T-Coffee: a novel method for fast and accurate multiple sequence alignment. We describe a new method (T-Coffee) for multiple sequence alignment that provides a dramatic improvement in accuracy with a modest sacrifice in speed as compared to the most commonly used alternatives. The method is broadly based on the popular progressive approach to multiple alignment but avoids the most serious pitfalls caused by the greedy nature of this algorithm. With T-Coffee we pre-process a data set of all pair-wise alignments between the sequences. This provides us with a library of alignment information that can be used to guide the progressive alignment. Intermediate alignments are then based not only on the sequences to be aligned next but also on how all of the sequences align with each other. This alignment information can be derived from heterogeneous sources such as a mixture of alignment programs and/or structure superposition. Here, we illustrate the power of the approach by using a combination of local and global pair-wise alignments to generate the library. The resulting alignments are significantly more reliable, as determined by comparison with a set of 141 test cases, than any of the popular alternatives that we tried. The improvement, especially clear with the more difficult test cases, is always visible, regardless of the phylogenetic spread of the sequences in the tests.

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  1. Chen, Weiyang; Liao, Bo; Li, Weiwei: Use of image texture analysis to find DNA sequence similarities (2018)
  2. Prohaska, Sonja J.; Berkemer, Sarah J.; Gärtner, Fabian; Gatter, Thomas; Retzlaff, Nancy; Höner zu Siederdissen, Christian; Stadler, Peter F.: Expansion of gene clusters, circular orders, and the shortest Hamiltonian path problem (2018)
  3. Arribas-Gil, Ana; Matias, Catherine: A time warping approach to multiple sequence alignment (2017)
  4. DeBlasio, Dan; Kececioglu, John: Parameter advising for multiple sequence alignment (2017)
  5. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  6. Pesch, Robert: Cross-species network and transcript transfer (2016)
  7. Fürstberger, Axel; Maucher, Markus; Kestler, Hans A.: Extended pairwise local alignment of wild card DNA/RNA sequences using dynamic programming (2015)
  8. 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)
  9. Federico, Maria; Peterlongo, Pierre; Pisanti, Nadia; Sagot, Marie-France: \textscRime: repeat identification (2014)
  10. Le Thi, Hoai An; Dinh, Tao Pham; Belghiti, Moulay: DCA based algorithms for multiple sequence alignment (MSA) (2014)
  11. Wang, Lei; Peng, Hui; Zheng, Jinhua: ADLD: a novel graphical representation of protein sequences and its application (2014)
  12. Daugelaite, Jurate; O’Driscoll, Aisling; Sleator, Roy D.: An overview of multiple sequence alignments and cloud computing in bioinformatics (2013)
  13. Andoni, Alexandr; Daskalakis, Constantinos; Hassidim, Avinatan; Roch, Sebastien: Global alignment of molecular sequences via ancestral state reconstruction (2012)
  14. Hanif, Muhammad Kashif; Zimmermann, Karl-Heinz: Graphics card processing: accelerating profile-profile alignment (2012) ioport
  15. Petitjean, François; Gançarski, Pierre: Summarizing a set of time series by averaging: from Steiner sequence to compact multiple alignment (2012)
  16. Elloumi, Mourad (ed.); Zomaya, Albert Y. (ed.): Algorithms in computational molecular biology. Techniques approaches and applications. (2011)
  17. Niranjan Reddy, B. P.; Prasad, G. B. K. S.; Raghavendra, K.: \textitInsilico analysis of glutathione S-transferase supergene family revealed hitherto unreported insect specific (\delta)- and (\varepsilon)-GSTs and mammalian specific (\mu)-GSTs in \textitIxodesscapularis (Acari: Ixodidae) (2011)
  18. Otto, Wolfgang; Stadler, Peter F.; Prohaska, Sonja J.: Phylogenetic footprinting and consistent sets of local aligments (2011)
  19. Petitjean, François; Ketterlin, Alain; Gançarski, Pierre: A global averaging method for dynamic time warping, with applications to clustering (2011)
  20. Durand, Pierre M.; Hazelhurst, Scott; Coetzer, Theresa L.: Evolutionary rates at codon sites May be used to align sequences and infer protein domain function (2010) ioport

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