ArticleBioinformatics (Oxford, England)2024
HAlign 4: a new strategy for rapidly aligning millions of sequences.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- deMEM: a novel divide-and-conquer framework based on de Bruijn graph for scalable multiple sequence alignment.GigaScience · 2026Article
- Exploring the protein universe with distant similarity detection methods.Protein science : a publication of the Protein Society · 2026Review
- TempSnap-Trace: A temporal snapshot-based framework for haplotype network tracing.Biosafety and health · 2025Article
- HAlign-G: rapid and low-memory multiple-genome aligner for large-scale closely related genomes.Genome biology · 2025Article
- ReAlign-P: a vertical iterative realignment method for protein multiple sequence alignment.Bioinformatics (Oxford, England) · 2025Article
- Fast sequence alignment for centromeres with RaMA.Genome research · 2025Article
- Advances in post-processing methods for multiple sequence alignment.Frontiers in genetics · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationHAlign is a high-performance multiple sequence alignment software based on the star alignment strategy, which is the preferred choice for rapidly aligning large numbers of sequences. HAlign3, implemented in Java, is the latest version capable of aligning an ultra-large number of similar DNA/RNA sequences. However, HAlign3 still struggles with long sequences and extremely large numbers of sequences.
resultsTo address this issue, we have implemented HAlign4 in C++. In this version, we replaced the original suffix tree with Burrows-Wheeler Transform and introduced the wavefront alignment algorithm to further optimize both time and memory efficiency. Experiments show that HAlign4 significantly outperforms HAlign3 in runtime and memory usage in both single-threaded and multi-threaded configurations, while maintains high alignment accuracy comparable to MAFFT. HAlign4 can complete the alignment of 10 million coronavirus disease 2019 (COVID-19) sequences in about 12 min and 300 GB of memory using 96 threads, demonstrating its efficiency and practicality for large-scale alignment on standard workstations. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/malabz/HAlign-4, dataset is available at https://zenodo.org/records/13934503.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.