MtHc: a motif-based hierarchical method for clustering massive 16S rRNA sequences into OTUs

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• High-throughput sequencing has generated vast amounts of microbial 16S rRNA data.
• Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is essential for metagenomic analysis.
• Existing OTU inference methods face challenges in balancing accuracy and computational efficiency.

Purpose of the Study:

• To develop a novel motif-based hierarchical method (MtHc) for clustering massive 16S rRNA sequences.
• To achieve high clustering accuracy with low memory usage for large-scale metagenomic datasets.
• To provide an efficient and robust solution for OTU inference.

Main Methods:

• Constructing a weighted network where 16S rRNA sequences are nodes.
• Heuristically searching for motifs (n-node sub-graphs with distances below a threshold).
• Seeding candidate clusters using motifs and hierarchically merging them based on motif distances.

Main Results:

• MtHc demonstrates higher clustering performance compared to existing methods.
• The method exhibits lower memory usage and robustness to parameter settings.
• MtHc proves effective for handling large-scale metagenomic datasets.

Conclusions:

• MtHc offers an effective solution for accurate and efficient OTU clustering of massive 16S rRNA data.
• The method's low memory footprint and robustness make it suitable for big data analysis.
• The MtHC software is available for academic users to facilitate microbial community analysis.