ESPRIT-Forest

ESPRIT-Forest performs parallel hierarchical clustering of next-generation sequencing datasets to enable scalable analysis of microbial 16S rRNA and metagenomic sequence relationships.

Key Features:

• Parallel hierarchical clustering: Implements hierarchical clustering with a multiple-pair merging criterion to construct clusters using multiple threads.
• Pseudo-metric partitioning tree: Organizes sequences with a pseudo-metric based partitioning tree that enables sub-linear time nearest-neighbor searches.
• Subquadratic complexity: Achieves subquadratic time and space complexity for large-scale sequence datasets.
• Scalability: Scales to analyze tens of millions of sequences from next-generation sequencing datasets.
• Accuracy: Maintains high clustering accuracy comparable to standard hierarchical clustering methods.
• Empirical demonstration: Applied to the Human Microbiome Project (HMP) microbial 16S rRNA dataset.

Scientific Applications:

• Microbiomics: Enables taxonomic profiling and community structure analysis from large 16S rRNA sequencing datasets.
• Metagenomics: Supports hierarchical clustering of metagenomic sequences for comparative community analysis.
• Taxonomic and functional inference: Facilitates inference of taxonomic relationships and supports downstream functional annotation workflows in complex biological communities.

Methodology:

Uses a pseudo-metric based partitioning tree for sub-linear nearest-neighbor search combined with a multiple-pair merging criterion to perform parallel, multi-threaded hierarchical clustering and achieve subquadratic time and space complexity.