MacSyFinder

MacSyFinder models and detects macromolecular systems in genomic datasets to identify, classify, and analyze conserved molecular components, with a focus on prokaryotic organisms.

Key Features:

• Flexible system modeling: User-defined models represent components, evolutionary associations, genetic architecture, functional analogs, and component multifunctionality within systems.
• Sequence similarity search with HMM profiles: Uses Hidden Markov Model (HMM) protein profiles to search for system components across complete genomes and unstructured datasets such as metagenomes.
• Compliance-based system assignment: Assigns sequence hits to specific molecular systems based on adherence to the predefined content and organization of the system model.
• CRISPR-Cas system modeling: Enables definition and detection of various classes of CRISPR-Cas systems using publicly available protein profiles.

Scientific Applications:

• Macromolecular system discovery: Identification and classification of conserved cellular machinery and pathway components across genomes.
• Evolutionary analysis: Exploration of homologs and evolutionary relationships of system components across prokaryotic genomes.
• Genomics and metagenomics screening: Detection of macromolecular systems in both assembled genomes and unstructured metagenomic datasets.

Methodology:

Implements user-defined system models and performs sequence similarity searches using Hidden Markov Model protein profiles via Hmmer and makeblastdb (version 2.2.28 or higher), assigns hits to systems based on model compliance, and is implemented in Python 2.7.