crop

crop clusters 16S rRNA gene sequences into operational taxonomic units (OTUs) using the unsupervised Bayesian Clustering 16S rRNA for OTU Prediction (CROP) method to enable accurate microbial diversity profiling.

Key Features:

• Unsupervised Bayesian clustering (CROP): Applies an unsupervised Bayesian framework (Clustering 16S rRNA for OTU Prediction) to group sequences into OTUs based on inherent data structure.
• Avoids arbitrary similarity thresholds: Eliminates the need for fixed cut-off thresholds such as 3% or 5% sequence divergence when defining OTUs.
• Robustness to sequencing errors: Demonstrates resilience to sequencing errors and provides more reliable clustering results compared to conventional hierarchical clustering methods.
• Implementation: Implemented in C++ and supported on Linux and MS Windows platforms.

Scientific Applications:

• Microbial ecology: Clusters 16S rRNA gene sequences into OTUs to profile microbial diversity within environmental samples.
• Community composition and relative abundance estimation: Facilitates determination of OTUs and their relative abundances to study microbial community structure and dynamics.

Methodology:

Uses an unsupervised Bayesian clustering algorithm (CROP) that clusters sequences based on inherent structure and avoids hard similarity cut-offs (e.g., 3%/5%); implemented in C++.