review · Briefings in Bioinformatics · 2013
Pattern recognition and probabilistic measures in alignment-free sequence analysis
Cited by 32Open access
Abstract
With the massive production of genomic and proteomic data, the number of available biological sequences in databases has reached a level that is not feasible anymore for exact alignments even when just a fraction of all sequences is used. To overcome this inevitable time complexity, ultrafast alignment-free methods are studied. Within the past two decades, a broad variety of nonalignment methods have been proposed including dissimilarity measures on classical representations of sequences like k-words or Markov models. Furthermore, articles were published that describe distance measures on alternative representations such as compression complexity, spectral time series or chaos game representation. However, alignments are still the standard method for real world applications in biological sequence analysis, and the time efficient alignment-free approaches are usually applied in cases when the accustomed algorithms turn out to fail or be too inconvenient.
Cite this paper
Schwende, I., & Pham, T. D. (2013). Pattern recognition and probabilistic measures in alignment-free sequence analysis. Briefings in Bioinformatics, 15(3), 354–368. https://doi.org/10.1093/bib/bbt070
Schwende, Isabel, and Tuan D. Pham. “Pattern recognition and probabilistic measures in alignment-free sequence analysis.” Briefings in Bioinformatics, vol. 15, no. 3, 2013, pp. 354–68, https://doi.org/10.1093/bib/bbt070.
Schwende, Isabel, and Tuan D. Pham. 2013. “Pattern recognition and probabilistic measures in alignment-free sequence analysis.” Briefings in Bioinformatics 15 (3): 354–68. https://doi.org/10.1093/bib/bbt070.
Schwende, I. and Pham, T.D. (2013) ‘Pattern recognition and probabilistic measures in alignment-free sequence analysis’, Briefings in Bioinformatics, 15(3), pp. 354–368. Available at: https://doi.org/10.1093/bib/bbt070.
@article{schwende2013pattern,
title = {{Pattern recognition and probabilistic measures in alignment-free sequence analysis}},
author = {Schwende, Isabel and Pham, Tuan D.},
journal = {Briefings in Bioinformatics},
volume = {15},
number = {3},
pages = {354--368},
year = {2013},
doi = {10.1093/bib/bbt070}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.