Minipoa: A minimizer-based method for fast and memory-efficient partial order alignment

Partial order alignment (POA) has emerged as a fundamental component in long-read error correction, assembly and pangenomics. However, conventional POA algorithms are limited by high time and memory requirements, making them inefficient for large-scale datasets. Here, we present minipoa, a fast and memory-efficient POA tool that incorporates seed-chain-align heuristics, adaptive or static banding strategies, and single-instruction multiple-data optimizations. Minipoa achieves up to a 5-fold speedup over abPOA, reduces memory usage by up to 16-fold, and improves correction accuracy, while maintaining strong performance on both PacBio and ONT simulated datasets, and can be readily integrated into existing long-read error correction and assembly workflows. In multiple sequence alignment datasets, minipoa demonstrates superior computational efficiency and alignment accuracy compared with all other tested tools, achieving Total Column scores up to 2.5-fold higher than MAFFT in low-similarity scenarios. Moreover, minipoa enables multiple sequence alignment of megabase-long genomes and million-sequence datasets, demonstrated by 342 Mycobacterium tuberculosis sequences and one million SARS-CoV-2 sequences respectively. Collectively, minipoa is well positioned to become a cornerstone in the era of large-scale pangenomics.