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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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Final cluster composition. Cluster membership and FBPs for the eighteen clusters identified by our method in the test dataset of 79 JASPAR motifs. Clusters marked with an asterisk are structurally homogeneous.
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Fig8: Final cluster composition. Cluster membership and FBPs for the eighteen clusters identified by our method in the test dataset of 79 JASPAR motifs. Clusters marked with an asterisk are structurally homogeneous.

Mentions: Once we have clustered the motifs, we must generate the FBP for each of the defined clusters. This can be achieved through any of the standard multiple alignment methods, although it has previously been shown that a local Smith-Waterman alignment may be preferred for binding motifs which are typically short ungapped sequences [10]. The membership and FBPs for each of the clusters derived by GMACS for the JASPAR dataset are shown in Figure 8. In order to assess the stability of the FBPs in our solution, we perform a leave-one-out cross-validation (LOOCV) as carried out in [10,11]. GMACS achieves a LOOCV rate of 0.96 for the 79 JASPAR motifs, compared to 0.91 for STAMP. The improved classification rate for our approach is unsurprising given that more of the clusters elucidated are structurally homogeneous and therefore FBPs are less likely to be affected by the removal of any individual motif. The MoSta algorithm [11] successfully manages to re-cluster all the 67 clustered motifs to their cognate FBPs achieving a LOOCV accuracy rate of 1.0 - this result however is achieved based on the exclusion of all singleton clusters from the original solution.Figure 8


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

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

Final cluster composition. Cluster membership and FBPs for the eighteen clusters identified by our method in the test dataset of 79 JASPAR motifs. Clusters marked with an asterisk are structurally homogeneous.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig8: Final cluster composition. Cluster membership and FBPs for the eighteen clusters identified by our method in the test dataset of 79 JASPAR motifs. Clusters marked with an asterisk are structurally homogeneous.
Mentions: Once we have clustered the motifs, we must generate the FBP for each of the defined clusters. This can be achieved through any of the standard multiple alignment methods, although it has previously been shown that a local Smith-Waterman alignment may be preferred for binding motifs which are typically short ungapped sequences [10]. The membership and FBPs for each of the clusters derived by GMACS for the JASPAR dataset are shown in Figure 8. In order to assess the stability of the FBPs in our solution, we perform a leave-one-out cross-validation (LOOCV) as carried out in [10,11]. GMACS achieves a LOOCV rate of 0.96 for the 79 JASPAR motifs, compared to 0.91 for STAMP. The improved classification rate for our approach is unsurprising given that more of the clusters elucidated are structurally homogeneous and therefore FBPs are less likely to be affected by the removal of any individual motif. The MoSta algorithm [11] successfully manages to re-cluster all the 67 clustered motifs to their cognate FBPs achieving a LOOCV accuracy rate of 1.0 - this result however is achieved based on the exclusion of all singleton clusters from the original solution.Figure 8

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