MUSCLE

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.


References in zbMATH (referenced in 31 articles )

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  1. Arribas-Gil, Ana; Matias, Catherine: A time warping approach to multiple sequence alignment (2017)
  2. Davidson, Ruth; Rusinko, Joseph; Vernon, Zoe; Xi, Jing: Modeling the distribution of distance data in Euclidean space (2017)
  3. 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)
  4. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  5. Mier, Pablo; Alanis-Lobato, Gregorio; Andrade-Navarro, Miguel A.: Protein-protein interactions can be predicted using coiled coil co-evolution patterns (2017)
  6. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  7. Nguyen, Ken; Guo, Xuan; Pan, Yi: Multiple biological sequence alignment. Scoring functions, algorithms and evaluation (2016)
  8. Pesch, Robert: Cross-species network and transcript transfer (2016)
  9. Axelson-Fisk, Marina: Comparative gene finding. Models, algorithms and implementation (2015)
  10. 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)
  11. Federico, Maria; Peterlongo, Pierre; Pisanti, Nadia; Sagot, Marie-France: Rime: repeat identification (2014)
  12. Lee, Suk-Hwan: DWT based coding DNA watermarking for DNA copyright protection (2014) ioport
  13. Daskalakis, Constantinos; Roch, Sebastien: Alignment-free phylogenetic reconstruction: Sample complexity via a branching process analysis (2013)
  14. Daugelaite, Jurate; O’Driscoll, Aisling; Sleator, Roy D.: An overview of multiple sequence alignments and cloud computing in bioinformatics (2013)
  15. Palacios, Julia A.; Minin, Vladimir N.: Gaussian process-based Bayesian nonparametric inference of population size trajectories from gene genealogies (2013)
  16. Andoni, Alexandr; Daskalakis, Constantinos; Hassidim, Avinatan; Roch, Sebastien: Global alignment of molecular sequences via ancestral state reconstruction (2012)
  17. Bose, R.P.Jagadeesh Chandra; Aalst, Wil M.P.van der: Process diagnostics using trace alignment: opportunities, issues, and challenges (2012) ioport
  18. Gong, Yu-Nong; Chen, Guang-Wu; Suchard, Marc A.: A novel empirical mutual information approach to identify co-evolving amino acid positions of influenza A viruses (2012)
  19. Petitjean, François; Gançarski, Pierre: Summarizing a set of time series by averaging: from Steiner sequence to compact multiple alignment (2012)
  20. 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) ioport

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