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On avoided words, absent words, and their application to biological sequence analysis

BACKGROUND: The deviation of the observed frequency of a word w from its expected frequency in a given sequence x is used to determine whether or not the word is avoided. This concept is particularly useful in DNA linguistic analysis. The value of the deviation of w, denoted by [Formula: see text] ,...

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Autores principales: Almirantis, Yannis, Charalampopoulos, Panagiotis, Gao, Jia, Iliopoulos, Costas S., Mohamed, Manal, Pissis, Solon P., Polychronopoulos, Dimitris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5348888/
https://www.ncbi.nlm.nih.gov/pubmed/28293277
http://dx.doi.org/10.1186/s13015-017-0094-z
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author Almirantis, Yannis
Charalampopoulos, Panagiotis
Gao, Jia
Iliopoulos, Costas S.
Mohamed, Manal
Pissis, Solon P.
Polychronopoulos, Dimitris
author_facet Almirantis, Yannis
Charalampopoulos, Panagiotis
Gao, Jia
Iliopoulos, Costas S.
Mohamed, Manal
Pissis, Solon P.
Polychronopoulos, Dimitris
author_sort Almirantis, Yannis
collection PubMed
description BACKGROUND: The deviation of the observed frequency of a word w from its expected frequency in a given sequence x is used to determine whether or not the word is avoided. This concept is particularly useful in DNA linguistic analysis. The value of the deviation of w, denoted by [Formula: see text] , effectively characterises the extent of a word by its edge contrast in the context in which it occurs. A word w of length [Formula: see text] is a [Formula: see text] -avoided word in x if [Formula: see text] , for a given threshold [Formula: see text] . Notice that such a word may be completely absent from x. Hence, computing all such words naïvely can be a very time-consuming procedure, in particular for large k. RESULTS: In this article, we propose an [Formula: see text] -time and [Formula: see text] -space algorithm to compute all [Formula: see text] -avoided words of length k in a given sequence of length n over a fixed-sized alphabet. We also present a time-optimal [Formula: see text] -time algorithm to compute all [Formula: see text] -avoided words (of any length) in a sequence of length n over an integer alphabet of size [Formula: see text] . In addition, we provide a tight asymptotic upper bound for the number of [Formula: see text] -avoided words over an integer alphabet and the expected length of the longest one. We make available an implementation of our algorithm. Experimental results, using both real and synthetic data, show the efficiency and applicability of our implementation in biological sequence analysis. CONCLUSIONS: The systematic search for avoided words is particularly useful for biological sequence analysis. We present a linear-time and linear-space algorithm for the computation of avoided words of length k in a given sequence x. We suggest a modification to this algorithm so that it computes all avoided words of x, irrespective of their length, within the same time complexity. We also present combinatorial results with regards to avoided words and absent words.
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spelling pubmed-53488882017-03-14 On avoided words, absent words, and their application to biological sequence analysis Almirantis, Yannis Charalampopoulos, Panagiotis Gao, Jia Iliopoulos, Costas S. Mohamed, Manal Pissis, Solon P. Polychronopoulos, Dimitris Algorithms Mol Biol Research BACKGROUND: The deviation of the observed frequency of a word w from its expected frequency in a given sequence x is used to determine whether or not the word is avoided. This concept is particularly useful in DNA linguistic analysis. The value of the deviation of w, denoted by [Formula: see text] , effectively characterises the extent of a word by its edge contrast in the context in which it occurs. A word w of length [Formula: see text] is a [Formula: see text] -avoided word in x if [Formula: see text] , for a given threshold [Formula: see text] . Notice that such a word may be completely absent from x. Hence, computing all such words naïvely can be a very time-consuming procedure, in particular for large k. RESULTS: In this article, we propose an [Formula: see text] -time and [Formula: see text] -space algorithm to compute all [Formula: see text] -avoided words of length k in a given sequence of length n over a fixed-sized alphabet. We also present a time-optimal [Formula: see text] -time algorithm to compute all [Formula: see text] -avoided words (of any length) in a sequence of length n over an integer alphabet of size [Formula: see text] . In addition, we provide a tight asymptotic upper bound for the number of [Formula: see text] -avoided words over an integer alphabet and the expected length of the longest one. We make available an implementation of our algorithm. Experimental results, using both real and synthetic data, show the efficiency and applicability of our implementation in biological sequence analysis. CONCLUSIONS: The systematic search for avoided words is particularly useful for biological sequence analysis. We present a linear-time and linear-space algorithm for the computation of avoided words of length k in a given sequence x. We suggest a modification to this algorithm so that it computes all avoided words of x, irrespective of their length, within the same time complexity. We also present combinatorial results with regards to avoided words and absent words. BioMed Central 2017-03-14 /pmc/articles/PMC5348888/ /pubmed/28293277 http://dx.doi.org/10.1186/s13015-017-0094-z Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Almirantis, Yannis
Charalampopoulos, Panagiotis
Gao, Jia
Iliopoulos, Costas S.
Mohamed, Manal
Pissis, Solon P.
Polychronopoulos, Dimitris
On avoided words, absent words, and their application to biological sequence analysis
title On avoided words, absent words, and their application to biological sequence analysis
title_full On avoided words, absent words, and their application to biological sequence analysis
title_fullStr On avoided words, absent words, and their application to biological sequence analysis
title_full_unstemmed On avoided words, absent words, and their application to biological sequence analysis
title_short On avoided words, absent words, and their application to biological sequence analysis
title_sort on avoided words, absent words, and their application to biological sequence analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5348888/
https://www.ncbi.nlm.nih.gov/pubmed/28293277
http://dx.doi.org/10.1186/s13015-017-0094-z
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