Visibiome

Visibiome performs phylogeny-based similarity searches and comparative analyses of microbiome samples using 16S rRNA profiles to contextualize microbial community composition.

Key Features:

• Phylogeny-Based Similarity Search: Performs exhaustive phylogeny-based comparisons of query samples against an extensive database of 16S rRNA profiles from diverse environments.
• Scalable Distributed System: Implements a distributed architecture including web framework components, task queueing and scheduling, cloud computing resources, and a dedicated database server to support high computational demand.
• Efficient Search Algorithms: Uses Nearest Neighbor search algorithms for sublinear searches in high-dimensional metric spaces and an optimized Earth Mover Distance (EMD) implementation of weighted UniFrac for similarity assessment.
• Adaptive Rarefaction and 16S rRNA Copy Number Correction: Supports pairwise adaptive rarefaction and optional correction for 16S rRNA copy number variation to normalize sequencing-depth and gene-copy biases.
• Visualization Outputs: Produces barchart-based compositional comparisons and rankings of closest matches within the database for result interpretation.

Scientific Applications:

• Microbial ecology and environmental microbiology: Enables comparative analyses of community composition across environmental and ecological studies using 16S rRNA profiles.
• Pattern discovery and factor inference: Aids in uncovering patterns and potential driving factors influencing microbial community assembly and composition.
• Contextualization and hypothesis generation: Contextualizes novel samples within existing datasets to support hypothesis generation and exploration of ecological dynamics.

Methodology:

Methods explicitly include phylogeny-based similarity searches against a database of 16S rRNA profiles, Nearest Neighbor search algorithms for sublinear high-dimensional searches, an EMD-based implementation of weighted UniFrac, pairwise adaptive rarefaction, optional 16S rRNA copy number correction, and deployment on a distributed system with task queueing, scheduling, cloud resources, and a dedicated database server.