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Alignment-free clustering of transcription factor binding motifs using a genetic-k-medoids approach

Overview of attention for article published in BMC Bioinformatics, January 2015
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Title
Alignment-free clustering of transcription factor binding motifs using a genetic-k-medoids approach
Published in
BMC Bioinformatics, January 2015
DOI 10.1186/s12859-015-0450-2
Pubmed ID
Authors

Pilib Ó Broin, Terry J Smith, Aaron AJ Golden

Abstract

BackgroundFamilial binding profiles (FBPs) represent the average binding specificity for a group of structurally related DNA-binding proteins. The construction of such profiles allows the classification of novel motifs based on similarity to known families, can help to reduce redundancy in motif databases and de novo prediction algorithms, and can provide valuable insights into the evolution of binding sites. Many current approaches to automated motif clustering rely on progressive tree-based techniques, and can suffer from so-called frozen sub-alignments, where motifs which are clustered early on in the process remain `locked¿ in place despite the potential for better placement at a later stage. In order to avoid this scenario, we have developed a genetic-k-medoids approach which allows motifs to move freely between clusters at any point in the clustering process.ResultsWe demonstrate the performance of our algorithm, GMACS, on multiple benchmark motif datasets, comparing results obtained with current leading approaches. The first dataset includes 355 position weight matrices from the TRANSFAC database and indicates that the k-mer frequency vector approach used in GMACS outperforms other motif comparison techniques. We then cluster a set of 79 motifs from the JASPAR database previously used in several motif clustering studies and demonstrate that GMACS can produce a higher number of structurally homogeneous clusters than other methods without the need for a large number of singletons. Finally, we show the robustness of our algorithm to noise on multiple synthetic datasets consisting of known motifs convolved with varying degrees of noise.ConclusionsOur proposed algorithm is generally applicable to any DNA or protein motifs, can produce highly stable and biologically meaningful clusters, and, by avoiding the problem of frozen sub-alignments, can provide improved results when compared with existing techniques on benchmark datasets.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 4%
United States 1 4%
Unknown 21 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 39%
Professor 5 22%
Student > Master 2 9%
Other 2 9%
Researcher 2 9%
Other 3 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 39%
Biochemistry, Genetics and Molecular Biology 6 26%
Computer Science 6 26%
Unspecified 2 9%

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 29 January 2015.
All research outputs
#8,687,837
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#3,315
of 4,195 outputs
Outputs of similar age
#160,639
of 253,066 outputs
Outputs of similar age from BMC Bioinformatics
#102
of 138 outputs
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