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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

Basic Helix-Loop-Helix family motifs. The bHLH familty of motifs from the JASPAR dataset comprises three distinct binding subtypes. GMACS correctly classifies the motifs into these three subtypes, the core subtype containing six motifs (Arnt, AHR-ARNT, MAX, MYC-MAX, USF, n-MYC), and the MYF (MYF, NHLH1) and TCF (HAND1, TAL1) subtypes, each containing two motifs.
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Fig5: Basic Helix-Loop-Helix family motifs. The bHLH familty of motifs from the JASPAR dataset comprises three distinct binding subtypes. GMACS correctly classifies the motifs into these three subtypes, the core subtype containing six motifs (Arnt, AHR-ARNT, MAX, MYC-MAX, USF, n-MYC), and the MYF (MYF, NHLH1) and TCF (HAND1, TAL1) subtypes, each containing two motifs.

Mentions: We begin our detailed examination of the results with the ten members of the bHLH family which form three distinct subgroups (as shown in Figure 5). STAMP creates two homogeneous clusters with six and two members respectively. Of the remaining members, one is clustered with the GATA1 zinc-finger and FOXL1 forkhead motifs, while the other is clustered with the ETS family. MoSta groups the motifs as a cluster of five and three. The larger cluster is surprisingly missing the AHR-ARNT motif which contains the strong consensus ‘CACGTG’ sequence associated with that subgroup, while the smaller cluster includes the TAL1-TCF3, NHLH1, and MYF motifs, mixing the remaining subtypes. GMACS provides the only approach to create three homogeneous clusters. The first cluster is the same six-member group created by STAMP, the second contains the NHLH1 and MYF motifs (MYF subgroup), while the final cluster groups the TAL1 and HAND1 (TCF subgroup) motifs together.Figure 5


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

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

Basic Helix-Loop-Helix family motifs. The bHLH familty of motifs from the JASPAR dataset comprises three distinct binding subtypes. GMACS correctly classifies the motifs into these three subtypes, the core subtype containing six motifs (Arnt, AHR-ARNT, MAX, MYC-MAX, USF, n-MYC), and the MYF (MYF, NHLH1) and TCF (HAND1, TAL1) subtypes, each containing two motifs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig5: Basic Helix-Loop-Helix family motifs. The bHLH familty of motifs from the JASPAR dataset comprises three distinct binding subtypes. GMACS correctly classifies the motifs into these three subtypes, the core subtype containing six motifs (Arnt, AHR-ARNT, MAX, MYC-MAX, USF, n-MYC), and the MYF (MYF, NHLH1) and TCF (HAND1, TAL1) subtypes, each containing two motifs.
Mentions: We begin our detailed examination of the results with the ten members of the bHLH family which form three distinct subgroups (as shown in Figure 5). STAMP creates two homogeneous clusters with six and two members respectively. Of the remaining members, one is clustered with the GATA1 zinc-finger and FOXL1 forkhead motifs, while the other is clustered with the ETS family. MoSta groups the motifs as a cluster of five and three. The larger cluster is surprisingly missing the AHR-ARNT motif which contains the strong consensus ‘CACGTG’ sequence associated with that subgroup, while the smaller cluster includes the TAL1-TCF3, NHLH1, and MYF motifs, mixing the remaining subtypes. GMACS provides the only approach to create three homogeneous clusters. The first cluster is the same six-member group created by STAMP, the second contains the NHLH1 and MYF motifs (MYF subgroup), while the final cluster groups the TAL1 and HAND1 (TCF subgroup) motifs together.Figure 5

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