AncestralClust: clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees

Motivation: Clustering is a fundamental task in the analysis of nucleotide sequences. Despite the exponential increase in the size of sequence databases of homologous genes, few methods exist to cluster divergent sequences. Traditional clustering methods have mostly focused on optimizing high speed clustering of highly similar sequences. We develop a phylogenetic clustering method which infers ancestral sequences for a set of initial clusters and then uses a greedy algorithm to cluster sequences. Results: We describe a clustering program AncestralClust, which is developed for clustering divergent sequences. We compare this method with other state-of-the-art clustering methods using datasets of homologous sequences from different species. We show that, in divergent datasets, AncestralClust has higher accuracy and more even cluster sizes than current popular methods.

Automated summary

The study developed a phylogenetic clustering method called AncestralClust for clustering divergent sequences. Comparison with other state-of-the-art clustering methods showed that AncestralClust has higher accuracy and more even cluster sizes in divergent datasets.