  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. 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. 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.
