SSR_VibraProfiler: a Python package for accurate classification of varieties using SSRs with intra-variety specificity and inter-variety polymorphism.

Background: Simple sequence repeats (SSRs) are widely used as molecular markers; however, traditional development of SSR molecular markers heavily relies on experimental methods. The advancement of modern sequencing technology has provided the possibility of directly extracting SSR characteristics from sequencing data and using them for variety identification.

Results: We have developed a computational framework for variety identification, treating the presence or absence of each SSR in sequencing data as a numerical characteristic while ignoring specific loci, flanking sequences, and occurrence counts. Therefore, subsequent variety identification does not rely on experimental validation but is directly performed based on the numerical characteristic matrix. Using a formula, we measure the variance of these numerical characteristics both within and among varieties, and select SSRs that exhibit intra-variety specificity and inter-variety polymorphism, forming a 0,1 matrix. We use t-SNE (t-distributed Stochastic Neighbor Embedding) to project the matrix onto a two-dimensional plane, followed by K-means clustering of the individuals. The classification performance of the matrix is preliminarily assessed by comparing the cluster labels with the true labels, providing an initial evaluation of its effectiveness in variety detection. Ultimately, we construct a recognition model based on the SSRs matrix and apply it for variety identification. The process has been encapsulated into the package SSR_VibraProfiler, which can serve as a tool for constructing an SSR variety DNA fingerprint database. We tested this package on a Rhododendron dataset that included 40 individuals from 8 varieties. The accuracy achieved through t-SNE dimensionality reduction and K-means clustering was 100%. Furthermore, we used the leave-one-out method to validate the accuracy of our method in predicting variety, and confirmed the reliability of our method in detecting varieties. The package is freely available at https://github.com/Olcat35412/SSR_VibraProfiler .

Conclusion: We introduced SSR_VibraProfiler, a Python package for distinguishing and predicting individual varieties without a reference genome by extracting SSR numerical characteristics from next-generation sequencing data. This tool will contribute to the development, identification, and protection of new varieties.