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. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  2. Nguyen, Ken; Guo, Xuan; Pan, Yi: Multiple biological sequence alignment. Scoring functions, algorithms and evaluation (2016)
  3. Mora-Gutiérrez, Roman Anselmo; Lárraga-Ramírez, María E.; Rincón-García, Eric A.; Ponsich, Antonin; Ramírez-Rodríguez, Javier: Adaptation of the method of musical composition for solving the multiple sequence alignment problem (2015)
  4. Federico, Maria; Peterlongo, Pierre; Pisanti, Nadia; Sagot, Marie-France: Rime: repeat identification (2014)
  5. Lee, Suk-Hwan: DWT based coding DNA watermarking for DNA copyright protection (2014)
  6. Daskalakis, Constantinos; Roch, Sebastien: Alignment-free phylogenetic reconstruction: Sample complexity via a branching process analysis (2013)
  7. Daugelaite, Jurate; O’Driscoll, Aisling; Sleator, Roy D.: An overview of multiple sequence alignments and cloud computing in bioinformatics (2013)
  8. Andoni, Alexandr; Daskalakis, Constantinos; Hassidim, Avinatan; Roch, Sebastien: Global alignment of molecular sequences via ancestral state reconstruction (2012)
  9. Bose, R.P.Jagadeesh Chandra; Aalst, Wil M.P.van der: Process diagnostics using trace alignment: opportunities, issues, and challenges (2012)
  10. Petitjean, François; Gançarski, Pierre: Summarizing a set of time series by averaging: from Steiner sequence to compact multiple alignment (2012)
  11. Saeed, Fahad; Perez-Rathke, Alan; Gwarnicki, Jaroslaw; Berger-Wolf, Tanya; Khokhar, Ashfaq: A high performance multiple sequence alignment system for pyrosequencing reads from multiple reference genomes (2012)
  12. Desai, Dhwani K.; Nandi, Soumyadeep; Srivastava, Prashant K.; Lynn, Andrew M.: ModEnzA: accurate identification of metabolic enzymes using function specific profile HMMs with optimised discrimination threshold and modified emission probabilities (2011)
  13. Petitjean, François; Ketterlin, Alain; Gançarski, Pierre: A global averaging method for dynamic time warping, with applications to clustering (2011)
  14. Lennox, Kristin P.; Dahl, David B.; Vannucci, Marina; Day, Ryan; Tsai, Jerry W.: A Dirichlet process mixture of hidden Markov models for protein structure prediction (2010)
  15. Arenas-Díaz, Edgar D.; Ochoterena, Helga; Rodríguez-Vázquez, Katya: Multiple sequence alignment using a genetic algorithm and GLOCSA (2009)
  16. Guyon, Frédéric; Brochier-Armanet, Céline; Guénoche, Alain: Comparison of alignment free string distances for complete genome phylogeny (2009)
  17. Iliopoulos, Costas S.; Rahman, M.Sohel: Indexing factors with gaps (2009)
  18. Althaus, Ernst; Canzar, Stefan: A Lagrangian relaxation approach for the multiple sequence alignment problem (2008)
  19. Gronau, Ilan; Moran, Shlomo: Optimal implementations of UPGMA and other common clustering algorithms (2007)
  20. Du, Zhihua; Lin, Feng: Pattern-constrained multiple polypeptide sequence alignment (2005)

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