BWA

Burrows-Wheeler Aligner: BWA is a software package for mapping low-divergent 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 1Mbp. BWA-MEM and BWA-SW share similar features such as long-read support and split alignment, but BWA-MEM, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate. BWA-MEM also has better performance than BWA-backtrack for 70-100bp Illumina reads


References in zbMATH (referenced in 45 articles )

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  1. Bannai, Hideo; Gagie, Travis; I, Tomohiro: Refining the (r)-index (2020)
  2. Giancarlo, Raffaele; Manzini, Giovanni; Restivo, Antonio; Rosone, Giovanna; Sciortino, Marinella: The alternating BWT: an algorithmic perspective (2020)
  3. Marchet, Camille; Lecompte, Lolita; Limasset, Antoine; Bittner, Lucie; Peterlongo, Pierre: A resource-frugal probabilistic dictionary and applications in bioinformatics (2020)
  4. Mäkinen, Veli; Norri, Tuukka: Applying the positional Burrows-Wheeler transform to all-pairs Hamming distance (2019)
  5. Zhou, Tianjian; Sengupta, Subhajit; Müller, Peter; Ji, Yuan: TreeClone: reconstruction of tumor subclone phylogeny based on mutation pairs using next generation sequencing data (2019)
  6. Chen, Yangjun; Wu, Yujia: On the string matching with (k) mismatches (2018)
  7. Daykin, Jacqueline W.; Groult, Richard; Guesnet, Yannick; Lecroq, Thierry; Lefebvre, Arnaud; Léonard, Martine; Prieur-Gaston, Élise: A survey of string orderings and their application to the Burrows-Wheeler transform (2018)
  8. Na, Joong Chae; Kim, Hyunjoon; Min, Seunghwan; Park, Heejin; Lecroq, Thierry; Léonard, Martine; Mouchard, Laurent; Park, Kunsoo: FM-index of alignment with gaps (2018)
  9. Rabadan, Raul; Bhanot, Gyan; Marsilio, Sonia; Chiorazzi, Nicholas; Pasqualucci, Laura; Khiabanian, Hossein: On statistical modeling of sequencing noise in high depth data to assess tumor evolution (2018)
  10. Teixeira, Andreia Sofia; Fernandes, Francisco; Francisco, Alexandre P.: SpliceTAPyR -- an efficient method for transcriptome alignment (2018)
  11. Benoit, Gaetan; Lemaitre, Claire; Rizk, Guillaume; Drezen, Erwan; Lavenier, Dominique: De Novo NGS data compression (2017)
  12. Daykin, Jacqueline W.; Watson, Bruce: Indeterminate string factorizations and degenerate text transformations (2017)
  13. Filion, Guillaume J.: Analytic combinatorics for computing seeding probabilities (2017)
  14. Fiscon, Giulia; Weitschek, Emanuel: String-matching and alignment algorithms for finding motifs in NGS data (2017)
  15. Gagie, Travis; Manzini, Giovanni; Sirén, Jouni: Wheeler graphs: a framework for BWT-based data structures (2017)
  16. Liu, Yongchao; Schmidt, Bertil: CUSHAW suite: parallel and efficient algorithms for NGS read alignment (2017)
  17. Schmidt, Markus; Kutzner, Arne; Heese, Klaus: A novel specialized single-linkage clustering algorithm for taxonomically ordered data (2017)
  18. Shen, Carol; Shen, Tony; Lin, Jimmy: Comparative assessment of alignment algorithms for NGS data: features, considerations, implementations, and future (2017)
  19. Shpak, Max; Ni, Yang; Lu, Jie; Müller, Peter: Variance in estimated pairwise genetic distance under high versus low coverage sequencing: the contribution of linkage disequilibrium (2017)
  20. Theodoridis, Evangelos: Cloud storage-management techniques for NGS data (2017)

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