Muscle: multiple sequence alignment with high accuracy and high throughput. We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log‐expectation score, and refinement using tree‐dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T‐Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T‐Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.

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  1. Berkemer, Sarah J.; Höner zu Siederdissen, Christian; Stadler, Peter F.: Compositional properties of alignments (2021)
  2. Hosseininasab, Amin; van Hoeve, Willem-Jan: Exact multiple sequence alignment by synchronized decision diagrams (2021)
  3. Heaps, Sarah E.; Nye, Tom M. W.; Boys, Richard J.; Williams, Tom A.; Cherlin, Svetlana; Embley, T. Martin: Generalizing rate heterogeneity across sites in statistical phylogenetics (2020)
  4. Durden, Chris; Sullivant, Seth: Identifiability of phylogenetic parameters from (k)-mer data under the coalescent (2019)
  5. Huang, Hsin-Hsiung; Girimurugan, Senthil Balaji: Discrete wavelet packet transform based discriminant analysis for whole genome sequences (2019)
  6. Jain, Sahil; Baranwal, Manoj: Computational analysis in designing T cell epitopes enriched peptides of Ebola glycoprotein exhibiting strong binding interaction with HLA molecules (2019)
  7. Zamudio, Gabriel S.; Prosdocimi, Francisco; Torres de Farias, Sávio; José, Marco V.: A neutral evolution test derived from a theoretical amino acid substitution model (2019)
  8. Amiri, Saeid; Clarke, Bertrand S.; Clarke, Jennifer L.: Clustering categorical data via ensembling dissimilarity matrices (2018)
  9. Chen, Weiyang; Liao, Bo; Li, Weiwei: Use of image texture analysis to find DNA sequence similarities (2018)
  10. Liu, Yong; Chen, Jingan; Qian, Jieying; Lin, Hao; Sun, Ning; Huang, Zunnan: Evolutionary analysis and structural characterization of \textitAquilariasinensis sesquiterpene synthase in agarwood formation: a computational study (2018)
  11. Arribas-Gil, Ana; Matias, Catherine: A time warping approach to multiple sequence alignment (2017)
  12. Davidson, Ruth; Rusinko, Joseph; Vernon, Zoe; Xi, Jing: Modeling the distribution of distance data in Euclidean space (2017)
  13. DeBlasio, Dan; Kececioglu, John: Parameter advising for multiple sequence alignment (2017)
  14. Drellich, Elizabeth; Gainer-Dewar, Andrew; Harrington, Heather A.; He, Qijun; Heitsch, Christine; Poznanović, Svetlana: Geometric combinatorics and computational molecular biology: branching polytopes for RNA sequences (2017)
  15. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  16. Mier, Pablo; Alanis-Lobato, Gregorio; Andrade-Navarro, Miguel A.: Protein-protein interactions can be predicted using coiled coil co-evolution patterns (2017)
  17. Polishko, Anton; Hasan, Md. Abid; Pan, Weihua; Bunnik, Evelien M.; Le Roch, Karine; Lonardi, Stefano: ThIEF: finding genome-wide trajectories of epigenetics marks (2017)
  18. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  19. Nguyen, Ken; Guo, Xuan; Pan, Yi: Multiple biological sequence alignment. Scoring functions, algorithms and evaluation (2016)
  20. Pesch, Robert: Cross-species network and transcript transfer (2016)

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