MetaCerberus: distributed highly parallelized HMM-based processing for robust functional annotation across the tree...

Area of Science:

• Genomics and Bioinformatics
• Metagenomics
• Computational Biology

Background:

• Accurate gene function inference is crucial for understanding biological systems.
• Existing annotation tools often face limitations in speed, memory usage, and scalability, particularly for large-scale metagenomic datasets.
• There is a need for efficient and accurate tools to annotate gene functions across diverse life forms, from single genomes to complex communities.

Purpose of the Study:

• To introduce MetaCerberus, a novel, massively parallel, and scalable tool for gene function annotation.
• To provide a fast and memory-efficient HMMER-based annotation solution.
• To enable comprehensive gene function elucidation across various public databases and for diverse biological contexts, including viruses and metacommunities.

Main Methods:

• MetaCerberus utilizes an HMM/HMMER-based approach for gene function inference.
• The tool is designed for massive parallelism, ensuring high speed and low memory consumption.
• It integrates with major public databases such as KEGG (KO), COGs, CAZy, FOAM, VOGs, and PHROGs.

Main Results:

• MetaCerberus demonstrates 1.3x higher speed and uses 5x less memory than eggNOG-mapper v2 in HMMER mode.
• It provides superior annotation of viruses, phages, and archaeal viruses compared to DRAM, Prokka, and InterProScan.
• The tool annotates more KOs across domains than DRAM, utilizing a significantly smaller database and less memory.

Conclusions:

• MetaCerberus offers a scalable, efficient, and accurate solution for gene function annotation across genomes and metacommunities.
• Its performance advantages and comprehensive database integration make it a valuable tool for biological research.
• The tool facilitates the exploration of the biosphere's genetic potential across the tree of life.