Opfi

Opfi identifies candidate gene clusters in assembled genomic and metagenomic sequences to enable discovery and analysis of co-localized genes that collectively perform specific biological functions.

Key Features:

• Modular pipeline: Extracts candidate gene clusters from assembled sequences using a configurable modular workflow.
• Annotation: Provides annotation capabilities to label and interpret the biological significance of identified gene clusters.
• De-duplication: Performs de-duplication to remove redundant gene cluster records from results.
• Rule-based filtering: Applies customizable rule-based filters to select candidate gene systems according to user-defined criteria.
• Visualization: Generates visual representations of putative gene clusters to aid interpretation.
• Implementation: Implemented in Python.

Scientific Applications:

• Genomics and metagenomics research: Identification and characterization of gene clusters within large genomic and metagenomic datasets.
• Biotechnology: Discovery of co-localized genes that collectively encode biochemical pathways or functions relevant to biotechnological applications.
• Targeted hypothesis testing: Selection of candidate gene systems tailored to specific research criteria via rule-based filters.

Methodology:

Operates as a modular Python pipeline that extracts candidate gene clusters from assembled sequences and performs annotation, de-duplication, customizable rule-based filtering, and visualization.