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Linear-time computation of minimal absent words using suffix array

Overview of attention for article published in BMC Bioinformatics, December 2014
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  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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Title
Linear-time computation of minimal absent words using suffix array
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0388-9
Pubmed ID
Authors

Carl Barton, Alice Heliou, Laurent Mouchard, Solon P Pissis

Abstract

BackgroundAn absent word of a word y of length n is a word that does not occur in y. It is a minimal absent word if all its proper factors occur in y. Minimal absent words have been computed in genomes of organisms from all domains of life; their computation also provides a fast alternative for measuring approximation in sequence comparison. There exists an O(n) -time and O(n) -space algorithm for computing all minimal absent words on a fixed-sized alphabet based on the construction of suffix automata (Crochemore et al., 1998). No implementation of this algorithm is publicly available. There also exists an O(n2) -time and O(n) -space algorithm for the same problem based on the construction of suffix arrays (Pinho et al., 2009). An implementation of this algorithm was also provided by the authors and is currently the fastest available.ResultsOur contribution in this article is twofold: first, we bridge this unpleasant gap by presenting an O(n) -time and O(n) -space algorithm for computing all minimal absent words based on the construction of suffix arrays; and second, we provide the respective implementation of this algorithm. Experimental results, using real and synthetic data, show that this implementation outperforms the one by Pinho et al. The open-source code of our implementation is freely available at http://github.com/solonas13/maw.ConclusionsClassical notions for sequence comparison are increasingly being replaced by other similarity measures that refer to the composition of sequences in terms of their constituent patterns. One such measure is the minimal absent words. In this article, we present a new linear-time and linear-space algorithm for the computation of minimal absent words based on the suffix array.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Germany 1 5%
Unknown 19 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 29%
Researcher 5 24%
Student > Master 4 19%
Student > Bachelor 2 10%
Professor > Associate Professor 1 5%
Other 1 5%
Unknown 2 10%
Readers by discipline Count As %
Computer Science 10 48%
Biochemistry, Genetics and Molecular Biology 3 14%
Agricultural and Biological Sciences 3 14%
Environmental Science 1 5%
Chemistry 1 5%
Other 1 5%
Unknown 2 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 September 2015.
All research outputs
#7,138,842
of 23,313,051 outputs
Outputs from BMC Bioinformatics
#2,744
of 7,384 outputs
Outputs of similar age
#96,785
of 355,968 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 152 outputs
Altmetric has tracked 23,313,051 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,384 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 355,968 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 152 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.