Double click on the chemistry specific panel (for example, Identifiler_v1). Click in the Panel header cell to highlight the column. Use control + D to fill down the remaining cells.

Note: If analyzing data for eventual CODIS uploading purposes, then select the Identifiler_CODIS_v1.1 panel under the AmpFLSTR_Panels_v1.1 option.

9. In the Size Standard column, select chemistry specific size standard. (for

example, CE_G5_HID_GS500 for Identifier).

10. Analyze the data by clicking on the green arrow. Name the project ID Demo Data and click OK. A green bar will progress down the page indicating the samples are being analyzed.

Evaluate Data Color-coded, sortable PQVs are displayed on the Samples (size calling) and Genotypes (allele calling) tabs following analysis allowing quick isolation of problem samples that require further examination. There are multiple, individual parameters on the samples and genotypes tabs which are assigned a PQV. In addition, an overall value is assigned to the samples tab (Sizing Quality) and the genotypes tab (Genotyping Quality). The values which can be displayed are:

• PASS (green square) – Displayed when the sample meets or exceeds parameters.

• CHECK (yellow triangle) – Displayed when there are problematic components.

• LOW (red octagon) – Displayed when the result falls below the defined threshold.

• - Displayed as an indicator rather than a value. Holding the cursor over a column header displays the full name of the PQV column. Overall PQV results are reported in the column labeled SQ on the samples tab page and as GQ on the genotypes tab page. The SQ and GQ quality indicators have the following meaning:

Green – Good data (numerical range 0.75-1.0) Red – Low quality data (numerical range 0.0-0.75)

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See Figure 1

Figure 1

Check Size Standards The Advanced peak detection algorithm in GeneMapper® ID includes a sizing quality and the (SQ) value to assess the sizing of a sample. The SQ value assigned by the software can be overridden and an incorrect size standard peak can be reassigned using the Size Match Editor. Overridding the SQ value sets the value to 1.0; the override is indicated by a check mark in the SQO column.

1. When analysis is complete check the Sizing Quality for all samples. Green squared indicates that the samples passed the sizing criteria. The size standards for these samples do not need to be reviewed.

2. When the sizing quality is low the size match editor can be used to adjust

the size standard. Typical problems include a peak shift, missing peak or an extra peak (spike or pull up).

Note: To view raw data, go to View > Raw Data. Samples with low sizing quality values cannot be displayed in the Plot window. Only raw data can be viewed for low quality samples.

3. When the SQ is flagged yellow or red, highlight the row for that sample and select Analysis > Size Match Editor or click on the Size Match Editor

icon in the Tool bar to open the Size Standard window. See Figure 2

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Figure 2 Note: To ease viewing of the Size Standard, maximize the Size Standard window by clicking and dragging in the corner of the window. The X and Y scale of the size standard window may be changed by moving the cursor over either axis until a magnifier is seen. Click and drag to zoom.

4. To edit a particular size standard peak click the peak to be edited. Right-click the peak to open the editing pop-up window and choose to add, delete or change the selected peak.

5. If adding or changing a peak move the cursor to the right to open the

select size sub-menu. Choose the desired value from this menu.

6. Go to Tools > Check Sizing Quality or use the icon. If the size standard peaks are sized correctly after editing but the quality score is still below passing, click the override SQ button to set the SQ to 1.0.

• If deleting a label from a peak select Tools > Auto Adjust Sizes or to adjust the size of other peaks automatically to the right of the selected peak.

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• To delete all labels and manually assign peak values select Edit >

Delete all Size Labels or . • Click Apply if editing a second size standard peak or Click OK if

finished.

7. If the sample should not be considered for interpretation due to problems within the raw data, the sample file can be deleted from the project using Edit > Delete from Project.

NOTE: All user edited size standard samples automatically have a sizing quality of 1.0 and display a check mark in the SQO column in the samples tab page. NOTE: If a change has been made to a sample’s size standard the sample needs to be reanalyzed. This can be done by clicking on the green arrow.

Review Genotypes Genotype PQVs and Genotype Quality (GQ) are displayed in the Genotypes Table and Plots. PQVs provide an easy way to scan through data and determine problematic markers (loci). The Genotype PQVs are used to calculate the overall Genotype Quality. Samples with green Genotype Qualities do not need further assessment. Green indicates a high confidence level that allele calls are accurate.

1. To view the Genotype Table, click the Genotypes Tab from the main project window.

2. Select the Passed Table from the Table Settings drop down menu. This will

show all the data that contain a Passed (green) GQ score and these data do not need to be reviewed.

NOTE: A laboratory could export this table as a record of the data that did not require any human analysis.

3. Select the Low Quality Table from the Table Settings drop down menu. This will show all the data that contain a Low Quality (red) GQ score.

4. Highlight the samples that need to be reviewed by selecting all rows.

5. Select the Samples Tab and then click the Display Plots icon to open the

Samples Plot Window.

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6. Select the HID Genotyping Plot from the Plot Settings drop down menu. This setting will show the Samples Plot with all the dye colors separated and will have the Genotypes Table showing the Genotype PQVs for all samples that were “Low Quality”.

7. Highlight the marker of interest in the Genotypes Table at the bottom of the

screen. This will show the dye channel containing the marker of interest in the Plot Display above and will highlight the peak(s) of interest.

8. Zooming in to view the marker of interest may aid in evaluating the data.

Hold the cursor arrow above the X-axis until a magnifying glass appears. Click and drag to enlarge the marker range. Red triangles denote the start and end of each of each marker. The same can be done to adjust the Y-axis by holding the cursor arrow to the left of the Y-axis until a magnifying glass appears.

See Figure 3

Figure 3

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9. If a peak is designated as an allele incorrectly, highlight the peak by left clicking on it, then right click. Select Delete Allele(s) on the menu that opens. Type in a reason in the Delete Allele Comment window and click OK.

See Figure 4

Figure 4

10. This will remove the designation and the GQ score will be overridden. This will be indicated by a Good (green) GQ and an X will show in the Allele Edits Column on the Genotypes Table.

See Figure 5

NOTE: The labels used in the Samples Plot or Genotypes Plot can be changed to show information that may be helpful to the analyst such as size, height, designation, Allele Edit comments, etc.

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Figure 5

11. An OL allele (microvariant) can be compared to the ladders in that project

by viewing the sample containing the OL allele and the ladder that was run with it. This is done by selecting the sample and the ladder in the Samples Tab by holding down the CTRL key and clicking on each of them with the mouse and then selecting to Display Plots.

12. In the Display Plots window, select the Ladder Overlay from the Plot Setting

drop down menu. This will show all the peaks for both samples selected with the Sizing Table below.

13. Select the Hide All dye color button at the top of the screen to hide all the

peaks. Then select the dye color containing your peak of interest by clicking on the button for that color.

14. Select the peak of interest in the Sizing Table at the bottom. This will

highlight the peak in the Samples Plot above and make it easier to identify your peak.

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15. Zooming in to view the peak of interest may aid in evaluating the data. Hold

the cursor arrow above the X-axis until a magnifying glass appears. Click and drag to enlarge the sizing range. The same can be done to adjust the Y-axis by holding the cursor arrow to the left of the Y-axis until a magnifying glass appears.

See Figure 6

Figure 6

16. Select your peak of interest and the ladder peaks on either side of it by

clicking and dragging the mouse around those peaks. Then type Crtl+S to sort the Sizing Table so that it contains only the information for these peaks.

See Figure 7

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Figure 7

17. It is possible to change the allele call for the OL allele (microvariant) after

performing the calculation using the information in the Sizing Table.

18. Select the HID Genotyping from the plot setting dropdown menu. Highlight the OL allele peak by left-clicking on the peak or clicking on the peak in the Sizing Table.

19. Right-click on the peak. Select Rename Allele on the menu that opens.

Move the mouse to the right and select Custom from the menu that opens. Type the name of the allele designation that you have calculated in the Create Custom Allele Name box and select OK.

See Figure 8

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Figure 8

20. Type in a reason in the Edit Allele Comment window and click OK.

See Figure 9

Figure 9

21. This will Change the designation and the GQ score will be overridden. This will be indicated by a Good (green) GQ and an X will show in the Allele Edits Column on the Genotypes Table.

See Figure 10

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Figure 10 22. If there is a peak that needs to be investigated relative to the other dye

channels in the sample select to Combine the Dyes using the shortcut key at the top, Ctrl+7, or via View > Plots > Combine Dyes. This setting will show the Samples Plot with all the dye colors overlaid and will have the Genotypes Table showing the Genotype PQVs for that entire sample.

23. Highlight the marker of interest in the Genotypes Table at the bottom of the

screen. This will highlight the peak(s) of interest. 24. Zooming in to view the peaks of interest may aid in evaluating the data.

Hold the cursor arrow above the X-axis until a magnifying glass appears. Click and drag to enlarge the sizing range. The same can be done to adjust the Y-axis by holding the cursor arrow to the left of the Y-axis until a magnifying glass appears and then clicking and dragging.

See Figure 11

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Figure 11

25. If the peak needs to be deleted or the allele designation needs to be edited, select to Separate the Dyes using the shortcut key at the top, Ctrl+8, or via View > Plots > Separate Dyes. Use steps 9-10 or 18-21 above, to delete or edit the peak, respectively.

26. If the sample passes laboratory requirements after reviewing the data and

no changes needed to be made, the GQ score can be manually overridden. This is done by highlighting the row of the marker and then right clicking on the row. A new window will open asking if the user wants to override the GQ of that marker.

See Figure 12

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Figure 12

27. After selecting Yes, the GQ score will turn to Pass (green).

See Figure 13

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Figure 13 28. After all Low Quality (red) GQs have been reviewed for that batch, close the

Samples Plot window. 29. Once analysis is complete, the Allele Edits Table can be selected from the

Table Setting drop down menu. This will show all samples that required human intervention.

See Figure 14

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Figure 14

Generate Output

Export Table

1. Select either the Samples Tab or the Genotypes Tab depending on which information is to be output.

Note: The table to be exported will contain all the information shown in the table being viewed and only that information. These settings can be changed in the GeneMapper Manager under the Table Settings tab.

2. Select File > Export Table from the toolbar.

3. Using the options on the left and the middle option screen, navigate to

where the file is to be saved. 4. From the drop-down menu select the Export File As format desired. 5. Enter the file name and click Export Table.

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See Figure 15

Figure 15

Export Table for CODIS

1. In the Sample Tab, designate the Specimen Category for all samples if not already completed.

Note: If there are samples that do not need to be exported, such as allelic ladders, positive controls, negative controls, etc., designate these as “no export”.

2. Select File > Export Table for CODIS from the toolbar. 3. Using the options on the left and the middle option screen, navigate to

where the file is to be saved.

4. From the drop-down menu select the Export File As format desired.

5. From the drop-down menu select the Source Laboratory.

6. From the drop-down menu select the Destination Laboratory.

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Note: The Specimen Category, Source Laboratory and Destination Laboratory can be user defined. This setting is in the CODIS Export Manager found under the Tools Menu.

7. Enter the file name and click Export.

See Figure16

Figure 16

Export Combined Table 1. Select the Samples Tab. 2. Designate the Specimen Category for all samples.

Note: If there are samples that do not need to be exported, such as allelic ladders, positive controls, negative controls, etc., designate these as “no export”.

3. Select File > Export Combined Table from the toolbar.

4. Using the options on the left and the middle option screen, navigate to

where the file is to be saved.

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5. From the drop-down menu select the Export File As format desired.

6. Select to display the data either as one line per marker or one line per sample.

7. Enter the file name and click Export Combined Table.

See Figure17

Figure 17

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GeneMapper®ID v3.2

Flag Name Flag Description Allele Edit (AE)

-The check box displays unchecked immediately after analysis. -The check box is checked when the marker allele calls have been edited by you.

Allele Display Overflow (ADO)

-The check box is checked when the number of alleles the marker calls exceeds the number to display previously set by you. -The user specifies how many alleles to display in the Table Settings Editor parameters. The default is two. -There are six columns for each allele to indicate name, size, height, area, mutation and comments.

Allele Number (AN)

-A flag is automatically displayed when the number of alleles exceeds the maximum legal number for the organism, or no alleles are found. That number is specified in the analysis method. (2) -Triggering this flag reduces the final PQV Genotype value (GQ parameter) to zero (“0” multiplier). -Not editable.

Broad Peak (BD)

-A flag is automatically displayed when the called alleles’ peak width is wider than a certain value. The default of this value is 1.5 basepair. -Not editable.

Out of Bin Allele (BIN)

-A flag is automatically displayed when the called alleles’ peak apex is out of the bin boundary. -Triggering this flag reduces the final PQV Genotype value. The default is 20% (0.2 multiplier) of what it would be otherwise. -For HID, this is labeled OL (off ladder alleles). -Not editable.

GMID 3.2 Rules 10.26.2007 Page 1 of 5


Control Concordance (CC)

-A flag is automatically displayed when the designated control sample does not exactly match the defined alleles for this marker in the panel being scored. -This feature serves as an internal control for quality assurance and allows you to see deviations in your controls. -This flag is used for all applications. -We recommend that you run the control sample at least once for every panel. -Not editable.

Genotype Quality (GQ)

-A red octagon is shown when the analysis for a sample is of low quality. -Pass, Check, or Low Quality flags are set by the Analysis Method. Not editable.

Low Peak Height (LPH)

-A flag is automatically displayed when the alleles are lower than the specified values. Low Homozygote = 200;Low Heterozygote = 100

Matrix Not Found (MNF)

-A flag is automatically displayed when no matrix file is found attached to the sample file. -Not editable.

Offscale (OS)

-A flag is automatically displayed when there are offscale peaks present within the marker size range. OS = >8192 in raw data -This flag is used for all applications. -Not editable.

Overlap (OVL)

-A flag is automatically displayed when a peak in the overlapped region is called twice. (It is possible to have two allele size ranges that overlap). -This feature serves as a warning for an allele calling error. -This is used for HID only.



Peak Height Ratio (PHR)

-A flag is automatically displayed when there are two alleles present and the ratio between the lower allele height and the higher allele height is below a certain level. -If there are more than two alleles present, the calculation iterates through all the peak pairs. -PHR can be set in the Peak Quality tab of the analysis method (Analysis Manager). PHR = 50%. -Not editable.

Sample File Not Found (SFNF)

-A flag is automatically displayed when no sample file is found. -Not editable.

Size Standard Not Found (SNF)

-A flag is automatically displayed when a size standard is not found. -Not editable.

Spectral Pull-Up (SPU)

-A flag is automatically displayed when the marker signal contains bleedthrough peaks (pull-up peaks). -Pull-up is when the peak height of the called allele peak is less than 5% of the larger peak within ± 1 data point. -The default is a conservative 10% to check all colors. The user can set this value. -This flag is used for HID, SNP, and Microsatellite. -Not editable.

Sizing Quality (SQ)

-A value is automatically displayed to indicate the sizing process quality. -The sizing quality is calculated based on the similarity between the size standard fragment pattern and the actual size standard peak distribution pattern in the sample. -Not editable. -This feature performs the following functions: -Identifies and eliminates the primer peaks based on peak shape -Performs size matching (ratio matching) -Makes a size calling curve using the Local Southern Method



Analysis Methods :NEST Allele Bin Set AmpFLSTR_Bins_v1 [√] Use marker-specific stutter ratio if available Marker Repeat Type: Tri Tetra Penta Hexa Cutoff Value 0.0 0.2 0.0 0.0 MinusA Ratio 0.0 0.0 0.0 0.0 MinusA Distance (From) 0.0 0.0 0.0 0.0 MinusA Distance (To) 0.0 0.0 0.0 0.0 Minus Stutter Ratio 0.0 0.0 0.0 0.0 Minus Stutter Distance (From) 0.0 3.25 0.0 0.0 Minus Stutter Distance (To) 0.0 4.75 0.0 0.0 Plus Stutter Ratio 0.0 0.0 0.0 0.0 Plus Stutter Distance (From) 0.0 0.0 0.0 0.0 Plus Stutter Distance (To) 0.0 0.0 0.0 0.0 Amelogenin Cutoff 0.25

From To Blue 0.0 0.0 Green 0.0 0.0 Yellow 0.0 0.0 Red 0.0 0.0

Range Filter

Orange 0.0 0.0 Peak Detector

Peak Detector Algorithm Advanced Analysis Partial Ranges Sizing 75-450

Smoothing and Baselining Light Baseline Window 51 Size Calling Method Local Southern Method

B: 50 G: 50 Y: 50 R: 50

Peak Amplitude Thresholds

O: 50 Min. Peak Half Width: 2 Polynomial Degree: 3 Peak Window Size: 15

Peak Start: 0.0 Slope Threshold Peak End: 0.0



Peak Quality

Homozygous min peak height 200.0 Heterozygous min peak height 100.0 Min peak height ratio 0.5 Max peak width (basepairs) 1.5 Pull-up ratio 0.4 Max expected alleles 2 Quality Flags

Spectral Pull-up 0.8 Broad Peak 0.8 Out of Bin Allele 0.8 Overlap 0.8 Control Concordance 1.0 Low Peak Height 0.3 Off-Scale 0.1 Peak Height Ratio 0.3

Pass Range: Low Quality Range:

Sizing Quality

0.75 to 1.0 0.0 to 0.75 Genotype Quality 0.75 to 1.0 0.0 to 0.75 The Genotype PQVs are used to calculate the overall Genotype Quality (GQ). The user can set level of importance of PQVs in the overall calculation of the Genotype Quality in the Quality Flags Tab of the Analysis Method Editor. PQVs are weighted from 0 to 1 The actual value of a PQV in the equation is :1 minus the weight

• 0 weight = no effect on the final GQ (1-0 = 1, 1 X the GQ value = GQ value, therefore no change)

• 1 = full effect (1-1 = 0, The GQ value X 0 = 0; 0 = low GQ value flag)

• PQV value between 0 and 1, the higher the PQV, the greater the impact on the

GQ value.


Documents

Section 1 - Gene Mapper ID