Biology subjects

Moure, J. C.

Publications and source records attributed to Moure, J. C..

2 recordsLinked to original sources

bioRxiv2026

Theseus: Fast and Optimal Affine-Gap Sequence-to-Graph Alignment

MotivationSequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. ResultsThis work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus exploits the diagonal transition property to process only a subset of the dynamic programming cells, combined with a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: multiple sequence alignment (MSA) and pangenome read mapping. For MSA, we compare it against the state-of-the-art methods SPOA, abPOA, and POASTA. Theseus is 2.0x to 232.2x faster than the other two optimal aligners, SPOA and POASTA. Compared with abPOA, a heuristic aligner, Theseus is 3.3x faster on average, while ensuring optimality. For pangenome read mapping, we benchmark Theseus against the alignment stage of the popular mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9x to 16.9x speed improvement on short reads. AvailabilityTheseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib. Contactalbert.jimenez.blanco@upc.es

bioinformatics↗

bioRxiv2022

WFA-GPU: Gap-affine pairwise alignment using GPUs

MotivationAdvances in genomics and sequencing technologies demand faster and more scalable analysis methods that can process longer sequences with higher accuracy. However, classical pairwise alignment methods, based on dynamic programming (DP), impose impractical computational requirements to align long and noisy sequences like those produced by PacBio, and Nanopore technologies. The recently proposed WFA algorithm paves the way for more efficient alignment tools, improving time and memory complexity over previous methods. However, high-performance computing (HPC) platforms require efficient parallel algorithms and tools to exploit the computing resources available on modern accelerator-based architectures. ResultsThis paper presents the WFA-GPU, a GPU (Graphics Processing Unit)-accelerated tool to compute exact gap-affine alignments based on the WFA algorithm. We present the algorithmic adaptations and performance optimizations that allow exploiting the massively parallel capabilities of modern GPU devices to accelerate the alignment computations. In particular, we propose a CPU-GPU co-design capable of performing inter-sequence and intra-sequence parallel sequence alignment, combining a succinct WFA-data representation with an efficient GPU implementation. As a result, we demonstrate that our implementation outperforms the original multi-threaded WFA implementation between 1.5-7.7x and up to 17x when using heuristic methods on long and noisy sequences. Compared to other state-of-the-art tools and libraries, the WFA-GPU is up to 29x faster than other GPU implementations and up to four orders of magnitude faster than other CPU implementations. AvailabilityWFA-GPU code and documentation are publicly available at https://github.com/quim0/WFA-GPU. Contactquim.aguado@uab.cat

bioinformatics↗