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

Overview of attention for article published in Algorithms for Molecular Biology, March 2017
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Title
On avoided words, absent words, and their application to biological sequence analysis
Published in
Algorithms for Molecular Biology, March 2017
DOI 10.1186/s13015-017-0094-z
Pubmed ID
Authors

Yannis Almirantis, Panagiotis Charalampopoulos, Jia Gao, Costas S. Iliopoulos, Manal Mohamed, Solon P. Pissis, Dimitris Polychronopoulos, Yannis Almirantis, Panagiotis Charalampopoulos, Jia Gao, Costas S. Iliopoulos, Manal Mohamed, Solon P. Pissis, Dimitris Polychronopoulos

Abstract

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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 14%
United Kingdom 1 14%
Unknown 5 71%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 57%
Researcher 2 29%
Student > Doctoral Student 1 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 43%
Biochemistry, Genetics and Molecular Biology 2 29%
Computer Science 1 14%
Unspecified 1 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 March 2017.
All research outputs
#5,010,747
of 9,201,335 outputs
Outputs from Algorithms for Molecular Biology
#86
of 169 outputs
Outputs of similar age
#146,866
of 254,452 outputs
Outputs of similar age from Algorithms for Molecular Biology
#4
of 5 outputs
Altmetric has tracked 9,201,335 research outputs across all sources so far. This one is in the 27th percentile – i.e., 27% of other outputs scored the same or lower than it.
So far Altmetric has tracked 169 research outputs from this source. They receive a mean Attention Score of 2.6. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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