TarBio - Genetic Purity Analysis Software

Desktop application for calculating genetic similarity and purity of laboratory samples against genotype × marker databases. Features heatmap visualization and Excel/JSON export.

GENOTYPE ID & PURITY SOFTWARE

Summary

Genotype ID & Purity Software (TarBio Genetic Purity Software) is a desktop application solution developed to calculate the genetic similarity and purity of laboratory samples against genotype × marker-based databases. The software enables comparison of samples with reference genotypes, detection of inter-genotype similarity groups, and generation of pairwise similarity matrices (heatmaps). Results are presented both numerically and visually (graphs/heatmaps); they can also be exported to Excel/JSON.

Keywords: genotype, marker, purity analysis, similarity analysis, fingerprint, heatmap

1. Introduction

Verification of variety and determination of genetic purity in plant/organism material is critically important in seed production, breeding studies, and quality control. This software automates the comparison of marker data obtained in the laboratory (e.g., SSR/STR, etc.) with pre-defined reference genotypes, producing fast and reproducible results.

2. Software Objectives and Requirements

Creating and editing genotype × marker databases
Ability to store as many markers and genotypes as needed (scalable architecture)
Entering new sample data and automatic comparison of each sample with all reference genotypes
Calculating match percentage; also grouping of fully matching genotypes
Generating pairwise similarity matrices and creating heatmaps
Importing/exporting data in Excel/JSON format
User-friendly interface: genotype names, marker names, marker values should be easily editable

3. Methodology (Algorithms)

3.1. A — Variety Purity Analysis

Input: a sample and reference genotypes (Genotype list) in the database
Method: Non-null (present) markers in the sample are selected. For each reference genotype, comparison is made with marker values at the same indices. Matching marker count is calculated. Match percentage = (matching_markers / total_compared_markers) × 100. Results are sorted from highest to lowest match percentage.
Note: If there are no valid markers in the sample to compare, analysis cannot be performed.

3.2. B — Inter-Variety Similarity Analysis

Input: list of all genotypes
Method (Fingerprint Technique): Marker values of each genotype are converted to a string (null values are represented with 'N'). Genotypes with exactly the same string are grouped — this provides groups with 100% similarity. From the resulting groups, the largest similar group and other similar groups are separated from unique (singleton) genotypes.
Additionally, a matrix can be generated by calculating similarity percentage over common filled markers between each pair of genotypes through pairwise comparison (for heatmap).

3.3. Pairwise Similarity Matrix

For each genotype pair, only markers that are filled in both genotypes are compared. The ratio of matching markers to the number of commonly comparable markers is calculated and converted to percentage. If there are no common filled markers, similarity is assumed to be 0. Output can be used as a pairwise similarity list or NxN matrix.

4. Interface and Workflow (Usage Scenario)

Opening screen: Software name, brief description, data source loading (Excel/JSON), analysis type selection
Database management: Number of genotypes, genotype names, number of markers, marker names, and marker values for each genotype can be edited. Marker and genotype addition/removal should be dynamic.
Sample management: Multiple samples can be added, each adjusted according to the number of markers in the database.
Analysis selection: "Variety Purity" or "Variety Similarity"
Results:
Purity: Match percentages sorted by reference genotypes for each sample
Similarity: Exactly matching groups (fingerprint groups), largest group, unique genotypes
Heatmap: Visual created using pairwise similarity
Export: Excel (appropriate table structure) and JSON. PDF report support is optional.

5. Validation and Test Protocol

Unit tests:
PurityAnalysis: sample with some null markers vs. fully filled references. Comparison with expected percentages.
SimilarityAnalysis: exactly matching and partially matching genotype sets
Integration tests: Testing the flow of data loading via UI -> running analysis -> export
Real data validation: Results of samples from the laboratory should be compared with known references.

6. Results and Discussion

The software accelerates routine analyses such as genotype verification and purity assessment while reducing human error. Analyzing only common filled markers for samples with partial data provides reliable metrics even in missing data situations. The fingerprint method is a simple and powerful approach for exact match detection; however, additional statistics (e.g., specific weights, similarity score thresholds) may be needed for interpreting partial similarities.

7. Future Improvements

More advanced scoring for partial matches (e.g., Jaccard similarity, similarity weighting)
Statistical confidence intervals and error tolerance reports
Multiple sample batch analyses and clustering (hierarchical/k-means) integration
Report automation and laboratory information management systems (LIMS) integration

8. Application User Guide (Brief)

Create database: Determine the number of genotypes and markers; enter genotype names and marker names; add marker values for each genotype.
Add samples: Add as many rows as the number of samples and enter marker values.
Select analysis: "Purity" or "Similarity". When "Purity" is selected, comparison is made with references for each sample. When "Similarity" is selected, genotypes are separated into exactly matching groups and/or pairwise similarity is calculated.
Review results, view in graph and table format, export to Excel/JSON as needed.