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  • GitHub - lh3 bwa: Burrow-Wheeler Aligner for short-read alignment (see . . .
    BWA is a software package for mapping DNA sequences against a large reference genome, such as the human genome It consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM The first algorithm is designed for Illumina sequence reads up to 100bp, while the rest two for longer sequences ranged from 70bp to a few megabases
  • BWA - Mem - GitHub
    BWA is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome BWA-MEM, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate than other BWA alignment algorithms
  • bwa README. md at master · lh3 bwa - GitHub
    Burrow-Wheeler Aligner for short-read alignment (see minimap2 for long-read alignment) - lh3 bwa
  • bwa-mem2 bwa-mem2: The next version of bwa-mem - GitHub
    The tool bwa-mem2 is the next version of the bwa-mem algorithm in bwa It produces alignment identical to bwa and is ~1 3-3 1x faster depending on the use-case, dataset and the running machine The original bwa was developed by Heng Li (@lh3)
  • Releases · lh3 bwa - GitHub
    BWA produces alignments identical to v0 7 17 and v0 7 18 (0 7 19: 22 March 2025, r1273)
  • GitHub - brentp bwa-meth: fast and accurate alignment of BS-Seq reads . . .
    fast and accurate alignment of BS-Seq reads using bwa-mem and a 3-letter genome - brentp bwa-meth
  • etri bwa-mem-scale - GitHub
    BWA-MEM-SCALE builds upon BWA-MEM2 and BWA-Mich, and includes performance improvements to entire steps of genome sequence alignments It adds Exact Match Filter (EMF), FM-index Accelerator (FMA), and various optimization techniques BWA-MEM-SCALE gives up to 3 32X speedup compared to BWA-MEM2 when the available memory capacity is over 133GB
  • kaist-ina BWA-MEME: BWA-MEME: Faster BWA-MEM2 using learned-index - GitHub
    If you use BWA-MEME, please cite the following paper Youngmok Jung, Dongsu Han, BWA-MEME: BWA-MEM emulated with a machine learning approach, Bioinformatics, Volume 38, Issue 9, 1 May 2022, Pages 2404–2413, https: doi org 10 1093 bioinformatics btac137


















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