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

BMC Bioinformatics

Software

Terry J Smith¹  Aaron AJ Golden²  Pilib Ó Broin³ 

[1] Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, 10461, New York, USA;Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, 10461, New York, USA;Department of Mathematical Sciences, Yeshiva University, 10033, New York, NY, USA;Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, 10461, New York, USA;National Centre for Biomedical Engineering Science, National University of Ireland, University Road, Galway, Ireland;

关键词: Transcription factor;    Motif;    Clustering;    Genetic algorithm;   

DOI  :  10.1186/s12859-015-0450-2

 received in 2014-08-04, accepted in 2015-01-02,  发布年份 2015

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Ó Broin et al.; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.