| Title: |
Erimin: A Pipeline to Identify Bacterial Strain Specific Primers |
| Authors: |
Margaritis Tsifintaris; Paraskevi Koutra; Pavlos Tsiartas; Panagiotis Repanas; Sotirios Touliopoulos; Grigorios Nelios; Anastasia Anastasiadou; Georgia Tamouridou; Anastasios Nikolaou; Ilias Tsochantaridis |
| Source: |
DNA ; Volume 6 ; Issue 1 ; Pages: 11 |
| Publisher Information: |
Multidisciplinary Digital Publishing Institute |
| Publication Year: |
2026 |
| Collection: |
MDPI Open Access Publishing |
| Subject Terms: |
strain-specific primers; comparative genomics; PCR assay development; Whole-Genome Sequencing (WGS); bioinformatics pipeline |
| Description: |
Background/Objectives: Strain-level detection of bacteria is essential for applications such as diagnostics, food safety, and microbial monitoring. While 16S rRNA gene sequencing provides genus- or species-level resolution, it cannot reliably discriminate closely related strains. Whole-genome sequencing (WGS) offers high-resolution strain differentiation but remains impractical for routine detection due to cost and analytical complexity. This study aims to enable the translation of WGS data into accurate and cost-effective strain-specific PCR assays. Methods: We developed Erimin, a modular, shell-based bioinformatics pipeline for the automated identification of strain-specific genomic regions from short-read WGS data. Erimin systematically analyzes all available reference genomes for a given bacterial species in combination with sequencing data from a target strain. The workflow integrates reference-based read alignment, extraction of unmapped reads, de novo assembly, contig filtering and validation, genome annotation, and in silico PCR primer design and specificity evaluation. Results: Erimin was applied to Lactiplantibacillus pentosus whole-genome sequencing data to identify genomic regions specific to strain L33 through comparative analysis against a comprehensive set of reference genome assemblies representing multiple Lactiplantibacillus species. These regions were used for in silico PCR primer design and computational specificity assessment against non-target bacterial genomes, supporting discrimination of closely related strains. Conclusions: Erimin provides a structured computational approach for identifying strain-specific genomic regions from WGS data and for supporting the in silico design of PCR primers. This framework facilitates strain-level discrimination using targeted molecular assays. |
| Document Type: |
text |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://dx.doi.org/10.3390/dna6010011 |
| DOI: |
10.3390/dna6010011 |
| Availability: |
https://doi.org/10.3390/dna6010011 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.5E1A387C |
| Database: |
BASE |