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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

Modified selection process. This modified selection process is designed to only allow the recombination of individuals sharing the same K-value. In cases where no suitable mate exists, or where the pre-determined diversity threshold has been exceed, mutation will occur (with the standard probability) without crossover.
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Fig4: Modified selection process. This modified selection process is designed to only allow the recombination of individuals sharing the same K-value. In cases where no suitable mate exists, or where the pre-determined diversity threshold has been exceed, mutation will occur (with the standard probability) without crossover.

Mentions: As individuals in the population may have different K-values, special consideration must be given when carrying out the selection step. During the k-medoids phase of the fitness calculation, the current set of medoids is updated to a partially-optimized state. Crossover of medoids between solutions containing different numbers of clusters would result in a disruption to this improvement. If the algorithm were carrying out a full k-medoids implementation this would not present a problem since the medoids would be optimized on the next pass of the fitness function. Since however, only one pass through the medoids occur, crossover is constrained to individual sharing the same number of clusters. Figure 4 shows the modified selection process to account for this fact. Once the first parent is selected, a check is made to see if there are any other individuals in the population with the same number of clusters – if there are, then the second mate is selected from within that subpopulation and crossover occurs as normal. If the individual, however, is the only member of the population with that specific value of K, then no valid mate exists and the crossover step is skipped. Figure 4 also shows two additional features of the algorithm design. The first of these is the concept of population diversity, expressed as the proportion of the population with the same K-value. When this value is greater than a pre-defined threshold (default: 0.8), it gives an indication that the population has largely converged on a solution with a specific number of clusters and mutation is temporarily increased to both maintain the remaining diversity and encourage further exploration of cluster space. The second feature is the offspring validity check which is necessary after crossover and/or mutation to ensure that there are no duplicate medoids as a result of the recombination or mutation.Figure 4


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

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

Modified selection process. This modified selection process is designed to only allow the recombination of individuals sharing the same K-value. In cases where no suitable mate exists, or where the pre-determined diversity threshold has been exceed, mutation will occur (with the standard probability) without crossover.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: Modified selection process. This modified selection process is designed to only allow the recombination of individuals sharing the same K-value. In cases where no suitable mate exists, or where the pre-determined diversity threshold has been exceed, mutation will occur (with the standard probability) without crossover.
Mentions: As individuals in the population may have different K-values, special consideration must be given when carrying out the selection step. During the k-medoids phase of the fitness calculation, the current set of medoids is updated to a partially-optimized state. Crossover of medoids between solutions containing different numbers of clusters would result in a disruption to this improvement. If the algorithm were carrying out a full k-medoids implementation this would not present a problem since the medoids would be optimized on the next pass of the fitness function. Since however, only one pass through the medoids occur, crossover is constrained to individual sharing the same number of clusters. Figure 4 shows the modified selection process to account for this fact. Once the first parent is selected, a check is made to see if there are any other individuals in the population with the same number of clusters – if there are, then the second mate is selected from within that subpopulation and crossover occurs as normal. If the individual, however, is the only member of the population with that specific value of K, then no valid mate exists and the crossover step is skipped. Figure 4 also shows two additional features of the algorithm design. The first of these is the concept of population diversity, expressed as the proportion of the population with the same K-value. When this value is greater than a pre-defined threshold (default: 0.8), it gives an indication that the population has largely converged on a solution with a specific number of clusters and mutation is temporarily increased to both maintain the remaining diversity and encourage further exploration of cluster space. The second feature is the offspring validity check which is necessary after crossover and/or mutation to ensure that there are no duplicate medoids as a result of the recombination or mutation.Figure 4

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