Limits...
Linear normalised hash function for clustering gene sequences and identifying reference sequences from multiple sequence alignments.

Helal M, Kong F, Chen SC, Zhou F, Dwyer DE, Potter J, Sintchenko V - Microb Inform Exp (2012)

Bottom Line: However, defining the optimal number of clusters, cluster density and boundaries for sets of potentially related sequences of genes with variable degrees of polymorphism remains a significant challenge.The method was evaluated using sets of closely related (16S rRNA gene sequences of Nocardia species) and highly variable (VP1 genomic region of Enterovirus 71) sequences and outperformed existing unsupervised machine learning clustering methods and dimensionality reduction methods.This method does not require prior knowledge of the number of clusters or the distance between clusters, handles clusters of different sizes and shapes, and scales linearly with the dataset.

View Article: PubMed Central - HTML - PubMed

Affiliation: Sydney Emerging Infections and Biosecurity Institute, Sydney Medical School - Westmead, University of Sydney, Sydney, New South Wales, Australia. vitali.sintchenko@swahs.health.nsw.gov.au.

ABSTRACT

Background: Comparative genomics has put additional demands on the assessment of similarity between sequences and their clustering as means for classification. However, defining the optimal number of clusters, cluster density and boundaries for sets of potentially related sequences of genes with variable degrees of polymorphism remains a significant challenge. The aim of this study was to develop a method that would identify the cluster centroids and the optimal number of clusters for a given sensitivity level and could work equally well for the different sequence datasets.

Results: A novel method that combines the linear mapping hash function and multiple sequence alignment (MSA) was developed. This method takes advantage of the already sorted by similarity sequences from the MSA output, and identifies the optimal number of clusters, clusters cut-offs, and clusters centroids that can represent reference gene vouchers for the different species. The linear mapping hash function can map an already ordered by similarity distance matrix to indices to reveal gaps in the values around which the optimal cut-offs of the different clusters can be identified. The method was evaluated using sets of closely related (16S rRNA gene sequences of Nocardia species) and highly variable (VP1 genomic region of Enterovirus 71) sequences and outperformed existing unsupervised machine learning clustering methods and dimensionality reduction methods. This method does not require prior knowledge of the number of clusters or the distance between clusters, handles clusters of different sizes and shapes, and scales linearly with the dataset.

Conclusions: The combination of MSA with the linear mapping hash function is a computationally efficient way of gene sequence clustering and can be a valuable tool for the assessment of similarity, clustering of different microbial genomes, identifying reference sequences, and for the study of evolution of bacteria and viruses.

No MeSH data available.


Related in: MedlinePlus

Largest four Nocardia species clusters. Positions of cluster centroids are highlighted in square blocks, on PCA 1 and 2 as x-axis and y-axis coordinates, and linear mapping clustering results.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3351711&req=5

Figure 3: Largest four Nocardia species clusters. Positions of cluster centroids are highlighted in square blocks, on PCA 1 and 2 as x-axis and y-axis coordinates, and linear mapping clustering results.

Mentions: The combination of MSA with the linear mapping hash function identified the centroid for each cluster, which was then used as a reference. Centroids were the data elements or points positioned in the middle of the cluster cloud, i.e. these were the points with the minimum total distance between them and other points in the same cluster. For a high dimensional dataset, other methods (such as k-means) identify different centroids for each parameter taken as the main parameter, and it is up to the user to decide which parameter should be taken as the basis for the centroid definition. Figure 3 illustrates the PCA plot for the 97 Nocardia sequences of four species (N. cyriacigeorgica, N. farcinica, N. abscessus, and N. nova). The labels for cluster centroids are shown close to the centroid point highlighted in a square block. Since the clustering and the centroid identification are performed using the linear mapping method, and the PCA plot values are calculated using the PCA scores for the different coordinates (PC1 and PC2 in our case), the centroids do not necessarily fall in a geometric central point in the clusters' two dimensional space.


Linear normalised hash function for clustering gene sequences and identifying reference sequences from multiple sequence alignments.

Helal M, Kong F, Chen SC, Zhou F, Dwyer DE, Potter J, Sintchenko V - Microb Inform Exp (2012)

Largest four Nocardia species clusters. Positions of cluster centroids are highlighted in square blocks, on PCA 1 and 2 as x-axis and y-axis coordinates, and linear mapping clustering results.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3351711&req=5

Figure 3: Largest four Nocardia species clusters. Positions of cluster centroids are highlighted in square blocks, on PCA 1 and 2 as x-axis and y-axis coordinates, and linear mapping clustering results.
Mentions: The combination of MSA with the linear mapping hash function identified the centroid for each cluster, which was then used as a reference. Centroids were the data elements or points positioned in the middle of the cluster cloud, i.e. these were the points with the minimum total distance between them and other points in the same cluster. For a high dimensional dataset, other methods (such as k-means) identify different centroids for each parameter taken as the main parameter, and it is up to the user to decide which parameter should be taken as the basis for the centroid definition. Figure 3 illustrates the PCA plot for the 97 Nocardia sequences of four species (N. cyriacigeorgica, N. farcinica, N. abscessus, and N. nova). The labels for cluster centroids are shown close to the centroid point highlighted in a square block. Since the clustering and the centroid identification are performed using the linear mapping method, and the PCA plot values are calculated using the PCA scores for the different coordinates (PC1 and PC2 in our case), the centroids do not necessarily fall in a geometric central point in the clusters' two dimensional space.

Bottom Line: However, defining the optimal number of clusters, cluster density and boundaries for sets of potentially related sequences of genes with variable degrees of polymorphism remains a significant challenge.The method was evaluated using sets of closely related (16S rRNA gene sequences of Nocardia species) and highly variable (VP1 genomic region of Enterovirus 71) sequences and outperformed existing unsupervised machine learning clustering methods and dimensionality reduction methods.This method does not require prior knowledge of the number of clusters or the distance between clusters, handles clusters of different sizes and shapes, and scales linearly with the dataset.

View Article: PubMed Central - HTML - PubMed

Affiliation: Sydney Emerging Infections and Biosecurity Institute, Sydney Medical School - Westmead, University of Sydney, Sydney, New South Wales, Australia. vitali.sintchenko@swahs.health.nsw.gov.au.

ABSTRACT

Background: Comparative genomics has put additional demands on the assessment of similarity between sequences and their clustering as means for classification. However, defining the optimal number of clusters, cluster density and boundaries for sets of potentially related sequences of genes with variable degrees of polymorphism remains a significant challenge. The aim of this study was to develop a method that would identify the cluster centroids and the optimal number of clusters for a given sensitivity level and could work equally well for the different sequence datasets.

Results: A novel method that combines the linear mapping hash function and multiple sequence alignment (MSA) was developed. This method takes advantage of the already sorted by similarity sequences from the MSA output, and identifies the optimal number of clusters, clusters cut-offs, and clusters centroids that can represent reference gene vouchers for the different species. The linear mapping hash function can map an already ordered by similarity distance matrix to indices to reveal gaps in the values around which the optimal cut-offs of the different clusters can be identified. The method was evaluated using sets of closely related (16S rRNA gene sequences of Nocardia species) and highly variable (VP1 genomic region of Enterovirus 71) sequences and outperformed existing unsupervised machine learning clustering methods and dimensionality reduction methods. This method does not require prior knowledge of the number of clusters or the distance between clusters, handles clusters of different sizes and shapes, and scales linearly with the dataset.

Conclusions: The combination of MSA with the linear mapping hash function is a computationally efficient way of gene sequence clustering and can be a valuable tool for the assessment of similarity, clustering of different microbial genomes, identifying reference sequences, and for the study of evolution of bacteria and viruses.

No MeSH data available.


Related in: MedlinePlus