Prider: multiplexed primer design using linearly scaling approximation of set coverage.

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Designing oligonucleotide primers and probes is crucial for experiments like multiplexed PCR and digital multiplexed ligation assays.
• Optimizing primer and probe design for complex DNA datasets to achieve maximal diversity coverage with minimal oligonucleotides presents a significant challenge.
• Current tools lack efficiency in handling large datasets for this optimization problem.

Purpose of the Study:

• To develop an efficient R package, Prider, for designing oligonucleotide primers and probes.
• To address the optimization problem of identifying the minimal set of oligonucleotides for maximal coverage of diverse DNA sequences.
• To provide a scalable solution for primer and probe design in large-scale sequencing projects.

Main Methods:

• Prider employs an approach to initially cover all input sequences with primers.
• It then reduces complexity by removing redundant or narrowly covering primers.
• The package offers functions to access designed primers as data frames and visualize coverage using heatmaps.

Main Results:

• Prider provides nearly optimal primer and probe coverage for complex and large sequence sets.
• The R package scales linearly with increasing sequence data, ensuring efficient processing regardless of dataset diversity.
• Designed primers are accessible and their coverage is visualized through heatmaps.

Conclusions:

• Prider effectively solves the challenge of maximizing sequence diversity coverage with a minimal set of oligonucleotide primers or probes.
• This tool has immediate applicability in molecular diagnostics, clinical microbiology, and surveillance of epidemics and antimicrobial resistance.
• Integrating Prider with scalable molecular quantification techniques enhances molecular screening capabilities.