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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

Heatmaps for the Distance matrix generated by the MSA of the different datasets (a) Nocardia 16S rRNA gene, 364 sequences of 80 known species; (b) Nocardia 16S rRNA gene, 97 sequences of 4 known species; (c) EV71, 109 VP1 sequences of 11 known genogroups/subgenogroups, and (d) EV71, 500 VP1 sequences of unknown genogroups/subgenogroups.
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Figure 1: Heatmaps for the Distance matrix generated by the MSA of the different datasets (a) Nocardia 16S rRNA gene, 364 sequences of 80 known species; (b) Nocardia 16S rRNA gene, 97 sequences of 4 known species; (c) EV71, 109 VP1 sequences of 11 known genogroups/subgenogroups, and (d) EV71, 500 VP1 sequences of unknown genogroups/subgenogroups.

Mentions: Using the Multiple Sequence Alignment (MSA) output in the aligned order (rather than the input order), the sequences are sorted based on the tree building algorithm used, making the closer family of sequences in order before starting another family branch. The MSA is then used to generate a pair-wise distance matrix between the sequences. The produced sorting order made the main diagonal in the distance matrix to be the distance between a sequence and itself (Figure 1). The second diagonal represented the distance between a sequence and its closest other sequence; the third diagonal represented the distance between the sequence and its second closest sequence and so forth. The heat maps in Figure 1 illustrate the rectangular shapes of the darkest blue shades along the diagonal as the boundaries around which the natural selection of a cluster should be identified. The process of identifying these boundaries is based on the linear mapping of the second diagonal values to a normalized index value. The linear mapping to index values employs a deterministic hash function. The hash function used is uniform in the context of the input distance matrix. Thus, a very similar dataset should produce similar or very close hash codes, rather than a highly variable dataset.


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)

Heatmaps for the Distance matrix generated by the MSA of the different datasets (a) Nocardia 16S rRNA gene, 364 sequences of 80 known species; (b) Nocardia 16S rRNA gene, 97 sequences of 4 known species; (c) EV71, 109 VP1 sequences of 11 known genogroups/subgenogroups, and (d) EV71, 500 VP1 sequences of unknown genogroups/subgenogroups.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Heatmaps for the Distance matrix generated by the MSA of the different datasets (a) Nocardia 16S rRNA gene, 364 sequences of 80 known species; (b) Nocardia 16S rRNA gene, 97 sequences of 4 known species; (c) EV71, 109 VP1 sequences of 11 known genogroups/subgenogroups, and (d) EV71, 500 VP1 sequences of unknown genogroups/subgenogroups.
Mentions: Using the Multiple Sequence Alignment (MSA) output in the aligned order (rather than the input order), the sequences are sorted based on the tree building algorithm used, making the closer family of sequences in order before starting another family branch. The MSA is then used to generate a pair-wise distance matrix between the sequences. The produced sorting order made the main diagonal in the distance matrix to be the distance between a sequence and itself (Figure 1). The second diagonal represented the distance between a sequence and its closest other sequence; the third diagonal represented the distance between the sequence and its second closest sequence and so forth. The heat maps in Figure 1 illustrate the rectangular shapes of the darkest blue shades along the diagonal as the boundaries around which the natural selection of a cluster should be identified. The process of identifying these boundaries is based on the linear mapping of the second diagonal values to a normalized index value. The linear mapping to index values employs a deterministic hash function. The hash function used is uniform in the context of the input distance matrix. Thus, a very similar dataset should produce similar or very close hash codes, rather than a highly variable dataset.

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