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

Broin PÓ, Smith TJ, Golden AA - BMC Bioinformatics (2015)

Bottom Line: We demonstrate the performance of our algorithm, GMACS, on multiple benchmark motif datasets, comparing results obtained with current leading approaches.Finally, we show the robustness of our algorithm to noise on multiple synthetic datasets consisting of known motifs convolved with varying degrees of noise.Our 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, New York, 10461, USA. pilib.obroin@einstein.yu.edu.

ABSTRACT

Background: Familial 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.

Results: We 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.

Conclusions: Our 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.

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Sample familial binding profile. Sample FBP for four transcription factors from the basic Helix-Loop-Helix structural family. Columns which have low information content or are only present in a small number of the individual motifs are typically excluded from the FBP.
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Fig1: Sample familial binding profile. Sample FBP for four transcription factors from the basic Helix-Loop-Helix structural family. Columns which have low information content or are only present in a small number of the individual motifs are typically excluded from the FBP.

Mentions: The concept of a familial binding profile (FBP), or average binding specificity for a group of structurally related TFs (as shown in Figure 1), was introduced by the authors in [3], when they manually constructed 11 FBPs from 71 non-zinc-finger motifs taken from the JASPAR database [4]. FBPs are an important tool in regulatory genomics and serve a multitude of purposes: i) they can be used as informative priors for motif discovery algorithms, either biasing the search to TFs from a particular structural family, or providing a way to filter out spurious patterns and thereby increasing sensitivity [3,5], ii) they can be used to classify novel binding proteins based on their similarity to the binding affinities of known structural families [6,7], iii) they can be used to reduce redundancy in motif databases where minor variations or submotifs from the same binding site are incorrectly labelled as separate motifs; this redundancy reduction can also be applied to motif finding algorithms, either to merge similar motif predictions from a single algorithm or to combine results from multiple algorithms [8,9], and iv) they can be used to analyze binding site turnover and provide insights into how DNA-binding mechanisms have evolved over time [10].Figure 1


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

Broin PÓ, Smith TJ, Golden AA - BMC Bioinformatics (2015)

Sample familial binding profile. Sample FBP for four transcription factors from the basic Helix-Loop-Helix structural family. Columns which have low information content or are only present in a small number of the individual motifs are typically excluded from the FBP.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4384390&req=5

Fig1: Sample familial binding profile. Sample FBP for four transcription factors from the basic Helix-Loop-Helix structural family. Columns which have low information content or are only present in a small number of the individual motifs are typically excluded from the FBP.
Mentions: The concept of a familial binding profile (FBP), or average binding specificity for a group of structurally related TFs (as shown in Figure 1), was introduced by the authors in [3], when they manually constructed 11 FBPs from 71 non-zinc-finger motifs taken from the JASPAR database [4]. FBPs are an important tool in regulatory genomics and serve a multitude of purposes: i) they can be used as informative priors for motif discovery algorithms, either biasing the search to TFs from a particular structural family, or providing a way to filter out spurious patterns and thereby increasing sensitivity [3,5], ii) they can be used to classify novel binding proteins based on their similarity to the binding affinities of known structural families [6,7], iii) they can be used to reduce redundancy in motif databases where minor variations or submotifs from the same binding site are incorrectly labelled as separate motifs; this redundancy reduction can also be applied to motif finding algorithms, either to merge similar motif predictions from a single algorithm or to combine results from multiple algorithms [8,9], and iv) they can be used to analyze binding site turnover and provide insights into how DNA-binding mechanisms have evolved over time [10].Figure 1

Bottom Line: We demonstrate the performance of our algorithm, GMACS, on multiple benchmark motif datasets, comparing results obtained with current leading approaches.Finally, we show the robustness of our algorithm to noise on multiple synthetic datasets consisting of known motifs convolved with varying degrees of noise.Our 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, New York, 10461, USA. pilib.obroin@einstein.yu.edu.

ABSTRACT

Background: Familial 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.

Results: We 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.

Conclusions: Our 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.

Show MeSH
Related in: MedlinePlus