Mibianto: ultra-efficient online microbiome analysis through k-mer based metagenomics

Area of Science:

• Bioinformatics
• Computational Biology
• Microbiome Research

Background:

• Metagenomic analysis of microbiome species and composition is computationally complex and time-consuming.
• Existing k-mer based approaches expedite large sequencing data analysis and are used for metagenomic annotation.

Purpose of the Study:

• To develop an efficient and accurate tool for compositional analysis of microbiota from whole metagenome sequencing.
• To address challenges in speed, computational complexity, and data handling in metagenomic analysis.

Main Methods:

• Utilizes k-mer counting techniques for compositional analysis of microbiota.
• Employs a sketch-based workflow operating directly on read files, reducing data volume.
• Supports diverse sequencing platforms including short and long reads.

Main Results:

• Achieves significant data volume reduction (up to 99.59%) for enhanced efficiency and privacy.
• Provides accurate taxonomic profiling and advanced community composition estimation (diversity, ordination, differential abundance).
• Facilitates standardization of computational workflows for improved reproducibility.

Conclusions:

• Mibianto offers an efficient, accurate, and user-friendly solution for metagenomic data analysis.
• The tool supports diverse experimental designs and enhances reproducibility in microbiome studies.
• Mibianto is freely available, making advanced microbiome analysis accessible for research and clinical applications.