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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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Related in: MedlinePlus

Robustness to noise. Left panel: The creation process for the five synthetic datasets to assess the ability of GMACS to maintain homogeneous clusters in the face of decreasing information content is depicted. The MYB_PH3 motif is combined with a motif generated by randomly sampling columns from the JASPAR database resulting in a much more degenerate motif. Right panel: The ability of GMACS to produce structurally homogeneous clusters remains robust, maintained at a level above 0.6 even when information content for each motif in the dataset has been significantly reduced.
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Fig9: Robustness to noise. Left panel: The creation process for the five synthetic datasets to assess the ability of GMACS to maintain homogeneous clusters in the face of decreasing information content is depicted. The MYB_PH3 motif is combined with a motif generated by randomly sampling columns from the JASPAR database resulting in a much more degenerate motif. Right panel: The ability of GMACS to produce structurally homogeneous clusters remains robust, maintained at a level above 0.6 even when information content for each motif in the dataset has been significantly reduced.

Mentions: Having benchmarked our algorithm against current state-of-the art methods, we sought to examine the boundaries of its ability to maintain homogeneous clusters in the face of low information content motifs. To that end we produced five further synthetic datasets, each comprised of the weighted combination of the original JASPAR 79 motifs and motifs generated through random sampling of columns from the entire JASPAR database. The resulting datasets comprise motifs which are 90% original motif signal and 10% random motif ‘noise’, 80% signal, 20% noise, and so on, up to equal weighting of both signal and noise. An example of this can be seen in Figure 9 (left panel), which shows the original MYB_PH3 motif, the randomly generated motif, and the modified MYB_PH3 resulting from the weighted combination of the two (0.6/0.4 respectively). Once these increasingly noisy datasets had been generated, the clustering process was repeated ten times for each set and the resulting range of cluster homogeneity at each level of random signal incorporation was examined. As shown in Figure 9 (right panel) GMACS demonstrates robustness to highly degenerate motifs, maintaining a cluster structural homogeneity score above 0.6 even when the information content has been reduced by 50%.Figure 9


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

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

Robustness to noise. Left panel: The creation process for the five synthetic datasets to assess the ability of GMACS to maintain homogeneous clusters in the face of decreasing information content is depicted. The MYB_PH3 motif is combined with a motif generated by randomly sampling columns from the JASPAR database resulting in a much more degenerate motif. Right panel: The ability of GMACS to produce structurally homogeneous clusters remains robust, maintained at a level above 0.6 even when information content for each motif in the dataset has been significantly reduced.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig9: Robustness to noise. Left panel: The creation process for the five synthetic datasets to assess the ability of GMACS to maintain homogeneous clusters in the face of decreasing information content is depicted. The MYB_PH3 motif is combined with a motif generated by randomly sampling columns from the JASPAR database resulting in a much more degenerate motif. Right panel: The ability of GMACS to produce structurally homogeneous clusters remains robust, maintained at a level above 0.6 even when information content for each motif in the dataset has been significantly reduced.
Mentions: Having benchmarked our algorithm against current state-of-the art methods, we sought to examine the boundaries of its ability to maintain homogeneous clusters in the face of low information content motifs. To that end we produced five further synthetic datasets, each comprised of the weighted combination of the original JASPAR 79 motifs and motifs generated through random sampling of columns from the entire JASPAR database. The resulting datasets comprise motifs which are 90% original motif signal and 10% random motif ‘noise’, 80% signal, 20% noise, and so on, up to equal weighting of both signal and noise. An example of this can be seen in Figure 9 (left panel), which shows the original MYB_PH3 motif, the randomly generated motif, and the modified MYB_PH3 resulting from the weighted combination of the two (0.6/0.4 respectively). Once these increasingly noisy datasets had been generated, the clustering process was repeated ten times for each set and the resulting range of cluster homogeneity at each level of random signal incorporation was examined. As shown in Figure 9 (right panel) GMACS demonstrates robustness to highly degenerate motifs, maintaining a cluster structural homogeneity score above 0.6 even when the information content has been reduced by 50%.Figure 9

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