miComplete: weighted quality evaluation of assembled microbial genomes

Area of Science:

• Microbial genomics
• Bioinformatics
• Computational biology

Background:

• Metagenomics and single-cell genomics have greatly expanded the understanding of microbial genomic diversity.
• Estimating the completeness and redundancy of metagenome-assembled genomes (MAGs) and single-cell amplified genomes (SAGs) is crucial but challenging.
• Current methods often rely on counting conserved gene markers, which can be prone to biases.

Purpose of the Study:

• To introduce miComplete, a novel computational tool for accurately assessing genome completeness and redundancy.
• To address the limitations of existing marker-based approaches for MAGs and SAGs.

Main Methods:

• Developed miComplete, a Python 3 tool implementing a new weighting strategy.
• The strategy normalizes marker presence/absence by their median distance to the next marker in reference genomes.
• This method mitigates biases from DNA fragments with high marker density, such as ribosomal protein operons.

Main Results:

• miComplete offers a more robust estimation of genome completeness and redundancy.
• The weighting strategy effectively alleviates biases inherent in simpler marker-counting methods.
• Improved accuracy in assessing MAG and SAG quality.

Conclusions:

• miComplete provides a significant advancement in evaluating the quality of microbial genomes reconstructed from complex samples.
• The tool enhances the reliability of genomic diversity studies utilizing metagenomics and single-cell genomics.
• Accurate genome assessment is vital for downstream analyses in microbial ecology and evolution.