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Pattern recognition and probabilistic measures in alignment-free sequence analysis
University of Aizu, Japan.
The Aizu Research Cluster for Medical Engineering and Informatics (ARC-Medical), Research Center for Advanced Information Science and Technology, The University of Aizu, Japan.ORCID iD: 0000-0002-4255-5130
2013 (English)In: Briefings in Bioinformatics, ISSN 1467-5463, E-ISSN 1477-4054, Vol. 15, no 3, 354-368 p.Article in journal (Refereed) PublishedText
Abstract [en]

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.

Place, publisher, year, edition, pages
Oxford University Press, 2013. Vol. 15, no 3, 354-368 p.
Keyword [en]
alignment-free; sequence comparison; distance measures; distortion measures; pattern classification; signal processing
National Category
Bioinformatics (Computational Biology)
URN: urn:nbn:se:liu:diva-125052DOI: 10.1093/bib/bbt070ISI: 000336530600003PubMedID: 24096012OAI: diva2:902704
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2016-05-04Bibliographically approved

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Pham, Tuan D
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