openPrimeR

openPrimeR performs systematic design, evaluation, and optimization of multiplex PCR primer sets to maximize template coverage for highly diverse or somatically mutated targets such as immune receptor repertoires and pathogen genomes.

Key Features:

• Constrained set cover formulation: Primer selection is formalized as a constrained set cover problem to model coverage optimization across template collections.
• Optimization algorithms: Implements a greedy algorithm and an integer linear programming (ILP) formulation to compute minimal primer sets that maximize template coverage.
• Physicochemical constraint enforcement: Enforces constraints on melting temperature, GC content, self-complementarity, cross-dimerization, and amplicon characteristics to ensure experimental viability.
• Evaluation of existing primers: Provides computational tools to evaluate and compare existing primer sets against large template collections.
• Coverage gap analysis: Identifies coverage gaps across extensive template collections to guide primer design and optimization.
• Targeted for diverse/mutated sequences: Designed specifically to handle highly diverse or highly mutated targets such as immune receptor repertoires and pathogen genomes.

Scientific Applications:

• Immune receptor repertoire amplification: Design and optimization of multiplex PCR primer sets for amplifying immune receptor repertoires, including somatically mutated immunoglobulin variable gene segments.
• Pathogen genome targeting: Design of primer panels for pathogen genome regions requiring robust coverage across diverse or variable sequences.
• Antibody sequencing recovery: Applied to generate primer panels (oPR-IGHV, oPR-IGKV, oPR-IGLV) that improved recovery of antibody sequences from somatically mutated human immunoglobulin variable gene segments.
• General multiplex PCR applications: Applicable to any multiplex PCR use case requiring coverage-aware, constraint-compliant primer design.

Methodology:

Primer selection is formalized as a constrained set cover problem and solved using a greedy algorithm and an integer linear programming (ILP) formulation while enforcing constraints on melting temperature, GC content, self-complementarity, cross-dimerization, and amplicon characteristics; the package also provides methods to evaluate and compare primer sets and to identify coverage gaps.