How to Analyze Microsatellites in Geneious Prime: Complete Beginner's Guide

Microsatellites (also called Simple Sequence Repeats or STRs) are short, repetitive DNA sequences widely used for genetic fingerprinting, population studies, and forensic analysis. The Geneious Microsatellite Plugin automates the analysis of microsatellite traces from ABI fragment analysis machines, allowing researchers to call ladders, identify peaks, predict allele bins, and generate exportable reports. This guide walks you through your first microsatellite analysis project, from importing .fsa or .ab1 trace files to exporting allele call tables ready for downstream analysis.

In Short:

• The Microsatellite Plugin analyzes ABI fragment analysis traces to identify and bin microsatellite alleles automatically
• The workflow takes 15-30 minutes and includes importing traces, verifying ladders, defining loci, and binning peaks
• Results export as .csv tables showing binned allele sizes for each sample, suitable for downstream analyses

What Do You Need Before Starting?

Required:

• Geneious Prime (version R7 or later)
• Microsatellite Plugin installed
• .fsa or .abi trace files from the ABI fragment analyzer

Recommended:

• Known repeat unit information for your loci
• Expected PCR product size ranges
• Reference allele sizes (if available)

How Do You Install the Microsatellite Plugin?

1. Open Geneious Prime
2. Go to Tools → Plugins
3. Search for "Microsatellite Plugin"
4. Click Install

Alternatively, download the .gplugin file from the Geneious plugins page and drag it directly into Geneious.

How Do You Import and View Microsatellite Traces?

Step 1: Import Your .fsa Files

Option A: Drag and drop .fsa files directly into Geneious Option B: Use File → Import → From Multiple Files

Your traces will appear in the document table. Select any trace to view it in the viewer window.

Step 2: Explore the Trace Viewer

The viewer controls (right side panel) let you:

• Zoom in/out on specific regions
• Toggle dye channels on and off
• Show/hide peak labels, calls, and bins
• Adjust peak detection thresholds

Tip: Start with all dye channels visible to get an overview, then toggle individual channels when examining specific loci.

How Do You Verify Ladder Calls?

Ladder calling is the foundation of accurate microsatellite analysis. Geneious automatically detects ladders based on peak spacing in the last dye channel.

Critical requirement: The ladder must be in the last dye of your trace. If you ran 4 dyes, your trace should show exactly 4 dyes with the ladder in dye 4. If your traces are configured for 5 dyes but you only used 4, the last dye will be empty and Geneious cannot call the ladder.
 

What to Check:

Tip: multiple peaks can be selected and deleted at once (even across multiple sequences) by clicking and dragging the selection box.

How Do You Manually Edit Ladder Peaks?

If Geneious doesn't automatically recognize your ladder, follow these steps:

Step 1: Isolate the Ladder Dye

• Disable all trace dyes except the ladder (last dye)
• Enable: "Show Traces", "Show Peak Calls", and "Show Peak Labels"

Step 2: Optimize Visualization

• Increase Y scaling to maximum so peaks are clearly visible
• Increase X scaling to widen peaks for easier editing

Step 3: Add Missing Peaks

• Click where a peak should be 
  • (multiple selections can be made by holding the ctrl/cmd key)
• Click "Add Peak" button in the toolbar 
• Repeat for all missing peaks

Step 4: Remove Extra Peaks

• Select the incorrect peak
• Click "Remove Peak" button in the toolbar
• Remove stutter peaks (the shorter of each stutter pair)

Step 5: Verify Recognition After editing erroneous peaks, Geneious will automatically identify the ladder. You'll see the ladder name appear under the dye name in the right panel.

Using custom ladders: If you're using a non-standard microsatellite ladder, you may need to add it to Geneious's ladder library before it can be recognized. See how to add custom ladders to Geneious for detailed instructions.

Why this matters: An incorrectly called ladder will shift all your allele sizes, creating systematic errors across your entire dataset. Always verify ladder calls before proceeding to binning.

How Do You Define Loci for Your Analysis?

Step 1: Select All Traces and Click "Locus Info"

This opens the locus configuration dialog.

Step 2: Enter Information for Each Dye/Locus

For each locus, provide:

For diploid organisms: Set expected peaks to 2 For polyploid organisms: Set to match the number of homologous chromosomes (e.g., 4 for tetraploid)

Step 3: Save Your Locus Document

Click OK, then Save and name your locus document (e.g., "Cattle_Microsatellites_Panel_2024").

Your locus document will appear in the document table and automatically apply to all selected traces.

Tip: Save locus documents as templates for future projects with the same marker panel. To apply an existing locus document to new traces, select traces and choose the document from the "Loci" dropdown.

How Do You Check and Clean Peak Calls?

Before binning, manually review peak calls to remove artifacts.

Common Issues to Look For:

Stutter peaks: Small peaks one repeat unit shorter than true alleles (common in dinucleotide repeats)

• How to identify: Typically 10-20% the height of the main peak
• What to do: Remove manually or adjust stutter filter settings

Artifacts and noise: Non-specific peaks outside your expected size range

• What to do: Delete peaks by selecting them and pressing Delete

Split peaks: True alleles showing as multiple close peaks

• What to do: Merge peaks or adjust peak detection threshold

Why this matters: Incorrect peak calls will create false alleles in your final dataset. Manual review at this stage prevents hours of downstream troubleshooting.

How Do You Predict and Assign Allele Bins?

Binning groups peaks into allele categories based on your repeat unit, accounting for small variations in fragment sizing between samples.

Step 1: Click "Predict Bins"

Select each dye channel you want to bin.

Step 2: (Optional) Specify an Example Allele

If you have a known reference allele, you can anchor the bins to it. This ensures your bin labels match established nomenclature.

Example: If you know sample "Ref_001" has a 185 bp allele, designate it as the example allele to anchor bin "185" precisely.

What Binning Does:

Binning collapses slight sizing variations into consistent allele calls, which is essential for accurate genotyping and population analyses.

How Do You Set Your Sizing Method?

The sizing method converts x-coordinates (migration time) to base pairs.

Recommended algorithms:

• 3rd Order Least Squares (most common, good for standard runs)
• Local Southern (better for traces with drift or noise)

Select your sizing method from the dropdown in the trace viewer before proceeding.

Why this matters: Different sizing methods can shift allele calls by 0.5-2 bp. Using a consistent method across all samples ensures comparable results.

How Do You View and Export Your Results?

Step 1: Open the Alleles Table

Click Alleles Table to view all allele calls in tabular format.

What the Table Shows:

• Binned allele sizes for each locus and sample
• Warnings for missing data:
  • "No peaks" = No PCR product amplified
  • "Unbinned peak" = Peak outside bin boundaries

Step 2: Troubleshoot Warnings

"No peaks" warnings:

• Usually indicate PCR failure or insufficient DNA
• Review trace quality and consider re-running the sample

"Unbinned peak" warnings:

• Peak is just outside the bin boundary
• How to fix:
  1. Select the affected trace
  2. Turn on only the relevant dye
  3. Select the bin and choose Edit Bin
  4. Extend the range or drag the bin to include the peak

Peaks far outside bins:

• May indicate null alleles, contamination, or incorrect PCR products
• Review the trace carefully before deciding to include or exclude

Step 3: Export Your Data

When satisfied with your allele calls:

1. Click Export Table
2. Save as .csv format
3. Ready for downstream analysis (e.g. population genetics software)

Tip: Include sample metadata (population, location, collection date) in a separate column before exporting to streamline downstream analysis.