AOminer

AOminer predicts anti-CRISPR (Acr) operons in prokaryotic genomes by leveraging genomic context and a machine-learning two-state Hidden Markov Model (HMM) to identify Acr genes and their operonic arrangements.

Key Features:

• Genomic Context Exploitation: Incorporates genomic context surrounding known acr genes or their homologs, including co-occurrence with other acr genes and phage structural genes within the same operon.
• Machine Learning Approach: Utilizes a trained two-state Hidden Markov Model (HMM) to capture conserved genomic contexts and distinguish anti-CRISPR operons (AOs) from non-AOs.
• High Accuracy: Demonstrated superior performance in comparative evaluations, achieving an accuracy rate of 0.85.
• Automated Mining Capability: Performs automated mining of potential Acr operons from query genomes or operon datasets.

Scientific Applications:

• CRISPR-Cas System Research: Facilitates discovery of new Acr genes and operonic arrangements to study the CRISPR-Cas immune system and how (pro-)viruses evade bacterial defenses.
• Genome Editing Tool Development: Identification of novel Acr proteins supports the development of more controllable genome editing tools for genetic engineering and biotechnology.

Methodology:

AOminer employs a machine learning framework centered on a trained two-state HMM that captures conserved genomic features of Acr operons and leverages genomic context around known acr genes or homologs to predict AOs.