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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

Solution representation and evolutionary operators. Depiction of crossover and mutation in GMACS. The upper section shows the medoid vector representation of two selected parents, one wholly-shaded and one wholly-unshaded. Shown below them are the two offspring resulting from their uniform crossover. The lower section demonstrates the two modes of mutation: addition or removal of a randomly selected cluster, shown as shaded.
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Fig3: Solution representation and evolutionary operators. Depiction of crossover and mutation in GMACS. The upper section shows the medoid vector representation of two selected parents, one wholly-shaded and one wholly-unshaded. Shown below them are the two offspring resulting from their uniform crossover. The lower section demonstrates the two modes of mutation: addition or removal of a randomly selected cluster, shown as shaded.

Mentions: The medoid vector representation and the effects of the crossover and mutation operators on those encodings are shown in Figure 3. Two parents are shown at the top of the figure, one shaded and one unshaded. Both have five clusters (a point we will return to shortly), and the index of the motif currently assigned as the medoid for each of these clusters is shown as an integer value. The form of crossover we use is termed ‘uniform crossover’, meaning that each separate gene in an offspring’s genotype has an equal chance of coming from either parent. This type of crossover, while less common than single- or multi-point crossover, arguably produces a wider range of genotypes, exploring more of the search space. In order to explore solutions with different numbers of clusters (particularly those which may not arise as part of the random initialization), the mutation operator functions by perturbing the K-value for a given solution, either adding a cluster by copying the existing medoids and choosing at random an additional medoid from the remaining motifs, or removing a cluster (provided K>2), by randomly choosing a medoid to delete. The probability of a mutation occurring is typically kept quite low (lest the GA risk becoming a totally random walk), and here, the rate is set at 0.05.Figure 3


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

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

Solution representation and evolutionary operators. Depiction of crossover and mutation in GMACS. The upper section shows the medoid vector representation of two selected parents, one wholly-shaded and one wholly-unshaded. Shown below them are the two offspring resulting from their uniform crossover. The lower section demonstrates the two modes of mutation: addition or removal of a randomly selected cluster, shown as shaded.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Solution representation and evolutionary operators. Depiction of crossover and mutation in GMACS. The upper section shows the medoid vector representation of two selected parents, one wholly-shaded and one wholly-unshaded. Shown below them are the two offspring resulting from their uniform crossover. The lower section demonstrates the two modes of mutation: addition or removal of a randomly selected cluster, shown as shaded.
Mentions: The medoid vector representation and the effects of the crossover and mutation operators on those encodings are shown in Figure 3. Two parents are shown at the top of the figure, one shaded and one unshaded. Both have five clusters (a point we will return to shortly), and the index of the motif currently assigned as the medoid for each of these clusters is shown as an integer value. The form of crossover we use is termed ‘uniform crossover’, meaning that each separate gene in an offspring’s genotype has an equal chance of coming from either parent. This type of crossover, while less common than single- or multi-point crossover, arguably produces a wider range of genotypes, exploring more of the search space. In order to explore solutions with different numbers of clusters (particularly those which may not arise as part of the random initialization), the mutation operator functions by perturbing the K-value for a given solution, either adding a cluster by copying the existing medoids and choosing at random an additional medoid from the remaining motifs, or removing a cluster (provided K>2), by randomly choosing a medoid to delete. The probability of a mutation occurring is typically kept quite low (lest the GA risk becoming a totally random walk), and here, the rate is set at 0.05.Figure 3

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