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libFLASM: a software library for fixed-length approximate string matching

Overview of attention for article published in BMC Bioinformatics, November 2016
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Title
libFLASM: a software library for fixed-length approximate string matching
Published in
BMC Bioinformatics, November 2016
DOI 10.1186/s12859-016-1320-2
Pubmed ID
Authors

Lorraine A. K. Ayad, Solon P. Pissis, Ahmad Retha

Abstract

Approximate string matching is the problem of finding all factors of a given text that are at a distance at most k from a given pattern. Fixed-length approximate string matching is the problem of finding all factors of a text of length n that are at a distance at most k from any factor of length ℓ of a pattern of length m. There exist bit-vector techniques to solve the fixed-length approximate string matching problem in time [Formula: see text] and space [Formula: see text] under the edit and Hamming distance models, where w is the size of the computer word; as such these techniques are independent of the distance threshold k or the alphabet size. Fixed-length approximate string matching is a generalisation of approximate string matching and, hence, has numerous direct applications in computational molecular biology and elsewhere. We present and make available libFLASM, a free open-source C++ software library for solving fixed-length approximate string matching under both the edit and the Hamming distance models. Moreover we describe how fixed-length approximate string matching is applied to solve real problems by incorporating libFLASM into established applications for multiple circular sequence alignment as well as single and structured motif extraction. Specifically, we describe how it can be used to improve the accuracy of multiple circular sequence alignment in terms of the inferred likelihood-based phylogenies; and we also describe how it is used to efficiently find motifs in molecular sequences representing regulatory or functional regions. The comparison of the performance of the library to other algorithms show how it is competitive, especially with increasing distance thresholds. Fixed-length approximate string matching is a generalisation of the classic approximate string matching problem. We present libFLASM, a free open-source C++ software library for solving fixed-length approximate string matching. The extensive experimental results presented here suggest that other applications could benefit from using libFLASM, and thus further maintenance and development of libFLASM is desirable.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 2 15%
Student > Bachelor 2 15%
Researcher 2 15%
Student > Ph. D. Student 2 15%
Librarian 1 8%
Other 2 15%
Unknown 2 15%
Readers by discipline Count As %
Computer Science 4 31%
Biochemistry, Genetics and Molecular Biology 2 15%
Arts and Humanities 2 15%
Agricultural and Biological Sciences 2 15%
Economics, Econometrics and Finance 1 8%
Other 0 0%
Unknown 2 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 18 November 2016.
All research outputs
#14,279,821
of 22,899,952 outputs
Outputs from BMC Bioinformatics
#4,746
of 7,302 outputs
Outputs of similar age
#178,324
of 312,766 outputs
Outputs of similar age from BMC Bioinformatics
#58
of 125 outputs
Altmetric has tracked 22,899,952 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,302 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 312,766 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 125 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 52% of its contemporaries.